Machine Learning with Python

“Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.” - Andrew Ng

Welcome to the machine learning part of this course in which you will learn how to build benchmark classifiers for solving problems related to both supervised and unsupervised learning using Scikit-learn package. This part of course is divided into two subparts : supervised and unsupervised learning. In the sub part dedicated to supervised learning you will work with regression and classification, whereas in the unsupervised learning part you will have to encounter the problem of clustering. Through the exercises and different tasks you will master the core concepts of machine learning, that will help you in digging deeper into the field.

Sections:

• Supervised learning
  • Regression
    • Linear regression and core concepts of machine learning
    • Polynomial regression
    • l1 and l2 regularization
    • SVM (Support Vector Machines)
    • Decision trees family
  • Classification
    • Binary classification and core concepts of it
    • Hyperparameters tuning
    • Multiclassification
• Unsupervised learning
  • Clustering with unsupervised learning and key concepts of it
    • Understanding KMeans
    • Understanding DBSCAN
    • Description of assignment
  • Dimensionality reduction
    • Understanding PCA
    • Understanding t-SNE
    • Description of assignment