Machine Learning Part 1 – Data Preprocessing & Supervised Learning

First steps in Machine Learning: data preprocessing techniques (cleaning, encoding, scaling) and Exploratory Data Analysis to understand datasets.

1. 1Data Preprocessing & EDA Part 1Free
2. 2Data Preprocessing & EDA Part 2
3. 3Data Preprocessing & EDA Part 3
4. 4Data Preprocessing & EDA Part 4
5. 5Data Preprocessing & EDA Part 5
6. 6Data Preprocessing & EDA Part 6

Linear regression models: understanding concepts, building simple and multiple linear regression models, and evaluating model performance.

1. 1Simple and Multiple Linear Regression Part 1Free
2. 2Simple and Multiple Linear Regression Part 2
3. 3Simple and Multiple Linear Regression Part 3
4. 4Simple and Multiple Linear Regression Part 4
5. 5Simple and Multiple Linear Regression Part 5

Classification algorithms: logistic regression, KNN, SVM, and classification model evaluation techniques (confusion matrix, precision, recall, F1-score).

1. 1Classification Part 1Free
2. 2Classification Part 2
3. 3Classification Part 3
4. 4Classification Part 4
5. 5Classification Part 5

Tree-based models: Decision Tree, Random Forest for regression, and performance comparison with linear models.

1. 1Tree-based Regression Part 1Free
2. 2Tree-based Regression Part 2

Comprehensive exercises and supervised learning portfolio project: apply preprocessing, regression, and classification on real datasets.

1. 1Exercise and Project Portfolio 7 Part 1
2. 2Exercise and Project Portfolio 7 Part 2Free