Machine-Learning-using-Python

Final Thesis of the 'Applied ML using Python' program.

The advances in computing over the last few decades, both in hardware and software, have led to an unstoppable development of Artificial Intelligence, to the point where its applications are integrated into our daily lives. The advancements we see in home automation, voice assistance, mobile visual recognition, device recommendations... are part of human existence and make our lives easier, often without us being fully aware of the programming used.

Machine Learning techniques are part of the Artificial Intelligence developments, focusing on machine learning. We are experiencing a paradigm shift that allows devices to learn, extracting hidden knowledge from the enormous volume of data we can handle, a phenomenon we generically refer to as Big Data.

Through this program, I have delved into an exciting topic like Machine Learning. I began with an introduction to Python, then got to know its libraries, and finally focused on Machine Learning.

ML Algorithms used in this Project

• LinearRegression
• SVC/SVM Linear Kernel
• SVC/SVM RBF
• OvR (One vs Rest) Classifier strategy
• K-Means
• DBSCAN
• HDBSCAN
• Agglomerative Clustering
• PCA

Python Libraries used in this Project

• Scikit-learn
• SciPy
• MgLearn
• HDBSCAN
• Matplotlib
• MLextend
• Seaborn
• Numpy
• Pandas
• CSV