Introduction to the Course

Get an overview of the target audience, prerequisites, and motivation for taking this course.

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 world of machine learning (ML), a cutting-edge field that’s transforming industries across the globe. Machine learning is the science of making computers learn and adapt without being explicitly programmed, and it plays a crucial role in data analysis, prediction, and decision-making.

In this course, you’ll learn the core concepts, algorithms, and techniques that power machine learning. Whether you’re a beginner or an intermediate learner, this course offers something valuable. We’ll dive into the fundamentals, implement algorithms from scratch, and compare our results 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. Linear algebra, probability, and statistics are essential for understanding data transformations and manipulating matrices, probabilistic models, and data analysis. An understanding of calculus can aid in understanding optimization algorithms, which are central to training machine learning models. Proficiency in programming languages like Python is also required for implementing machine learning algorithms and working with libraries such as 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.

  • 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.