Hands-on Scikit-learn for Machine Learning

Course Curriculum

Chapter 1: Getting Started with a Simple ML Model in Scikit-learn

Lecture 1: The Course Overview

Lecture 2: Course Objectives, Software Installation, and Setup

Lecture 3: Overview of Scikit-learn

Lecture 4: Scikit-learn Programming Workflow Example

Lecture 5: Applying a KNN Model on Cancer Dataset

Lecture 6: Improving the KNN Performance on Cancer Dataset

Chapter 2: Classification Models

Lecture 1: Linear and Logistic Regression

Lecture 2: Evaluating Classification Models

Lecture 3: Logistic Regression and Evaluation with Scikit-learn

Lecture 4: Decision Trees

Lecture 5: Bagging, Boosting, and Random Forests

Lecture 6: Applying Ensemble Methods with Scikit-learn

Lecture 7: Support Vector Machines

Lecture 8: Applying Support Vector Machines Classifier with Scikit-learn

Lecture 9: Multi-class Classification Example with Scikit-learn

Chapter 3: Supervised Machine Learning – Regression

Lecture 1: Downloading and Inspecting the Dataset

Lecture 2: Handling Categorical Features and Missing Values

Lecture 3: Creating Train and Test Sets and Finding Correlation

Lecture 4: Feature Scaling, Evaluating Regression Models, and Applying Linear Regression

Lecture 5: Regularization Techniques for Regression Analysis

Lecture 6: Applying Random Forest for Regression Analysis

Lecture 7: Multi-Layer Perceptron, Neural Networks, and Applying MLP with Scikit-learn

Chapter 4: Unsupervised Learning —Dimensionality Reduction

Lecture 1: Principle Component Analysis

Lecture 2: Applying PCA with Scikit-learn for Feature Reduction

Lecture 3: Applying PCA for a Regression Problem on a Large Dataset

Lecture 4: Nonlinear Methods of Feature Extraction – t-SNE and Isomap

Lecture 5: Applying Dimensionality Reduction Techniques to Images

Chapter 5: Unsupervised Learning – Clustering

Lecture 1: Introduction to Clustering and k-means Clustering

Lecture 2: Applying k-means with Scikit-learn

Lecture 3: Agglomerative Clustering

Lecture 4: DBSCAN Clustering Algorithm

Lecture 5: Applying DBSCAN with Scikit-learn

Chapter 6: Improving ML Model Performance

Lecture 1: Handling Missing Values and Data Cleaning

Lecture 2: Handling Missing Values and Scaling Numerical Features

Lecture 3: Handling Outliers and Removing Distribution Skew

Lecture 4: Handling Outliers and Removing Distribution Skew (Continued)

Lecture 5: Deriving Additional Features

Lecture 6: Evaluating Different Models and Cross- Validation

Lecture 7: Model Selection Strategies

Lecture 8: Feature Engineering for Classification

Lecture 9: Model Selection Strategies for Credit Risk Assessment

Chapter 7: Creating Pipelines and Advanced Model Selection

Lecture 1: Creating Processing Pipelines with Scikit-learn

Lecture 2: Using Pipelines on Our Credit Risk Assessment Dataset

Lecture 3: Advanced Model Selection Techniques

Lecture 4: Practicing Pipelines with a Time-Series Dataset

Chapter 8: Handling Text Data with Scikit-learn

Lecture 1: Bag-of-Words Model and Sentiment Analysis

Lecture 2: Using Stop-Words and TF-IDF for Sentiment Analysis

Lecture 3: Using N-Grams to Improve Model Performance for Sentiment Analysis

Lecture 4: Using Stemming and Lemmatization for Sentiment Analysis

Lecture 5: Topic Modeling with TruncatedSVD and Latent Dirichlet Allocation