From 2f9d20a9231da6fa199dae6c80c65fd52c9c094f Mon Sep 17 00:00:00 2001 From: Deepak Date: Thu, 1 Feb 2024 16:19:34 +0530 Subject: [PATCH] updated docs --- docs/ai/computer-vision-cv/intro.md | 2 +- docs/ai/others-resources-interview.md | 15 +- ...advanced-stastistical-methods-in-python.md | 203 ------------ .../courses/365-ds-mathematics.md | 27 -- docs/computer-science/courses/readme.md | 15 - docs/computer-science/readme.md | 1 - .../courses/365-data-science-program.md | 20 +- ...advanced-stastistical-methods-in-python.md | 119 +++++++ docs/courses/365-ds-mathematics.md | 17 + docs/courses/chatgpt-prompt-eng.md | 6 +- .../decision-areas-and-credit-scorecards.md | 8 +- .../course-credit-risk-modeling/intro.md | 24 +- .../course-intro-to-data-and-data-science.md | 4 +- .../intro-time-series.md | 28 +- .../time-series-modeling.md | 56 ++-- .../courses/coursera-algorithms-part-1.md | 8 +- .../courses/coursera-algorithms-part-2.md | 4 + .../courses/coursera-how-google-does-ml.md | 14 +- .../customer-analytics-in-python/intro.md | 26 +- .../data-integration-specialist-aws.md | 0 .../google-professional-data-engineer-pde.md | 8 +- .../courses/microsoft-excel-google-sheets.md | 4 +- .../courses/mordern-algorithm-design.md | 0 .../courses/nutanix-hybrid-cloud.md | 12 +- docs/courses/readme.md | 312 +++++++----------- .../courses/se-radio.md | 0 .../courses/self-driving-nanodegree.md | 0 ...y-python-for-data-structures-algorithms.md | 7 - .../intro-to-probability/intro-syllabus.md | 3 +- .../data-center-networking.md | 2 +- docs/psychology/course-mindshift.md | 2 + docs/readme.md | 34 +- 32 files changed, 401 insertions(+), 580 deletions(-) delete mode 100755 docs/computer-science/courses/365-ds-advanced-stastistical-methods-in-python.md delete mode 100755 docs/computer-science/courses/365-ds-mathematics.md delete mode 100755 docs/computer-science/courses/readme.md rename docs/{computer-science => }/courses/365-data-science-program.md (53%) create mode 100755 docs/courses/365-ds-advanced-stastistical-methods-in-python.md create mode 100755 docs/courses/365-ds-mathematics.md rename docs/{computer-science => }/courses/coursera-algorithms-part-1.md (98%) rename docs/{computer-science => }/courses/coursera-algorithms-part-2.md (97%) rename docs/{computer-science => }/courses/coursera-how-google-does-ml.md (78%) rename docs/{computer-science => }/courses/data-integration-specialist-aws.md (100%) rename docs/{computer-science => }/courses/microsoft-excel-google-sheets.md (98%) rename docs/{computer-science => }/courses/mordern-algorithm-design.md (100%) rename docs/{computer-science => }/courses/nutanix-hybrid-cloud.md (96%) rename docs/{computer-science => }/courses/se-radio.md (100%) rename docs/{computer-science => }/courses/self-driving-nanodegree.md (100%) rename docs/{computer-science => }/courses/udemy-python-for-data-structures-algorithms.md (97%) diff --git a/docs/ai/computer-vision-cv/intro.md b/docs/ai/computer-vision-cv/intro.md index 7dc9f42549e..12a1579eaa8 100755 --- a/docs/ai/computer-vision-cv/intro.md +++ b/docs/ai/computer-vision-cv/intro.md @@ -97,7 +97,7 @@ https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio ## References -[Self Driving Nanodegree](../../computer-science/courses/self-driving-nanodegree) +[Self Driving Nanodegree](courses/self-driving-nanodegree.md) https://towardsdatascience.com/understanding-ssd-multibox-real-time-object-detection-in-deep-learning-495ef744fab diff --git a/docs/ai/others-resources-interview.md b/docs/ai/others-resources-interview.md index 5c9ff0bee4c..44f578d7198 100755 --- a/docs/ai/others-resources-interview.md +++ b/docs/ai/others-resources-interview.md @@ -108,8 +108,8 @@ - Readings in applied data science - https://github.com/hadley/stats337 - Fundamentals of data vizualization - https://serialmentor.com/dataviz - Spark - https://cognitiveclass.ai/learn/spark - - Spark, storm, flink - - Athena / Presto / Hive + - Spark, storm, flink + - Athena / Presto / Hive - HDFS Architecture - https://hadoop.apache.org/docs/r3.0.0/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html - Computer Vision - https://in.udacity.com/course/introduction-to-computer-vision--ud810 - NPTEL - Data Mining - https://onlinecourses.nptel.ac.in/noc19_cs15 @@ -200,11 +200,12 @@ - https://github.com/FavioVazquez/ds-cheatsheets - https://github.com/jonathan-bower/DataScienceResources - https://towardsdatascience.com/5-professional-projects-every-data-scientist-should-know-e89bf4e7e8e1 - 1. Customer Segmentation - 2. Text Classification - 3. Sentiment Analysis - 4. Time Series Forecasting - 5. Recommendation Systems + + 1. Customer Segmentation + 2. Text Classification + 3. Sentiment Analysis + 4. Time Series Forecasting + 5. Recommendation Systems ## Courses diff --git a/docs/computer-science/courses/365-ds-advanced-stastistical-methods-in-python.md b/docs/computer-science/courses/365-ds-advanced-stastistical-methods-in-python.md deleted file mode 100755 index c2717085a86..00000000000 --- a/docs/computer-science/courses/365-ds-advanced-stastistical-methods-in-python.md +++ /dev/null @@ -1,203 +0,0 @@ -# 365 DS - Advanced Stastistical Methods in Python - -## Linear regression - -Welcome to Advanced Statistics! (0:28) - -Welcome to the Course - -Introduction to Regression Analysis (1:27) - -The Linear Regression Model (5:50) - -Correlation vs Regression (1:43) - -Geometrical Representation of the Linear Regression Model (1:25) - -Python Packages Installation (4:39) - -First Regression in Python (7:11) - -First Regression in Python Exercise - -Using Seaborn for Graphs (1:21) - -How to Interpret the Regression Table (5:47) - -Decomposition of Variability (3:37) - -What is the OLS? (3:13) - -R-Squared (5:30) - -## Multiple Linear Regression - -Multiple Linear Regression (2:55) - -Adjusted R-Squared (6:00) - -Multiple Linear Regression Exercise - -Test for Significance of the Model (F-Test) (2:01) - -OLS Assumptions (2:21) - -A1: Linearity (1:50) - -A2: No Endogeneity (4:09) - -A3: Normality and Homoscedasticity (5:47) - -A4: No Autocorrelation (3:31) - -A5: No Multicollinearity (3:26) - -Dealing with Categorical Data - Dummy Variables (6:43) - -Dealing with Categorical Data - Dummy Variables Exercise - -Making Predictions with the Linear Regression (3:29) - -## Linear Regression with sklearn - -What is sklearn? (2:14) - -Game Plan for sklearn (1:55) - -Simple Linear Regression with sklearn (5:38) - -Simple Linear Regression with sklearn - Summary Table (4:48) - -A Note on Normalization - -Multiple Linear Regression with sklearn (3:10) - -Adjusted R-Squared (4:45) - -Adjusted R-Squared Exercise - -Feature Selection through p-values (F-regression) (4:41) - -A Note on Calculation of P-Values with sklearn - -Creating a Summary Table with the p-values (2:10) - -Multiple Linear Regression - Exercise - -Feature Scaling (5:38) - -Feature Selection through Standardization (5:22) - -Making Predictions with Standardized Coefficients (3:52) - -Feature Scaling - Exercise - -Underfitting and Overfitting (2:42) - -Training and Testing (6:54) - -## Linear Regression - Practical Example - -Practical Example (Part 1) (11:59) - -Practical Example (Part 2) (6:12) - -A Note on Multicollinearity - -Practical Example (Part 3) (3:15) - -Dummies and VIF - Exercise - -Practical Example (Part 4) (8:09) - -Dummy Variables Interpretation - Exercise - -Practical Example (Part 5) (7:34) - -Linear Regression - Exercise - -## Logistic Regression - -Introduction to Logistic Regression (1:19) - -A Simple Example in Python (4:42) - -Logistic vs Logit Function (4:00) - -Building a Logistic Regression (2:48) - -Bulding a Logistic Regression Exercise - -An Invaluable Coding Tip (2:26) - -Understanding Logistic Regression Tables (4:06) - -Understanding Logistic Regression Tables - Exercise - -What do the Odds Actually Mean (4:30) - -Binary Predictors in a Logistic Regression (4:32) - -Binary Predictors in a Logistic Regression - Exercise - -Calculating the Accuracy of the Model (3:21) - -Calculating the Accuracy of the Model - Exercise - -Underfitting and Overfitting (3:43) - -Testing the Model (5:05) - -Testing the Model - Exercise - -## Cluster Analysis (Basics and Prerequisites) - -Introduction to Cluster Analysis (3:41) - -Some Examples of Clusters (4:31) - -Difference between Classification and Clustering (2:32) - -Math Prerequisites (3:19) - -## K-Means Clustering - -K-Means Clustering (4:41) - -A Simple Example of Clustering (7:48) - -A Simple Example of Clustering - Exercise - -Clustering Categorical Data (2:50) - -Clustering Categorical Data - Exercise - -How to Choose the Number of Clusters (6:11) - -How to Choose the Number of Clusters - Exercise - -Pros and Cons of K-Means Clustering (3:23) - -To Standardize or to not Standardize (4:32) - -Relationship between Clustering and Regression (1:31) - -Market Segmentation with Cluster Analysis (Part 1) (6:03) - -Market Segmentation with Cluster Analysis (Part 2) (6:58) - -How is Clustering Useful? (4:47) - -Exercise - Species Segmentation with Cluster Analysis (Part 1) - -Exercise - Species Segmentation with Cluster Analysis (Part 2) - -## Other Types of Clustering - -Types of Clustering (3:39) - -Dendrogram (5:21) - -Heatmaps (4:34) - -https://365datascience.teachable.com/courses/enrolled/362812 diff --git a/docs/computer-science/courses/365-ds-mathematics.md b/docs/computer-science/courses/365-ds-mathematics.md deleted file mode 100755 index 09124eecd2a..00000000000 --- a/docs/computer-science/courses/365-ds-mathematics.md +++ /dev/null @@ -1,27 +0,0 @@ -# 365 DS - Mathematics - -## Introduction to Linear Algebra - -What is a Matrix - -Scalars and Vectors - -Linear Algebra and Geometry - -Scalars, Vectors, and Matrices as Python Arrays - -What is a Tensor? - -Addition and Subtraction - -Errors when Adding Matrices - -Transpose of a Matrix - -Dot Product - -Dot Product of Matrices - -Why is Linear Algebra Useful - -https://365datascience.teachable.com/courses/enrolled/372258 diff --git a/docs/computer-science/courses/readme.md b/docs/computer-science/courses/readme.md deleted file mode 100755 index 3cb1bd3793c..00000000000 --- a/docs/computer-science/courses/readme.md +++ /dev/null @@ -1,15 +0,0 @@ -# Courses - -- [Coursera - Algorithms Part - 1](coursera-algorithms-part-1) -- [Coursera - Algorithms Part - 2](coursera-algorithms-part-2) -- [Mordern Algorithm Design](mordern-algorithm-design) -- [SE Radio](se-radio) -- [Coursera - How Google does ML](coursera-how-google-does-ml) -- [Data Integration Specialist - AWS](data-integration-specialist-aws) -- [Udemy - Python for Data Structure](udemy-python-for-data-structures-algorithms) -- [365 Data Science Program](365-data-science-program) -- [Intro to Microsoft Excel / Google Sheets](computer-science/courses/microsoft-excel-google-sheets.md) -- [365 DS - Advanced Statistical Methods in Python](365-ds-advanced-stastistical-methods-in-python) -- [365 DS - Mathematics](365-ds-mathematics) -- [Nutanix Hybrid Cloud](nutanix-hybrid-cloud) -- [Self Driving Nanodegree](self-driving-nanodegree) diff --git a/docs/computer-science/readme.md b/docs/computer-science/readme.md index 2694ab942e9..bbbb804bd4e 100755 --- a/docs/computer-science/readme.md +++ b/docs/computer-science/readme.md @@ -11,7 +11,6 @@ - [Security](security/readme.md) - [Interview Question](interview-question/readme.md) - [IoT](iot/readme.md) -- [Courses](computer-science/courses/readme.md) - [Others](others/readme.md) - [Links](computer-science/links.md) diff --git a/docs/computer-science/courses/365-data-science-program.md b/docs/courses/365-data-science-program.md similarity index 53% rename from docs/computer-science/courses/365-data-science-program.md rename to docs/courses/365-data-science-program.md index 3f31ac00fea..41c3f162fee 100755 --- a/docs/computer-science/courses/365-data-science-program.md +++ b/docs/courses/365-data-science-program.md @@ -6,28 +6,16 @@ 4. Probability 5. Statistics 6. Mathematics -7. PowerBI - - https://365datascience.teachable.com/courses/enrolled/716963 - +7. PowerBI - https://365datascience.teachable.com/courses/enrolled/716963 8. SQL -9. SQL + Tableau - - https://365datascience.teachable.com/courses/enrolled/361448 - +9. SQL + Tableau - https://365datascience.teachable.com/courses/enrolled/361448 10. Introduction to Python 11. The Python Programmer 12. Git and Github 13. Introduction to R Programming 14. Advanced Statistical Methods -15. Deep Learning with TensorFlow - - https://365datascience.teachable.com/courses/enrolled/284663 - -16. Deep Learning with TensorFlow 2.0 - - https://365datascience.teachable.com/courses/enrolled/614390 - +15. Deep Learning with TensorFlow - https://365datascience.teachable.com/courses/enrolled/284663 +16. Deep Learning with TensorFlow 2.0 - https://365datascience.teachable.com/courses/enrolled/614390 17. **Credit Risk Modeling in Python** 18. **Time Series Analysis in Python** 19. **Customer Analytics in Python** diff --git a/docs/courses/365-ds-advanced-stastistical-methods-in-python.md b/docs/courses/365-ds-advanced-stastistical-methods-in-python.md new file mode 100755 index 00000000000..e85e38dc82c --- /dev/null +++ b/docs/courses/365-ds-advanced-stastistical-methods-in-python.md @@ -0,0 +1,119 @@ +# 365 DS - Advanced Stastistical Methods in Python + +## Linear regression + +- Welcome to Advanced Statistics! +- Welcome to the Course +- Introduction to Regression Analysis +- The Linear Regression Model +- Correlation vs Regression +- Geometrical Representation of the Linear Regression Model +- Python Packages Installation +- First Regression in Python +- First Regression in Python Exercise +- Using Seaborn for Graphs +- How to Interpret the Regression Table +- Decomposition of Variability +- What is the OLS? +- R-Squared + +## Multiple Linear Regression + +- Multiple Linear Regression +- Adjusted R-Squared +- Multiple Linear Regression Exercise +- Test for Significance of the Model (F-Test) +- OLS Assumptions +- A1: Linearity +- A2: No Endogeneity +- A3: Normality and Homoscedasticity +- A4: No Autocorrelation +- A5: No Multicollinearity +- Dealing with Categorical Data - Dummy Variables +- Dealing with Categorical Data - Dummy Variables Exercise +- Making Predictions with the Linear Regression + +## Linear Regression with sklearn + +- What is sklearn? +- Game Plan for sklearn +- Simple Linear Regression with sklearn +- Simple Linear Regression with sklearn - Summary Table +- A Note on Normalization +- Multiple Linear Regression with sklearn +- Adjusted R-Squared +- Adjusted R-Squared Exercise +- Feature Selection through p-values (F-regression) +- A Note on Calculation of P-Values with sklearn +- Creating a Summary Table with the p-values +- Multiple Linear Regression - Exercise +- Feature Scaling +- Feature Selection through Standardization +- Making Predictions with Standardized Coefficients +- Feature Scaling - Exercise +- Underfitting and Overfitting +- Training and Testing + +## Linear Regression - Practical Example + +- Practical Example (Part 1) +- Practical Example (Part 2) +- A Note on Multicollinearity +- Practical Example (Part 3) +- Dummies and VIF - Exercise +- Practical Example (Part 4) +- Dummy Variables Interpretation - Exercise +- Practical Example (Part 5) +- Linear Regression - Exercise + +## Logistic Regression + +- Introduction to Logistic Regression +- A Simple Example in Python +- Logistic vs Logit Function +- Building a Logistic Regression +- Bulding a Logistic Regression Exercise +- An Invaluable Coding Tip +- Understanding Logistic Regression Tables +- Understanding Logistic Regression Tables - Exercise +- What do the Odds Actually Mean +- Binary Predictors in a Logistic Regression +- Binary Predictors in a Logistic Regression - Exercise +- Calculating the Accuracy of the Model +- Calculating the Accuracy of the Model - Exercise +- Underfitting and Overfitting +- Testing the Model +- Testing the Model - Exercise + +## Cluster Analysis (Basics and Prerequisites) + +- Introduction to Cluster Analysis +- Some Examples of Clusters +- Difference between Classification and Clustering +- Math Prerequisites + +## K-Means Clustering + +- K-Means Clustering +- A Simple Example of Clustering +- A Simple Example of Clustering - Exercise +- Clustering Categorical Data +- Clustering Categorical Data - Exercise +- How to Choose the Number of Clusters +- How to Choose the Number of Clusters - Exercise +- Pros and Cons of K-Means Clustering +- To Standardize or to not Standardize +- Relationship between Clustering and Regression +- Market Segmentation with Cluster Analysis (Part 1) +- Market Segmentation with Cluster Analysis (Part 2) +- How is Clustering Useful? +- Exercise - Species Segmentation with Cluster Analysis (Part 1) +- Exercise - Species Segmentation with Cluster Analysis (Part 2) + +## Other Types of Clustering + +1. Types of Clustering +2. Dendrogram +3. Heatmaps + +https://365datascience.teachable.com/courses/enrolled/362812 diff --git a/docs/courses/365-ds-mathematics.md b/docs/courses/365-ds-mathematics.md new file mode 100755 index 00000000000..2450512e859 --- /dev/null +++ b/docs/courses/365-ds-mathematics.md @@ -0,0 +1,17 @@ +# 365 DS - Mathematics + +## Introduction to Linear Algebra + +- What is a Matrix +- Scalars and Vectors +- Linear Algebra and Geometry +- Scalars, Vectors, and Matrices as Python Arrays +- What is a Tensor? +- Addition and Subtraction +- Errors when Adding Matrices +- Transpose of a Matrix +- Dot Product +- Dot Product of Matrices +- Why is Linear Algebra Useful + +https://365datascience.teachable.com/courses/enrolled/372258 diff --git a/docs/courses/chatgpt-prompt-eng.md b/docs/courses/chatgpt-prompt-eng.md index 328b9a1aa9a..7a71d6892e6 100644 --- a/docs/courses/chatgpt-prompt-eng.md +++ b/docs/courses/chatgpt-prompt-eng.md @@ -117,9 +117,9 @@ _Chain-of-thought_ (CoT) prompting is a technique that allows [large language ## Prompt Examples -![ChatGPT Ultimate Prompting Guide](../../media/chatgpt-promt-engineering.png) +![ChatGPT Ultimate Prompting Guide](../media/chatgpt-promt-engineering.png) -![ChatGPT Prompts Commands](../../media/chatgpt-prompts.png) +![ChatGPT Prompts Commands](../media/chatgpt-prompts.png) Share the most important leadership lessons and insights from the book `{insert book}` by `{insert author}`. For each insight suggest an actionable way I can embody it. @@ -142,7 +142,7 @@ At a high level, a typical integration of the Assistants API has the following f [Create AI Assistants with OpenAI's Assistants API](https://www.freecodecamp.org/news/create-ai-assistants-with-openais-assistants-api/) -Knowledge based retrieval tool - +Knowledge based retrieval tool - [platform.openai.com/docs/assistants/overview](https://platform.openai.com/docs/assistants/overview) diff --git a/docs/courses/course-credit-risk-modeling/decision-areas-and-credit-scorecards.md b/docs/courses/course-credit-risk-modeling/decision-areas-and-credit-scorecards.md index 98969012e6b..d3122045d32 100755 --- a/docs/courses/course-credit-risk-modeling/decision-areas-and-credit-scorecards.md +++ b/docs/courses/course-credit-risk-modeling/decision-areas-and-credit-scorecards.md @@ -51,15 +51,15 @@ A credit scorecard is a lookup table that maps specific characteristics of a bor For example, a credit scorecard can give individual borrowers points for their age and income according to the following table. Other characteristics such as residential status, employment status, might also be included, although, for brevity, they are not shown in this table. -![image](../../../media/Course-Credit-Risk-Modeling_Decision-Areas-&-Credit-Scorecards-image1.jpg) +![image](../../media/Course-Credit-Risk-Modeling_Decision-Areas-&-Credit-Scorecards-image1.jpg) Using the credit scorecard in this example, a particular customer who is 31 and has an income of $52,000 a year, is placed into the second age group (26--40) and receives 25 points for their age, and similarly, receives 28 points for their income. Other characteristics (not shown here) might contribute additional points to their score. The total score is the sum of all points, which in this example is assumed to give the customer a total of 238 points (this is a fictitious example on an arbitrary scoring scale). -![image](../../../media/Course-Credit-Risk-Modeling_Decision-Areas-&-Credit-Scorecards-image2.jpg) +![image](../../media/Course-Credit-Risk-Modeling_Decision-Areas-&-Credit-Scorecards-image2.jpg) Technically, to determine the credit scorecard points, start out by selecting a set of potential predictors (column 1 in the next figure). Then, bin data into groups (for example, ages 'Up to 25', '25 to 40' (column 2 in the figure). This grouping helps to distinguish between "good" and "bad" customers. The Weight of Evidence (WOE) is a way to measure how well the distribution of "good" and "bad" are separated across bins or groups for each individual predictor (column 3 in the figure). By fitting a logistic regression model, you can identify which predictors, when put together, do a better job distinguishing between "good" and "bad" customers. The model is summarized by its coefficients (column 4 in the figure). Finally, the combination of WOE's and model coefficients (commonly scaled, shifted, and rounded) make up the scorecard points (column 5 in the figure). -![image](../../../media/Course-Credit-Risk-Modeling_Decision-Areas-&-Credit-Scorecards-image3.jpg) +![image](../../media/Course-Credit-Risk-Modeling_Decision-Areas-&-Credit-Scorecards-image3.jpg) ## Credit Scorecard Development Process @@ -76,7 +76,7 @@ https://www.mathworks.com/help/finance/about-credit-scorecards.html ## Gains Chart -![image](../../../media/Course-Credit-Risk-Modeling_Decision-Areas-&-Credit-Scorecards-image4.jpg) +![image](../../media/Course-Credit-Risk-Modeling_Decision-Areas-&-Credit-Scorecards-image4.jpg) - For the score range 245--250, the expected **marginal bad rate** is 1.2%. That is, 1.2% of applicants with a score between 245 and 250 will likely be "bad." - The **cumulative bad rate** - that is, the bad rate of all applicants above 245 - is 0.84% diff --git a/docs/courses/course-credit-risk-modeling/intro.md b/docs/courses/course-credit-risk-modeling/intro.md index 2243c228772..080bd0907f8 100755 --- a/docs/courses/course-credit-risk-modeling/intro.md +++ b/docs/courses/course-credit-risk-modeling/intro.md @@ -56,7 +56,7 @@ The total value that a lender is exposed to when a borrower defaults - PD - Binomial Logistic regression - LGD/EAD - Beta regression -![image](../../../media/Course-Credit-Risk-Modeling_Intro-image1.jpg) +![image](../../media/Course-Credit-Risk-Modeling_Intro-image1.jpg) - Risk based pricing @@ -86,7 +86,7 @@ https://www.listendata.com/2015/03/weight-of-evidence-woe-and-information.html ## Fine classing -![image](../../../media/Course-Credit-Risk-Modeling_Intro-image2.jpg) +![image](../../media/Course-Credit-Risk-Modeling_Intro-image2.jpg) ## Coarse classing @@ -98,9 +98,9 @@ How much information the original independent variable brings with respect to ex - Widely used in credit risk modeling -![image](../../../media/Course-Credit-Risk-Modeling_Intro-image3.jpg) +![image](../../media/Course-Credit-Risk-Modeling_Intro-image3.jpg) -![image](../../../media/Course-Credit-Risk-Modeling_Intro-image4.jpg) +![image](../../media/Course-Credit-Risk-Modeling_Intro-image4.jpg) ## Overfitting @@ -125,7 +125,7 @@ logistic_regression_model.predit_proba AUC curve , RoC curve -![image](../../../media/Course-Credit-Risk-Modeling_Intro-image5.jpg) +![image](../../media/Course-Credit-Risk-Modeling_Intro-image5.jpg) ## Gini - Measure of the inequality between rich and poor individuals in an economy @@ -133,13 +133,13 @@ AUC curve , RoC curve - the cumulative proportion of defaulted borrowers as a function of the cumulative proportion of all borrowers. - AUROC = (Gini -- 1) / 2. -![image](../../../media/Course-Credit-Risk-Modeling_Intro-image6.jpg) +![image](../../media/Course-Credit-Risk-Modeling_Intro-image6.jpg) ## Kolmogorov-Smirnov Shows to what extent the model seperate the actual good borrowers from the actual bad borrowers -![image](../../../media/Course-Credit-Risk-Modeling_Intro-image7.jpg) +![image](../../media/Course-Credit-Risk-Modeling_Intro-image7.jpg) ## K-S coefficient @@ -215,9 +215,9 @@ Is the new data too different from the original one? Actual data vs Excepted data -![image](../../../media/Course-Credit-Risk-Modeling_Intro-image8.jpg) +![image](../../media/Course-Credit-Risk-Modeling_Intro-image8.jpg) -![image](../../../media/Course-Credit-Risk-Modeling_Intro-image9.jpg) +![image](../../media/Course-Credit-Risk-Modeling_Intro-image9.jpg) #### INTERPRETATION RULES @@ -253,7 +253,7 @@ Recovery rate is the dependent variable for the LGD model Recovery rate is the proportion of the total exposure that can be recovered by the lender once a default has occurred. -![image](../../../media/Course-Credit-Risk-Modeling_Intro-image10.jpg) +![image](../../media/Course-Credit-Risk-Modeling_Intro-image10.jpg) CCF - Credit Conversion Factor @@ -318,9 +318,9 @@ Combines physical and digital identities - including device, behavioral biometri ### AWS KYC -![image](../../../media/Projects-AIML-Fraud-Risk-image1.jpg) +![image](../../media/Projects-AIML-Fraud-Risk-image1.jpg) -![image](../../../media/Projects-AIML-Fraud-Risk-image2.jpg) +![image](../../media/Projects-AIML-Fraud-Risk-image2.jpg) ## Links diff --git a/docs/courses/course-intro-to-data-and-data-science.md b/docs/courses/course-intro-to-data-and-data-science.md index 8a8d15ea91f..6411990cf43 100755 --- a/docs/courses/course-intro-to-data-and-data-science.md +++ b/docs/courses/course-intro-to-data-and-data-science.md @@ -31,14 +31,14 @@ Analytics - Future (Explore potential future events) - Quantitative analytics - formulas + algorithms -![image](../../media/Course-Intro-to-Data-and-Data-Science-image1.jpg) +![image](../media/Course-Intro-to-Data-and-Data-Science-image1.jpg) Business Intelligence is the preliminary step of predictive analytics - analyse past data and extract useful insights - create appropriate models -![image](../../media/Course-Intro-to-Data-and-Data-Science-image2.jpg) +![image](../media/Course-Intro-to-Data-and-Data-Science-image2.jpg) Factor analysis diff --git a/docs/courses/course-time-series-analysis/intro-time-series.md b/docs/courses/course-time-series-analysis/intro-time-series.md index cbe3fd43e61..069e1e62467 100755 --- a/docs/courses/course-time-series-analysis/intro-time-series.md +++ b/docs/courses/course-time-series-analysis/intro-time-series.md @@ -4,7 +4,7 @@ - Used to determine whether a data set is distributed a certain way (To see if the values of a data set follow a given distribution?) - Usually showcases how the data fits a Normal Distribution - Takes all the values a variable can take, and arranges them in accending order - - ![image](../../../media/Course-Time-Series-Analysis_Intro-Time-Series-image1.jpg) + - ![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image1.jpg) - Y-axis expresses the price with highest one on top and lowest on bottom - X-axis expresses theoretical quantiles of the dataset. How many standard deviations away from the mean these values are. - Diagonal line shows what the data points should follow, if they are Normally Distributed @@ -45,9 +45,9 @@ Account for unexpected shocks in our data - **Neural Prophet** - Vector Autoregression (VAR) -![image](../../../media/Course-Time-Series-Analysis_Intro-Time-Series-image2.jpg) +![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image2.jpg) -![image](../../../media/Course-Time-Series-Analysis_Intro-Time-Series-image3.jpg) +![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image3.jpg) https://www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python @@ -162,7 +162,7 @@ Arbitrage - Buy and sell commodities and make a safe profit, while the price adj In the most intuitive sense, stationarity means that the statistical properties of a process generating a time series do not change over time. It does not mean that the series does not change over time, just that the way it changes does not itself change over time. The algebraic equivalent is thus a linear function, perhaps, and not a constant one; the value of a linear function changes as 𝒙 grows, but the way it changes remains constant - it has a constant slope; one value that captures that rate of change. -![image](../../../media/Course-Time-Series-Analysis_Intro-Time-Series-image4.jpg) +![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image4.jpg) Figure 1: Time series generated by a stationary (top) and a non-stationary (bottom) processes. @@ -172,17 +172,17 @@ https://towardsdatascience.com/stationarity-in-time-series-analysis-90c94f27322 Time-series stationarity implies taking consecutive samples of data with the same size should have identical covariances regardless of the starting point. -![image](../../../media/Course-Time-Series-Analysis_Intro-Time-Series-image5.jpg) +![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image5.jpg) Covariance is correlation multiplied by standard deviations Example of covariance stationarity is White Noise: -![image](../../../media/Course-Time-Series-Analysis_Intro-Time-Series-image6.jpg) +![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image6.jpg) ### Strict Stationarity -![image](../../../media/Course-Time-Series-Analysis_Intro-Time-Series-image7.jpg) +![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image7.jpg) - Rarely observed in nature, therefore stationarity = covariance stationarity @@ -190,7 +190,7 @@ Example of covariance stationarity is White Noise: - Dickey-Fuller test (D-F test) -![image](../../../media/Course-Time-Series-Analysis_Intro-Time-Series-image8.jpg) +![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image8.jpg) ```bash sts.adfuller(df.market_value) @@ -275,15 +275,15 @@ Correlation can take values between -1.0 to +1.0 Blue line shows **Significance** -![image](../../../media/Course-Time-Series-Analysis_Intro-Time-Series-image9.jpg) +![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image9.jpg) ## PACF (Partial Autocorrelation Function) -![image](../../../media/Course-Time-Series-Analysis_Intro-Time-Series-image10.jpg) +![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image10.jpg) -![image](../../../media/Course-Time-Series-Analysis_Intro-Time-Series-image11.jpg) +![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image11.jpg) -![image](../../../media/Course-Time-Series-Analysis_Intro-Time-Series-image12.jpg) +![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image12.jpg) ```python sgt.plot_pacf(df.market_value,lags=40,zero=False,method=('ols')) @@ -297,11 +297,11 @@ plt.show() It cancels out all additional channels a previous period value affects the present one -![image](../../../media/Course-Time-Series-Analysis_Intro-Time-Series-image13.jpg) +![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image13.jpg) ## The PACF Method -![image](../../../media/Course-Time-Series-Analysis_Intro-Time-Series-image14.jpg) +![image](../../media/Course-Time-Series-Analysis_Intro-Time-Series-image14.jpg) ## What is the difference between the ACF and the PACF? diff --git a/docs/courses/course-time-series-analysis/time-series-modeling.md b/docs/courses/course-time-series-analysis/time-series-modeling.md index ead233d8df1..ab3f0ca879a 100755 --- a/docs/courses/course-time-series-analysis/time-series-modeling.md +++ b/docs/courses/course-time-series-analysis/time-series-modeling.md @@ -8,7 +8,7 @@ Patterns in the past persist in the future 1. Significant coefficients -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image1.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image1.jpg) 2. Parsimonious (as simple as possible) @@ -41,16 +41,16 @@ We rely on autoregressive models when there is clear autocorrelation within the Since time series assumes that patterns found in the past translate to the future, if autocorrelation is present in the data, we need to us some form of AR model to capture this relationship if we wish to make good estimates -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image2.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image2.jpg) - A linear model, where current period values are a sum of past outcomes multiplied by a numeric factor - phi is between -1 and +1 ## Implementation of simple mode in Python -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image3.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image3.jpg) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image4.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image4.jpg) Which do we use to select the correct ARmodel and why: the ACFor the PACF? The PACF because it shows the individual effect each past value has on the current one. @@ -123,11 +123,11 @@ This implies that there are other factors apart from the previous values of the This is why, these models incorporate past residuals (also known as error terms) to help us improve our estimations. These make sure our model handles unexpected shocks well, which is why it's also known as a smoothing model -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image5.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image5.jpg) ## Implementation of the Simple Model in Python -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image6.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image6.jpg) #### How is the MAmodel different from the ARmodel? @@ -166,11 +166,11 @@ The ARMA incorporates both past values (like the AR) and past errors (like the M Picking the correct order for such a model could be tricky, since including or removing AR and MA orders can have widly different effects on the accuracy -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image7.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image7.jpg) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image8.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image8.jpg) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image9.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image9.jpg) #### Positive 0.7649 ar.L1.returns means that @@ -218,9 +218,9 @@ An ARIMA model with 0 degrees of integration is simply an ARMA model, and so any The order of integration (d) tells us exactly how many times we need to compute the non-seasonal differences between the values to reach stationarity and including more is discouraged (due to data attrition and interpretability of the results) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image10.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image10.jpg) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image11.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image11.jpg) p - AutoRegressive (AR) components @@ -236,7 +236,7 @@ No Integration - ARIMA (0, 0, q) = MA(q) - ARIMA (p, 0, 0) = AR(p) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image12.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image12.jpg) - For any integration we lose a single observation @@ -248,7 +248,7 @@ An ARMA(1,2) and an MA(1) Because the order of integration does not affect the number of coefficients we are trying to find. -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image13.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image13.jpg) ARIMA (5,1,1) @@ -274,9 +274,9 @@ These variables can be pretty much anything that can have an affect on the value These models are great, when a big part of the change period to period cannot be explained by past values and past errors alone, so including other relevant values might be of great help (like the prices for an index of a market of a neighbouring country) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image14.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image14.jpg) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image15.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image15.jpg) The ARIMA is just an integrated version of the ARMA model. What that means is, we simply integrate the data (however many times is needed) to get a stationary set @@ -317,9 +317,9 @@ For instance, by not including exogenous variables and having no integration, th The equation on the left is exactly that - a SARIMAX equivalent of a SARMA -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image16.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image16.jpg) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image17.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image17.jpg) Capital letters - Seasonal components @@ -330,13 +330,13 @@ s = Length of cycle - The number of periods needed to pass before the tendency reappears - s = 1 -> No seasonality -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image18.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image18.jpg) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image19.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image19.jpg) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image20.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image20.jpg) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image21.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image21.jpg) ## Volatility @@ -366,17 +366,17 @@ As you can see on the left side of the equation, the endogenous variable is the Thus, this is only the variance equation of the model. The simplest ARCH model assumes a 0 or constant mean, so this is the only equation we are interested in -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image22.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image22.jpg) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image23.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image23.jpg) Why does the ARCHmodel require 2 equations? Because we need some sort of benchmark to measure the volatility away from it. -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image24.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image24.jpg) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image25.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image25.jpg) How many coefficients does the simple ARCHmodel estimate? - 3 @@ -390,9 +390,9 @@ The generalization comes from the fact that including a single past variance wou It serves as a sort of ARMA equivalent to the ARCH, where we're including both past values and past errors (albeit squared) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image26.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image26.jpg) -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image27.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image27.jpg) How is the GARCHdifferent from the ARCH? @@ -418,7 +418,7 @@ Yield non-significant coefficients - auto_arima by default only compares the model based on their AIC -![image](../../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image28.jpg) +![image](../../media/Course-Time-Series-Analysis_Time-Series-Modeling-image28.jpg) http://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.auto_arima.html#pmdarima.arima.auto_arima diff --git a/docs/computer-science/courses/coursera-algorithms-part-1.md b/docs/courses/coursera-algorithms-part-1.md similarity index 98% rename from docs/computer-science/courses/coursera-algorithms-part-1.md rename to docs/courses/coursera-algorithms-part-1.md index e577f05b5ca..c3bf7c3a231 100755 --- a/docs/computer-science/courses/coursera-algorithms-part-1.md +++ b/docs/courses/coursera-algorithms-part-1.md @@ -1,5 +1,11 @@ # Coursera - Algorithms Part - 1 +https://www.coursera.org/learn/algorithms-part1 + +Final Grade - 97.8% + +https://github.com/deepaksood619/Coursera-Algorithms-Part-1 + ## Syllabus ## Week - 1 @@ -274,7 +280,7 @@ What data structure or data structures would you use? ## 6.1. Hash Tables 1. **4-SUM.** Given an array `a[]` of integers, the 4-SUM problem is to determine if there exist distinct indices i, j, k, and l such that `a[i] + a[j] = a[k] + a[l]`. Design an algorithm for the 4-SUM problem that takes time proportional ton^2(under suitable technical assumptions). -2. ![image](../../media/Coursera-Algorithms-Part-1-image1.jpg) +2. ![image](../media/Coursera-Algorithms-Part-1-image1.jpg) ## Assignments diff --git a/docs/computer-science/courses/coursera-algorithms-part-2.md b/docs/courses/coursera-algorithms-part-2.md similarity index 97% rename from docs/computer-science/courses/coursera-algorithms-part-2.md rename to docs/courses/coursera-algorithms-part-2.md index 69a2baaf251..76bc3b2e5c2 100755 --- a/docs/computer-science/courses/coursera-algorithms-part-2.md +++ b/docs/courses/coursera-algorithms-part-2.md @@ -1,5 +1,9 @@ # Coursera - Algorithms Part - 2 +https://www.coursera.org/learn/algorithms-part2 + +https://github.com/deepaksood619/Coursera-Algorithms-Part-2 + ## Week - 1 ### Undirected Graph diff --git a/docs/computer-science/courses/coursera-how-google-does-ml.md b/docs/courses/coursera-how-google-does-ml.md similarity index 78% rename from docs/computer-science/courses/coursera-how-google-does-ml.md rename to docs/courses/coursera-how-google-does-ml.md index c7db680fc4f..ebb87265556 100755 --- a/docs/computer-science/courses/coursera-how-google-does-ml.md +++ b/docs/courses/coursera-how-google-does-ml.md @@ -36,9 +36,9 @@ Prediction / Inference Phase ## Applications -![image](../../media/Coursera-How-Google-does-ML-image1.jpg) +![image](../media/Coursera-How-Google-does-ML-image1.jpg) -![image](../../media/Coursera-How-Google-does-ML-image2.jpg) +![image](../media/Coursera-How-Google-does-ML-image2.jpg) ## Topics @@ -72,15 +72,15 @@ Prediction / Inference Phase 5. Machine Learning - Automated feedback loop that can outpace human scale -![image](../../media/Coursera-How-Google-does-ML-image3.jpg) +![image](../media/Coursera-How-Google-does-ML-image3.jpg) -![image](../../media/Coursera-How-Google-does-ML-image4.jpg) +![image](../media/Coursera-How-Google-does-ML-image4.jpg) -![image](../../media/Coursera-How-Google-does-ML-image5.jpg) +![image](../media/Coursera-How-Google-does-ML-image5.jpg) -![image](../../media/Coursera-How-Google-does-ML-image6.jpg) +![image](../media/Coursera-How-Google-does-ML-image6.jpg) -![image](../../media/Coursera-How-Google-does-ML-image5.jpg) +![image](../media/Coursera-How-Google-does-ML-image5.jpg) ## Resources diff --git a/docs/courses/customer-analytics-in-python/intro.md b/docs/courses/customer-analytics-in-python/intro.md index 0e4ca3ef4f9..60fe3bb279f 100755 --- a/docs/courses/customer-analytics-in-python/intro.md +++ b/docs/courses/customer-analytics-in-python/intro.md @@ -80,13 +80,13 @@ Develop the **best** product or service and offer it at the **right price** thro ## Physical and Online Retailers -![image](../../../media/Customer-Analytics-in-Python_Intro-image1.jpg) +![image](../../media/Customer-Analytics-in-Python_Intro-image1.jpg) ## Price elasticity is: the percentage change in an economic outcome of interest with respect to one percent change in a respective price It is expected that: units sold from a brand would increase if the unit price of the brand decreases and would increase if the unit price of a competitor brand increases -![image](../../../media/Customer-Analytics-in-Python_Intro-image2.jpg) +![image](../../media/Customer-Analytics-in-Python_Intro-image2.jpg) - Price elasticity of purchase probability - Price elasticity of brand choice probability @@ -134,7 +134,7 @@ segmentation_std = scaler.fit_transform(df_segmentation) - Manhattan distance - Maximum distance -![image](../../../media/Customer-Analytics-in-Python_Intro-image3.jpg) +![image](../../media/Customer-Analytics-in-Python_Intro-image3.jpg) - Segmentation between clusters - Ward method (|A-B|^2)/n~scale~ @@ -187,15 +187,15 @@ Problems ## Within Cluster Sum of Squares (WCSS) is used to determine best clustering solution -![image](../../../media/Customer-Analytics-in-Python_Intro-image4.jpg) +![image](../../media/Customer-Analytics-in-Python_Intro-image4.jpg) Choosing number of clusters - Elbow method -![image](../../../media/Customer-Analytics-in-Python_Intro-image5.jpg) +![image](../../media/Customer-Analytics-in-Python_Intro-image5.jpg) -![image](../../../media/Customer-Analytics-in-Python_Intro-image6.jpg) +![image](../../media/Customer-Analytics-in-Python_Intro-image6.jpg) -![image](../../../media/Customer-Analytics-in-Python_Intro-image7.jpg) +![image](../../media/Customer-Analytics-in-Python_Intro-image7.jpg) ## Purchase Analytics @@ -203,11 +203,11 @@ Choosing number of clusters - Elbow method % change in purchase probability in response to a 1% change in price -![image](../../../media/Customer-Analytics-in-Python_Intro-image8.jpg) +![image](../../media/Customer-Analytics-in-Python_Intro-image8.jpg) -![image](../../../media/Customer-Analytics-in-Python_Intro-image9.jpg) +![image](../../media/Customer-Analytics-in-Python_Intro-image9.jpg) -![image](../../../media/Customer-Analytics-in-Python_Intro-image10.jpg) +![image](../../media/Customer-Analytics-in-Python_Intro-image10.jpg) We have a product, which costs $2.40-. We have obtained the price elasticity of purchase probability to be -0.79. The customer at this price point is: inelastic @@ -223,11 +223,11 @@ The purchase probability of a client at $2.56 is 0.7, while their price elastici ## Multnomial Logistic Regression - For a multiclass scenario -![image](../../../media/Customer-Analytics-in-Python_Intro-image11.jpg) +![image](../../media/Customer-Analytics-in-Python_Intro-image11.jpg) -![image](../../../media/Customer-Analytics-in-Python_Intro-image12.jpg) +![image](../../media/Customer-Analytics-in-Python_Intro-image12.jpg) -![image](../../../media/Customer-Analytics-in-Python_Intro-image13.jpg) +![image](../../media/Customer-Analytics-in-Python_Intro-image13.jpg) ## Deep Learning diff --git a/docs/computer-science/courses/data-integration-specialist-aws.md b/docs/courses/data-integration-specialist-aws.md similarity index 100% rename from docs/computer-science/courses/data-integration-specialist-aws.md rename to docs/courses/data-integration-specialist-aws.md diff --git a/docs/courses/google-professional-data-engineer-pde.md b/docs/courses/google-professional-data-engineer-pde.md index 7e570e0063f..13c5e27054a 100644 --- a/docs/courses/google-professional-data-engineer-pde.md +++ b/docs/courses/google-professional-data-engineer-pde.md @@ -25,13 +25,13 @@ - Anything related to unstructured data - Cloud Storage - Anything related to structured data / Transactional workload - Cloud SQL / Cloud Spanner -![Different structured solution options](../../media/Screenshot%202023-03-17%20at%207.23.13%20PM.png) +![Different structured solution options](../media/Screenshot%202023-03-17%20at%207.23.13%20PM.png) -![](../../media/Screenshot%202023-03-17%20at%207.23.56%20PM.png) +![](../media/Screenshot%202023-03-17%20at%207.23.56%20PM.png) -![Google data warehouse solution architecture](../../media/Screenshot%202023-03-17%20at%207.36.20%20PM.png) +![Google data warehouse solution architecture](../media/Screenshot%202023-03-17%20at%207.36.20%20PM.png) -![](../../media/Screenshot%202023-03-17%20at%207.43.56%20PM.png) +![](../media/Screenshot%202023-03-17%20at%207.43.56%20PM.png) ## Resources diff --git a/docs/computer-science/courses/microsoft-excel-google-sheets.md b/docs/courses/microsoft-excel-google-sheets.md similarity index 98% rename from docs/computer-science/courses/microsoft-excel-google-sheets.md rename to docs/courses/microsoft-excel-google-sheets.md index 8b2b228f971..aa16fab4c0b 100755 --- a/docs/computer-science/courses/microsoft-excel-google-sheets.md +++ b/docs/courses/microsoft-excel-google-sheets.md @@ -29,7 +29,7 @@ Rows - 1,048,576- Text to column - Conditional Formatting - Custom cell formatting -![image](../../media/Intro-to-Microsoft-Excel-Google-Sheets-image1.jpg) +![image](../media/Intro-to-Microsoft-Excel-Google-Sheets-image1.jpg) ## Shortcuts @@ -254,7 +254,7 @@ Rows - 1,048,576- Text to column https://365datascience.teachable.com/courses/enrolled/233558 -![image](../../media/Intro-to-Microsoft-Excel-Google-Sheets-image2.jpg) +![image](../media/Intro-to-Microsoft-Excel-Google-Sheets-image2.jpg) [Google Sheets - Full Course](https://www.youtube.com/watch?v=N2opj8XzYBY&ab_channel=freeCodeCamp.org) diff --git a/docs/computer-science/courses/mordern-algorithm-design.md b/docs/courses/mordern-algorithm-design.md similarity index 100% rename from docs/computer-science/courses/mordern-algorithm-design.md rename to docs/courses/mordern-algorithm-design.md diff --git a/docs/computer-science/courses/nutanix-hybrid-cloud.md b/docs/courses/nutanix-hybrid-cloud.md similarity index 96% rename from docs/computer-science/courses/nutanix-hybrid-cloud.md rename to docs/courses/nutanix-hybrid-cloud.md index f438b80200b..3ec3df95d8e 100755 --- a/docs/computer-science/courses/nutanix-hybrid-cloud.md +++ b/docs/courses/nutanix-hybrid-cloud.md @@ -25,13 +25,13 @@ NIST defines cloud computing as "a model for enabling ubiquitous, convenient, on [According to NIST](https://classroom.udacity.com/nanodegrees/nd321-1/parts/cd9ca74f-cbf3-40f8-9726-289a03b5560a/modules/c1b1466d-dba6-4e06-9014-b1cca87f5ca4/lessons/81e12a4b-5f16-4a67-8da0-8fe5eea1f483/concepts/The%20cloud%20is%20an%20experience%20and%20a%20mindset%20%5bshow:%20https:/csrc.nist.gov/publications/detail/sp/800-145/final%5d), the cloud model has 5 essential characteristics, 3 service models, and 4 deployment models. -![image](../../media/Nutanix-Hybrid-Cloud-image1.jpg) +![image](../media/Nutanix-Hybrid-Cloud-image1.jpg) -![image](../../media/Nutanix-Hybrid-Cloud-image2.jpg) +![image](../media/Nutanix-Hybrid-Cloud-image2.jpg) -![image](../../media/Nutanix-Hybrid-Cloud-image3.jpg) +![image](../media/Nutanix-Hybrid-Cloud-image3.jpg) -![image](../../media/Nutanix-Hybrid-Cloud-image4.jpg) +![image](../media/Nutanix-Hybrid-Cloud-image4.jpg) There are first two major aspects of cloud consumption that organizations need to take into consideration: fiscal consumption and workload predictability. @@ -39,7 +39,7 @@ There are first two major aspects of cloud consumption that organizations need t ## Virtualization -![image](../../media/Nutanix-Hybrid-Cloud-image5.jpg) +![image](../media/Nutanix-Hybrid-Cloud-image5.jpg) Virtualization uses an abstraction layer and resource scheduler called a hypervisor to run virtual machines on shared hardware resources. Virtual machines, or VMs, can be run at 80% or higher resource utilization without contention, solving one of the major problems of distributed and 3-tier architecture. @@ -123,7 +123,7 @@ Nutanix provides the public cloud benefits that organizations want with the cont - **Lower cloud costs:** You can also reduce your datacenter TCO by up to 60%. This will help optimize your public cloud spend with lower cloud costs. - **True hybrid cloud:** This refers to the ability for you to combine both public and private cloud operations with unified management. -![image](../../media/Nutanix-Hybrid-Cloud-image6.jpg) +![image](../media/Nutanix-Hybrid-Cloud-image6.jpg) 1. Acropolis: The data plane 2. Prism: The management plane diff --git a/docs/courses/readme.md b/docs/courses/readme.md index 058f115b098..aa6873ccf65 100755 --- a/docs/courses/readme.md +++ b/docs/courses/readme.md @@ -2,38 +2,61 @@ ## AI / Data Courses -- [Intro to Data and Data Science](courses/course-intro-to-data-and-data-science.md) -- [Data Storage and Processing](courses/course-data-storage-and-processing-edx.md) -- [Credit Risk Modeling](courses/course-credit-risk-modeling/syllabus.md) -- [Time Series Analysis](courses/course-time-series-analysis/syllabus.md) -- [Customer Analytics in Python](courses/customer-analytics-in-python/syllabus.md) -- [Data Mining](courses/course-data-mining-nptel.md) -- [Big Data Computing](courses/course-big-data-computing-nptel.md) -- [Intro to Tensorflow](courses/course-intro-to-tensorflow.md) -- [Launching into ML](courses/course-launching-into-ml.md) -- [Feature Engineering](courses/course-feature-engineering.md) -- [Art and Science of ML](courses/course-art-and-science-of-ml.md) -- [Professional Data Engineer (PDE)](courses/google-professional-data-engineer-pde.md) -- [ChatGPT Prompt Engineering for Developers](courses/chatgpt-prompt-eng.md) (2 May 2023) - -### 365 Data Science Courses (19 Mar 2020 - 14 Apr 2020) - -- **Intro to Data and Data Science** -- Introduction to Microsoft Excel -- Advanced Microsoft Excel -- Advanced Statistical Methods -- **Credit Risk Modeling in Python** -- **Time Series Analysis in Python** -- **Customer Analytics in Python**p +1. [Data Storage and Processing](courses/course-data-storage-and-processing-edx.md) +2. [Data Mining](courses/course-data-mining-nptel.md) +3. [Data Integration Specialist - AWS](courses/data-integration-specialist-aws.md) +4. [Big Data Computing](courses/course-big-data-computing-nptel.md) +5. Specialization - ML with TensorFlow on GCP (15 Jun'2018 - 15 July'2018) + 1. [Coursera - How Google does ML](courses/coursera-how-google-does-ml.md) + 2. [Intro to Tensorflow](courses/course-intro-to-tensorflow.md) + 3. [Launching into ML](courses/course-launching-into-ml.md) + 4. [Feature Engineering](courses/course-feature-engineering.md) + 5. [Art and Science of ML](courses/course-art-and-science-of-ml.md) +6. TheSchoolOfAI - Decentralized Applications (10 Sept'2018 - 16 Oct'2018) +7. [TheSchoolOfAI - Move37](ai/move-37/readme.md) (10 Sept'2018 - 16 Oct'2018) +8. [Professional Data Engineer (PDE)](courses/google-professional-data-engineer-pde.md) +9. [ChatGPT Prompt Engineering for Developers](courses/chatgpt-prompt-eng.md) (2 May 2023) + +### [365 Data Science Courses](courses/365-data-science-program.md) (19 Mar 2020 - 14 Apr 2020) + +1. [Intro to Data and Data Science](courses/course-intro-to-data-and-data-science.md) +2. [Intro to Microsoft Excel / Google Sheets](courses/microsoft-excel-google-sheets.md) +3. Advanced Microsoft Excel +4. [Advanced Statistical Methods in Python](courses/365-ds-advanced-stastistical-methods-in-python.md) +5. [Credit Risk Modeling in Python](courses/course-credit-risk-modeling/syllabus.md) +6. [Time Series Analysis in Python](courses/course-time-series-analysis/syllabus.md) +7. [Customer Analytics in Python](courses/customer-analytics-in-python/syllabus.md) +8. [Mathematics](courses/365-ds-mathematics.md) ## Others -- [google-professional-cloud-architect-pca](courses/google-professional-cloud-architect-pca.md) -- [course-aws-certified-database-specialty](databases/others/course-aws-certified-database-specialty.md) -- [course-advanced-database-systems](databases/others/course-advanced-database-systems.md) -- [Udacity - Nutanix Hybrid Cloud Scholarship Foundation Course Nanodegree Program ](computer-science/courses/nutanix-hybrid-cloud.md) (23 June 2020) -- [Architecting on AWS (Hands on training)](cloud/aws/course-architecting-on-aws.md) (12-14 Aug 2020) -- [AWS Certified Developer - Associate (Jan 2024)](courses/aws-certified-developer-associate.md) +1. Udacity Android Nanodegree with Scholarship By Tata Trust and Google (Jan, 16) +2. Coursera - Machine Learning by Andrew Ng (June'16) +3. [Git Branching](https://learngitbranching.js.org) (Nov'17) +4. [Git Game v1 & v2](https://www.git-game.com) (Nov'17) +5. [FreeCodeCamp](https://www.freecodecamp.org/deepaksood619) (Dec'17 - Jan'21) +6. [Touch Typing | TypingClub](knowledge/games/touch-typing.md) - https://www.typingclub.com (Dec'17 - Jan'20) +7. [Coursera - Algorithms, Part - 1 by Princeton University](courses/coursera-algorithms-part-1.md) (25 Dec'17 - 26 Jan'18) +8. [Coursera - Algorithms, Part - 2 by Princeton University](courses/coursera-algorithms-part-2.md) (14 Feb'18 - 02 Apr'18 - 22 Apr'18) +9. [Mordern Algorithm Design](courses/mordern-algorithm-design.md) +10. [SE Radio](courses/se-radio.md) +11. [Coursera - Mindshift](psychology/course-mindshift.md) +12. [Learning How to Learn](psychology/learning/course-learning-how-to-learn.md) (23 Mar'18 - 14th Apr'18) +13. [Responsive Web Design Training Course | Udacity](https://www.udacity.com/course/responsive-web-design-fundamentals--ud893) +14. [Responsive Images | Learn Web Development | Udacity](https://www.udacity.com/course/responsive-images--ud882) +15. [Learn the Latest Tech Skills; Advance Your Career | Udacity](https://www.udacity.com/course/intro-to-html-and-css--ud304) +16. [Udemy - Modern React with Redux by Stephen Grider](https://www.udemy.com/react-redux) (24 Mar'18) +17. [Udemy - Python for Data Structure, Algorithms, and Interviews](courses/udemy-python-for-data-structures-algorithms.md) (20 Apr'18) +18. Khan Academy - LSAT Preparation (Sept'2018) +19. [Udacity - Scalable Microservices with Kubernetes by Google](https://in.udacity.com/course/scalable-microservices-with-kubernetes--ud615) (5 Jan'2018) +20. [Introduction to Probability - The Science of Uncertainty by MITx on edX](mathematics/probability/intro-to-probability/readme.md) (June'2018) +21. [google-professional-cloud-architect-pca](courses/google-professional-cloud-architect-pca.md) +22. [course-aws-certified-database-specialty](databases/others/course-aws-certified-database-specialty.md) +23. [course-advanced-database-systems](databases/others/course-advanced-database-systems.md) +24. [Udacity - Nutanix Hybrid Cloud Scholarship Foundation Course Nanodegree Program](courses/nutanix-hybrid-cloud.md) (23 June 2020) +25. [Self Driving Nanodegree](courses/self-driving-nanodegree.md) +26. [Architecting on AWS (Hands on training)](cloud/aws/course-architecting-on-aws.md) (12-14 Aug 2020) +27. [AWS Certified Developer - Associate (Jan 2024)](courses/aws-certified-developer-associate.md) ## Certifications @@ -51,42 +74,12 @@ - DRF - http://www.django-rest-framework.org/tutorial/1-serialization - Django - https://docs.djangoproject.com/en/2.0/intro/tutorial01 -### Udacity - Scalable Microservices with Kubernetes by Google (5 Jan'2018) - -https://in.udacity.com/course/scalable-microservices-with-kubernetes--ud615 - -### Coursera - Mindshift - -Mindshift: Break Through Obstacles to Learning and Discover Your Hidden Potential by McMaster University on Coursera By - Barbara Oakley, Terrence Sejnowski and M.S. Orlando Trejo (16 Oct'2018 - 20 Oct'2018) - -https://www.coursera.org/learn/mindshift - -https://www.coursera.org/account/accomplishments/verify/ZT58TT77JZQ6 - -### TheSchoolOfAI - Decentralized Applications (10 Sept'2018 - 16 Oct'2018) - -### Khan Academy - LSAT Preparation (Sept'2018) - -### TheSchoolOfAI - Move37 - Reinforcement Leraning Techniques (10 Sept'2018 - 16 Oct'2018) - -### Coursera - Specialization - ML with TensorFlow on GCP (15 Jun'2018 - 15 July'2018) - -- How Google does Machine Learning by Google Cloud -- Launching into Machine Learning -- Intro to TensorFlow -- Feature Engineering -- Art and Science of ML - -### Introduction to Probability - The Science of Uncertainty by MITx on edX (June'2018) - -https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2 - -### Youtube (1 May'2018 - 31 May'2018) +## Youtube (1 May'2018 - 31 May'2018) - Essence of Linear Algebra - Essence of Calculus -### Khan Academy (May'2018) +## Khan Academy (May'2018) - Algebra - 1 - Algebra - 2 @@ -94,161 +87,104 @@ https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2 - Combinatorics - Linear Algebra -### Udemy - Python for Data Structures, Algorithms, and Interviews! (20 Apr'18) - -https://www.udemy.com/python-for-data-structures-algorithms-and-interviews - -### Udemy - Modern React with Redux by Stephen Grider (24 Mar'18) - -https://www.udemy.com/react-redux - -### Coursera - Learning How to Learn - -Learning How to Learn: Powerful mental tools to help you master tough subjects by University of California, San Diego. (23 Mar'18 - 14th Apr'18) - -https://www.coursera.org/learn/learning-how-to-learn - -### Coursera - Algorithms, Part - 2, by Princeton University (14 Feb'18 - 02 Apr'18 - 22 Apr'18) - -https://www.coursera.org/learn/algorithms-part2 - -https://github.com/deepaksood619/Coursera-Algorithms-Part-2 - -### Coursera - Algorithms, Part - 1, by Princeton University (25 Dec'17 - 26 Jan'18) - -https://www.coursera.org/learn/algorithms-part1 - -Final Grade - 97.8% - -https://github.com/deepaksood619/Coursera-Algorithms-Part-1 - -### TypingClub - https://www.typingclub.com (Dec'17 - Present) - -### FreeCodeCamp (Dec'17 - Present) - -https://www.freecodecamp.org - -https://www.freecodecamp.org/deepaksood619 - -### Git Game v1 & v2 (Nov'17) - -https://www.git-game.com - -### Git Branching (Nov'17) - -https://learngitbranching.js.org - -https://www.udacity.com/course/responsive-web-design-fundamentals--ud893 - -https://www.udacity.com/course/responsive-images--ud882 - -https://www.udacity.com/course/intro-to-html-and-css--ud304 - -### Coursera - Machine Learning by Andrew Ng (29.06.2016) - -### Udacity Android Nanodegree course (Feb -2015 - present) (Scholarship) - -By Tata Trust and Google for Udacity Android Nanodegree Course. (Jan, 16) - - ## MTech - **Refresher July 2015** - - Data Structures and Algorithm RFM201z (8) - - Advance Programming RFM202z (8) + - Data Structures and Algorithm RFM201z (8) + - Advance Programming RFM202z (8) - **1st Semester Aug-Dec 2015 (Monsoon)** - - Graduate Algorithms CSE525 (4) - - Compilers CSE601 (8) - - Mobile Computing CSE535 (6) - - Advanced Networks CSE534 (7) - - OOPD CSE600z (8) - - Network Anonymity and Privacy (Sit through) CSE749 - - TA - Network Security CSE550 + - Graduate Algorithms CSE525 (4) + - Compilers CSE601 (8) + - Mobile Computing CSE535 (6) + - Advanced Networks CSE534 (7) + - OOPD CSE600z (8) + - Network Anonymity and Privacy (Sit through) CSE749 + - TA - Network Security CSE550 - **2nd Semester Jan-Apr 2016 (Winter)** - - Database System Implementation CSE507 (8) - - Distributed Systems CSE530 (8) - - Security Engineering CSE552 (9) - - Programming Cloud Services for Mobile Applications CSE635 (9) - - Scientific Communication COM504z (7) - - Big Data Analytics (Sit through) CSE510A - - TA - Computer Networks CSE232 + - Database System Implementation CSE507 (8) + - Distributed Systems CSE530 (8) + - Security Engineering CSE552 (9) + - Programming Cloud Services for Mobile Applications CSE635 (9) + - Scientific Communication COM504z (7) + - Big Data Analytics (Sit through) CSE510A + - TA - Computer Networks CSE232 - **Summer Semester May-July 2016 (Summer)** - - Minor MTech Project IP (Independent Project) (Dynamic Password Application) CSE590 (10) + - Minor MTech Project IP (Independent Project) (Dynamic Password Application) CSE590 (10) - **3rd Semester Aug-Dec 2016 (Monsoon)** - - Modern Algorithm Design CSE519 (5) - - SSIOT (Smart Sensing for Internet Of Things) CSE576 (6) - - MTech Capstone Project (Building a distributed system for collecting health data using mobile devices) CSE698 (S) - - TA - Mobile Computing CSE535 + - Modern Algorithm Design CSE519 (5) + - SSIOT (Smart Sensing for Internet Of Things) CSE576 (6) + - MTech Capstone Project (Building a distributed system for collecting health data using mobile devices) CSE698 (S) + - TA - Mobile Computing CSE535 - **4th Semester Jan-May 2017 (Winter)** - - Independent Study (Sanghosti - Study) CSE690 (10) + - Independent Study (Sanghosti - Study) CSE690 (10) ## BTech - **1st Semester (2011-2012)** Total Marks - 763 - - Engineering Graphics 300211 (8) - - Environment & Ecology 300212 (8) - - Applied Physics - II 300213 (8) - - Applied Maths - I 300214 (8) - - Basic Mechanical Engineering 300215 (8) - - Basic Civil Engineering 300216 (8) + - Engineering Graphics 300211 (8) + - Environment & Ecology 300212 (8) + - Applied Physics - II 300213 (8) + - Applied Maths - I 300214 (8) + - Basic Mechanical Engineering 300215 (8) + - Basic Civil Engineering 300216 (8) - **2nd Semester (2011-2012)** Total Marks - 681 - - Language (Professional Communication in English) 300111 (7) - - Applied Chemistry 300112 (8) - - Applied Physics - I 300113 (6) - - Applied Maths - II 300214 (8) - - Basic Electrical Engineering 300115 (6) - - Engineering Mechanics 300116 (6) + - Language (Professional Communication in English) 300111 (7) + - Applied Chemistry 300112 (8) + - Applied Physics - I 300113 (6) + - Applied Maths - II 300214 (8) + - Basic Electrical Engineering 300115 (6) + - Engineering Mechanics 300116 (6) - **3rd Semester (2012-2013)** Total Marks - 776 - - Mathematics - III 322311 (8) - - Basic Electronics 328313 (8) - - Network Analysis And Synthesis 328314 (9) - - Problem Solving & Logic Building Using C 322315 (9) - - Computer Fundamentals 322312 (8) - - Digital Electronics & Logic Design 333312 (7) + - Mathematics - III 322311 (8) + - Basic Electronics 328313 (8) + - Network Analysis And Synthesis 328314 (9) + - Problem Solving & Logic Building Using C 322315 (9) + - Computer Fundamentals 322312 (8) + - Digital Electronics & Logic Design 333312 (7) - **4th Semester (2012-2013)** Total Marks - 778 - - Computational Mathematics 322411 (8) - - Discrete Structure 322412 (8) - - Data Structure 322413 (8) - - Computer System Architecture 322414 (9) - - Object Oriented Concepts & Programming Using C++ 322415 (7) - - Principles of Management 322416 (9) + - Computational Mathematics 322411 (8) + - Discrete Structure 322412 (8) + - Data Structure 322413 (8) + - Computer System Architecture 322414 (9) + - Object Oriented Concepts & Programming Using C++ 322415 (7) + - Principles of Management 322416 (9) - **5th Semester (2013-2014)** Total Marks - 831 - - Microprocessor & Interfaces 328515 (9) - - Analysis and Design of Algorithms 322512 (7) - - Operating System 322513 (8) - - Theory of Computation 322514 (9) - - Principles of Communication System 322515 (10) - - Database Management System 322516 (10) + - Microprocessor & Interfaces 328515 (9) + - Analysis and Design of Algorithms 322512 (7) + - Operating System 322513 (8) + - Theory of Computation 322514 (9) + - Principles of Communication System 322515 (10) + - Database Management System 322516 (10) - **6th Semester (2013-2014)** Total Marks - 844 - - Computer Networks 322611 (9) - - Compiler Design 322612 (10) - - UNIX & SHELL Programming 322613 (8) - - Software Engineering 322614 (9) - - Computer Graphics 322615 (9) - - Elective - I: Inter Networking with TCP/IP 322635 (8) + - Computer Networks 322611 (9) + - Compiler Design 322612 (10) + - UNIX & SHELL Programming 322613 (8) + - Software Engineering 322614 (9) + - Computer Graphics 322615 (9) + - Elective - I: Inter Networking with TCP/IP 322635 (8) - **7th Semester (2014-2015)** Total Marks - 834 - - Internet & Multimedia Technology 322711 (9) - - Parallel Processor & Computing 322712 (8) - - Network Programming 322713 (8) - - Operations Research 322714 (9) - - Elective - II: Enterprise Resource Planning 322754 (8) - - Minor Project: Online Voting System 322724 (10) + - Internet & Multimedia Technology 322711 (9) + - Parallel Processor & Computing 322712 (8) + - Network Programming 322713 (8) + - Operations Research 322714 (9) + - Elective - II: Enterprise Resource Planning 322754 (8) + - Minor Project: Online Voting System 322724 (10) - **8th Semester (2014-2015)** Total Marks - 832 - - Data Mining & Warehousing 322812 (7) - - Artificial Intelligence & Expert Systems 322821 (8) - - Elective - III: Cyber Crime & Laws 322878 (8) - - Software Project Management 322813 (9) - - Elective - IV: E-Commerce & Strategic IT 300882 (9) - - Major Project: Automatic Vehicle License Plate Detection 322824 (10) + - Data Mining & Warehousing 322812 (7) + - Artificial Intelligence & Expert Systems 322821 (8) + - Elective - III: Cyber Crime & Laws 322878 (8) + - Software Project Management 322813 (9) + - Elective - IV: E-Commerce & Strategic IT 300882 (9) + - Major Project: Automatic Vehicle License Plate Detection 322824 (10) diff --git a/docs/computer-science/courses/se-radio.md b/docs/courses/se-radio.md similarity index 100% rename from docs/computer-science/courses/se-radio.md rename to docs/courses/se-radio.md diff --git a/docs/computer-science/courses/self-driving-nanodegree.md b/docs/courses/self-driving-nanodegree.md similarity index 100% rename from docs/computer-science/courses/self-driving-nanodegree.md rename to docs/courses/self-driving-nanodegree.md diff --git a/docs/computer-science/courses/udemy-python-for-data-structures-algorithms.md b/docs/courses/udemy-python-for-data-structures-algorithms.md similarity index 97% rename from docs/computer-science/courses/udemy-python-for-data-structures-algorithms.md rename to docs/courses/udemy-python-for-data-structures-algorithms.md index 4dac81d9670..ab3dee08759 100755 --- a/docs/computer-science/courses/udemy-python-for-data-structures-algorithms.md +++ b/docs/courses/udemy-python-for-data-structures-algorithms.md @@ -1,19 +1,12 @@ # Udemy - Python for data structures algorithms 1. Array Sequences - 2. Stacks, Queues, and Deques - 3. Linked Lists - 4. Recursion - 5. Trees - 6. Searching and Sorting - 7. Graph - 8. Riddles ## References diff --git a/docs/mathematics/probability/intro-to-probability/intro-syllabus.md b/docs/mathematics/probability/intro-to-probability/intro-syllabus.md index 1dafc3bca7c..752e08b10eb 100755 --- a/docs/mathematics/probability/intro-to-probability/intro-syllabus.md +++ b/docs/mathematics/probability/intro-to-probability/intro-syllabus.md @@ -267,6 +267,7 @@ L23: More on the Poisson process L24: Finite-state Markov chains L25: Steady-state behavior of Markov chains L26: Absorption probabilities and expected time to absorption -Course - https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2 + +Course - [MITx: Probability - The Science of Uncertainty and Data | edX](https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2) Syllabus - https://courses.edx.org/courses/course-v1:MITx+6.041x_4+1T2017/course diff --git a/docs/networking/networking-concepts/data-center-networking.md b/docs/networking/networking-concepts/data-center-networking.md index dfc9470c33a..1b5231f3c7d 100755 --- a/docs/networking/networking-concepts/data-center-networking.md +++ b/docs/networking/networking-concepts/data-center-networking.md @@ -36,4 +36,4 @@ https://en.wikipedia.org/wiki/InfiniBand ## Links -[nutanix-hybrid-cloud](computer-science/courses/nutanix-hybrid-cloud.md) +[nutanix-hybrid-cloud 1](courses/nutanix-hybrid-cloud.md) diff --git a/docs/psychology/course-mindshift.md b/docs/psychology/course-mindshift.md index a261428eac9..39f014702e6 100755 --- a/docs/psychology/course-mindshift.md +++ b/docs/psychology/course-mindshift.md @@ -82,3 +82,5 @@ In this final week of the course, we'll be exploring how and why to keep yoursel Mindshift: Break Through Obstacles to Learning and Discover Your Hidden Potential by McMaster University on Coursera taughty by Barbara Oakley, Terrence Sejnowski and M.S. Orlando Trejo https://www.coursera.org/learn/mindshift + +https://www.coursera.org/account/accomplishments/verify/ZT58TT77JZQ6 diff --git a/docs/readme.md b/docs/readme.md index f6fcc74b64f..4172b0f590f 100755 --- a/docs/readme.md +++ b/docs/readme.md @@ -16,26 +16,26 @@ If this is your first time visiting this wiki, start from the outline below and - [About me](about-me/readme.md) - [Computer Science](computer-science/readme.md) - - [AI](ai/readme.md) - - [Algorithms](algorithms/readme.md) - - [Data Structures](data-structures/readme.md) - - [Languages](languages/readme.md) - - [Python](python/readme.md) - - [Networking](networking/readme.md) - - [Databases](databases/readme.md) - - [Frontend](frontend/readme.md) - - [DevOps](devops/readme.md) - - [Cloud - AWS](cloud/aws/readme.md) - - [Cloud - Others](cloud/others/readme.md) - - [Technologies](technologies/readme.md) - - [Decentralized Applications / Cryptocurrencies](decentralized-applications/readme.md) - - [Courses](courses/readme.md) + - [AI](ai/readme.md) + - [Algorithms](algorithms/readme.md) + - [Data Structures](data-structures/readme.md) + - [Languages](languages/readme.md) + - [Python](python/readme.md) + - [Networking](networking/readme.md) + - [Databases](databases/readme.md) + - [Frontend](frontend/readme.md) + - [DevOps](devops/readme.md) + - [Cloud - AWS](cloud/aws/readme.md) + - [Cloud - Others](cloud/others/readme.md) + - [Technologies](technologies/readme.md) + - [Decentralized Applications / Cryptocurrencies](decentralized-applications/readme.md) + - [Courses](courses/readme.md) - [Book Summaries](book-summaries/readme.md) - [Mathematics](mathematics/readme.md) - [Knowledge](knowledge/readme.md) - - [Economics](economics/readme.md) - - [Management](management/readme.md) - - [Psychology](psychology/readme.md) + - [Economics](economics/readme.md) + - [Management](management/readme.md) + - [Psychology](psychology/readme.md) ## Size of Wiki