Foundational Machine Learning for Aspiring Engineers (8-9 hrs/week)

Master the core concepts and practical skills of machine learning, from data preprocessing to model evaluation, to build a strong foundation for an ML engineering career.
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Introduction to Machine Learning Fundamentals

Unit 1: What is Machine Learning?

Unit 2: Core ML Paradigms

Unit 3: Common ML Task Types

Unit 4: ML in the Real World

Python for Data Manipulation: NumPy Essentials

Unit 1: NumPy Array Basics

Unit 2: Array Operations and Math

Unit 3: Indexing, Slicing, and Reshaping

Python for Data Manipulation: Pandas for DataFrames

Unit 1: Pandas Basics: Series & DataFrames

Unit 2: Loading & Selecting Data

Unit 3: Data Aggregation & Merging

Data Preprocessing and Feature Engineering

Unit 1: The Importance of Clean Data

Unit 2: Categorical Feature Engineering

Unit 3: Numerical Feature Scaling

Foundational Supervised Learning: Linear Regression

Unit 1: Introduction to Regression

Unit 2: Simple Linear Regression Core

Unit 3: Multiple Linear Regression

Unit 4: Implementing Linear Regression

Unit 5: Interpreting and Evaluating Linear Regression

Foundational Supervised Learning: Logistic Regression

Unit 1: Introduction to Logistic Regression

Unit 2: Implementing Logistic Regression

Unit 3: Advanced Logistic Regression Concepts

Foundational Supervised Learning: Decision Trees

Unit 1: Decision Tree Basics

Unit 2: Splitting Criteria and Tree Growth

Unit 3: Implementing Decision Trees with Scikit-learn

Foundational Supervised Learning: K-Nearest Neighbors

Unit 1: KNN: The Basics

Unit 2: Implementing KNN with Scikit-learn

Unit 3: Advanced KNN Considerations

Model Evaluation and Validation Techniques

Unit 1: The Need for Evaluation

Unit 2: Splitting Your Data

Unit 3: Classification Metrics

Unit 4: Regression Metrics

Introduction to Unsupervised Learning: K-Means Clustering

Unit 1: Unsupervised Learning: The Basics

Unit 2: K-Means Algorithm Explained

Unit 3: Implementing K-Means with Scikit-learn

End-to-End Machine Learning Workflow and Best Practices

Unit 1: The ML Project Lifecycle

Unit 2: Building an ML Pipeline

Unit 3: ML Best Practices & Ethics