Course Structure

Week 1

Python and ML fundamentals

1. Python refresher
2. Data types and structure
3. Introduction to Numpy, Pandas, and Matplotlib:
4. Handling missing data with Pandas:
5. Data visualization techniques using Matplotlib and Seaborn
6. Feature scaling and normalization with Numpy and Scikit-learn.
7. Execute a high-level end-to-end machine learning project using various ML packages.

Week 2

Statistics and Unsupervised Methods

1. K-means clustering
2. Gaussian Mixture Model
3. Principal component analysis (PCA)
4. Singular Value Decomposition (SVD)
5. t-Distributed Stochastic Neighbor Embedding (t-SNE)

Week 3

Overview of machine learning concepts

1. Applications and examples
2. Types of machine learning
3. Parametric and non-parametric models
4. Python libraries
5. Optimization Methods and Gradient Descent
6. Linear regression
7. Evaluation metrics for regression
8. Polynomial regression
9. Bias-variance tradeoff
10. Regularization techniques (LASSO, Ridge)

Week 4

Supervised methods: Regression and Classification

1. Logistic regression
2. Evaluation metrics for classification
3. Support vector machines (SVM):
4. Decision trees and random forests
5. K-nearest neighbors (KNN)
6. Cross-validation
7. Grid search and random search for hyperparameter tuning

Week 5

Deep learning methods

1. Introduction to Autoencoders
2. Neural network architecture
3. Activation functions
4. Backpropagation
5. Training a neural network with TensorFlow and Keras

Week 6

Computer vision and advanced methods

1. CNN architecture
2. Convolution and pooling layers
3. Applications in computer vision
4. Generative models
5. Deep unsupervised learning

Course Textbook/Resources/Materials Müller, A. C., & Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly Media, Incorporated.