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Fundamentals of Machine Learning: A Pythonic Introduction

Introduction to the Course

Explore foundational machine learning concepts in this introductory lesson. Understand the course structure, prerequisites, and how you'll implement algorithms from scratch with Python. Gain a clear roadmap to build practical skills through projects and hands-on comparison with scikit-learn.

We'll cover the following...

Overview

Welcome to Fundamentals of Machine Learning: A Pythonic Introduction. This introductory course is designed to provide a solid foundation in the field of machine learning (ML), a rapidly evolving discipline that is transforming industries worldwide. Machine learning is the study of methods that enable computers to learn from data and improve their performance without being explicitly programmed, and it plays a crucial role in data analysis, prediction, and decision-making.

In this course, you will learn the core concepts, algorithms, and techniques that underpin machine learning. Whether you are a beginner or an intermediate learner, the course offers material of practical and conceptual value. The content covers fundamental principles, includes hands-on implementations of algorithms from scratch, and provides systematic comparisons with scikit-learn, the widely used Python library for machine learning.

Target audience

This course is designed for both beginners and intermediate learners who want to explore the exciting world of machine learning.

Beginners: If you’re new to the field of machine learning and have a basic knowledge of programming, linear algebra, probability, and statistics, this course is a perfect starting point. We’ll take you through the basics step by step, ensuring you grasp the foundational concepts before diving into more advanced topics.

Intermediate learners: If you already have some experience in machine learning and meet the prerequisites, you’ll also find this course valuable. We not only cover the fundamentals but also provide hands-on experience by implementing algorithms from scratch and comparing their performance with scikit-learn.

Prerequisites

Prerequisites for this course include a foundational understanding of key mathematical and computer science concepts.

Knowledge of linear algebra, probability, and statistics is essential for understanding data transformations and for working with matrices, probabilistic models, and data analysis techniques. Familiarity with calculus can further support comprehension of optimization algorithms, which are central to the training of machine learning models.

Proficiency in a programming language such as Python is also required for implementing machine learning algorithms and for working with libraries including NumPy, pandas, and TensorFlow.

Why take this course?

Here are some compelling reasons to take this course:

  • Interactive learning tools: To enhance your learning experience, we’ve created a variety of animations and applications that visually explain complex concepts and algorithms. These tools make it easier to grasp abstract ideas and reinforce your understanding. Let’s see the following animation we’ve created to explain the model overfitting.

Note: Don’t worry if overfitting feels a bit confusing right now. Think of it like memorizing answers for a test instead of understanding the concepts. The model performs well on what it has seen but struggles with new questions. We’ll explore this in more detail later in the course.

  • Algorithm implementation from scratch: In this course, we’ll guide you through implementing machine learning algorithms from scratch. This approach deepens your understanding of how these algorithms work under the hood.

  • Performance comparison with scikit-learn: We believe in learning by doing. We’ll compare our custom-built algorithms’ performance with the industry-standard scikit-learn library. This hands-on approach allows us to gain confidence in our machine-learning skills.

  • Hands-on experience: Throughout the course, we’ll work on six exciting projects to acquire practical experience with real-world machine learning applications. These projects serve to enhance our understanding of the concepts and techniques we’ll learn.