Machine Learning Fundamentals with Python

Name: Machine Learning Fundamentals with Python
Availability: InStock
Author: FreeAcademy

Intermediate

Assumes foundational knowledge. Builds on basics.

Master machine learning from the ground up. Learn supervised and unsupervised learning, build models with scikit-learn, and understand the intuition behind algorithms like linear regression, decision trees, and neural networks. Hands-on Python exercises with real datasets.

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Course Content

13 modules • 35 lessons

Course Introduction

1 lesson

1.1

Welcome to Machine Learning Fundamentals

Module 1: What is Machine Learning?

3 lessons

2.1

What is Machine Learning?

2.2

How Machines Learn

Module 2: Types of Machine Learning

3 lessons

3.1

Supervised Learning

3.2

Unsupervised Learning

3.3

Reinforcement Learning

Module 6: Decision Trees and Random Forests

3 lessons

7.1

Decision Trees Intuition

7.2

Random Forests

7.3

Building Tree-Based Models

Module 8: Train/Test Splits and Cross-Validation

3 lessons

9.1

Overfitting and Underfitting

9.2

Train/Test Split

9.3

Cross-Validation

Course Conclusion

1 lesson

13.1

Machine Learning Fundamentals with Python

Course Content

Course Introduction

Welcome to Machine Learning Fundamentals

Module 1: What is Machine Learning?

What is Machine Learning?

How Machines Learn

Real-World Applications

Module 2: Types of Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Module 3: Your ML Toolkit

NumPy Essentials for ML

Pandas for Data Handling

Introduction to Scikit-learn

Module 4: Linear Regression

Linear Regression Intuition

Cost Functions and Gradient Descent

Building Your First Linear Regression Model

Module 5: Classification Basics

Introduction to Classification

Logistic Regression

K-Nearest Neighbors

Module 6: Decision Trees and Random Forests

Decision Trees Intuition

Random Forests

Building Tree-Based Models

Module 7: Model Evaluation and Metrics

Regression Metrics

Classification Metrics

Confusion Matrix and ROC Curves

Module 8: Train/Test Splits and Cross-Validation

Overfitting and Underfitting

Train/Test Split

Cross-Validation

Module 9: Feature Engineering Basics

What is Feature Engineering?

Handling Missing Data

Encoding and Scaling Features

Module 10: Intro to Neural Networks

Neural Networks Intuition

Perceptrons and Layers

Deep Learning Overview

Module 11: Capstone Project

Project Overview: Build a Prediction Model

Project Implementation

Next Steps in Your ML Journey

Course Conclusion

Your Machine Learning Journey Begins