Practical Machine Learning for Python Developers

Rapidly acquire essential machine learning skills, from data preprocessing to model evaluation, specifically tailored for Python developers seeking practical application.

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Foundations of Machine Learning

Unit 1: What is Machine Learning?

Unit 2: The ML Project Workflow

Data Preparation and Feature Engineering

Unit 1: Understanding Your Data

Unit 2: Categorical Feature Encoding

Unit 3: Numerical Feature Scaling

Unit 4: Dataset Splitting

Supervised Learning: Regression Models

Unit 1: Introduction to Regression

Unit 2: Building Linear Regression Models

Unit 3: Interpreting and Using Regression Models

Unit 4: Advanced Regression Concepts

Supervised Learning: Classification Models

Unit 1: Introduction to Classification

Unit 2: Logistic Regression Fundamentals

Unit 3: Implementing Logistic Regression

Unit 4: Decision Trees for Classification

Unit 5: Implementing Decision Trees

Model Evaluation and Generalization

Unit 1: Evaluating Regression Models

Unit 2: Evaluating Classification Models

Unit 3: Generalization and Cross-Validation

Unsupervised Learning and Ethical Considerations

Unit 1: Introduction to Unsupervised Learning

Unit 2: K-Means Clustering in Practice

Unit 3: Ethical Considerations in ML