Link to Week 1's Jupyter Notebook

• Numpy
• Pandas
• Matplotlib

Link to Week 2's Jupyter Notebook

• One Hot encoding
• Label Encoding
• Normalization
• Dealing with Missing values
• Introduction to Machine learning
• Types of Learning (Supervised, Unsupervised and Reinforcement)
• Application of Machine Learning

Link to Week 3's Jupyter Notebook

• Linear Regression
• Multiple Linear Regression
• Polynomial Regression

Link to Week 4's Jupyter Notebook

• Logistic Regression
• Naive Bayes
• Introduction to Decision Trees for Classification

Link to Week 5's Jupyter Notebook

• OverFitting
• UnderFitting
• Regularization
• Support Vector Machines

Link to Week 6's Jupyter Notebook

• K-means Clustering
• Hierarchical Clustering

Link to Week 7's Jupyter Notebook

• PCA

• LDA

• Kernel PCA

• Model Selection:

• K-fold Cross Validation

• Parameter Tuning

• Grid Search

• Stacking

Link to Week 8's Jupyter Notebook

• Gradient Boosting
• XGBoost