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The Complete Machine Learning Course: From Zero to Expert!

Name: The Complete Machine Learning Course: From Zero to Expert!
Rating: 4.4 (82 reviews)

Learn Machine Learning in Python from scratch. Everything you need to get the job you want! Code templates included.

Created byLucas Bazilio

Last updated 4/2025

English

What you'll learn

Master Machine Learning in Python.
Become a confident, modern, and advanced Machine Learning engineer from scratch.
Become job-ready by understanding how Machine Learning really works behind the scenes.
Develop full Machine Learning pipelines for real datasets, from data cleaning to evaluation.
How to think and work like a Machine Learning engineer: problem-solving, researching, workflows.
Get fast and friendly support in the Q&A area.
Master all Machine Learning python libraries: numpy, scipy, pandas, scikit-learn, matplotlib, seaborn, imblearn, etc.
Learn how neural networks work and implement them using frameworks like TensorFlow or PyTorch.
Practice your skills with 100+ challenges and assignments (solutions included).

Course content

23 sections • 97 lectures • 26h 30m total length

Google Colab for Programming in Python3:08
In this lecture we explain how to use google colab for programming in Python.

Introduction to PCA4:44
On this lesson we introduce the Principal Component Analysis and give a brief background to the technique.
Introduction to the Dataset23:05
On this lesson we introduce the dataset crabs.csv.
Later we will use PCA for the visualization of this dataset.
Initial Visualization30:16
On this lesson we perform a basic exploration of the data set and an initial visualization.
At the end we discuss the importance of applying dimensionality reduction techniques.
On the following lesson we will apply PCA.
Using PCA1:05:12
On this lesson we use the Principal Component Analysis technique to visualize the separation between the classes.

Introduction to LLE2:59
On this lesson we introduce the Localy Linear Embedding (LLE) algorithm.
Locally Linear Embedding Algorithm3:39
On this lesson we introduce the steps that are followed in the LLE algorithm.
On future lessons we will apply this method in practise using Python.
Introduction to the Dataset14:38
On this lesson we introduce the dataset crabs.csv.
Later we will use LLE for the dimensionality reduction and visualization of this dataset.
Using LLE27:39
On this lesson we use the Locally Linear Embedding technique to reduce the dimensionality of our dataset.
We also visualize the new dimensional space with 2 components. This is a visualization in 2 dimensions.
LLE with 3 Dimensions23:38
On this lesson we use the Locally Linear Embedding technique to reduce the dimensionality of our dataset.
Now we use 3 components. At the end we do a visualization of the new dimensional space with 3 dimensions. With this, we end the practise of Locally Linear Embedding.

Introduction to t-SNE5:04
On this lesson we make a brief introduction to the t-Stochastic Neighbor Embedding dimensionality reduction technique.
Dataset0:47
On this lesson we mention that we will use the crabs.csv dataset on this section.
This is a dataset we already worked with previously. Those that are familiar with this
dataset can skip the following lesson and just go to the next one.
Introduction to the Dataset14:38
On this lesson we introduce the dataset crabs.csv.
Later we will use t-SNE for the visualization of this dataset.
t-SNE on Raw Data35:09
On this lesson we apply the t-Stochastic Neighbor Embedding technique on the original dataset of the crabs. At the end, we visualize the separation between the classes using 2 dimensions and 3 dimensions.
t-SNE on Scaled Data16:28
On this lesson we apply the t-Stochastic Neighbor Embedding technique on the scaled dataset of the crabs. At the end, we visualize the separation between the classes using 2 dimensions and 3 dimensions.
t-SNE on Standardized Data16:56
On this lesson we apply the t-Stochastic Neighbor Embedding technique on the standardized dataset of the crabs. At the end, we visualize the separation between the classes using 2 dimensions and 3 dimensions.

Introduction to MDS3:18
On this lesson we introduce the Multidimensional Scaling dimensionality reduction technique.
Using MDS with 2 Dimensions20:40
On this lesson we apply the MDS dimensionality reduction technique in the crabs dataset.
At the end, we visualize the separation between classes in 2 Dimensions.
Using MDS with 3 Dimensions19:08
On this lesson we apply the MDS dimensionality reduction technique in the crabs dataset.
At the end, we visualize the separation between classes in 3 Dimensions.

Introducción to ISOMAP2:55
On this lesson we make a brief introduction to the ISOMAP dimensionality reduction technique.
ISOMAP with 2 Dimensions28:59
On this lesson we use the ISOMAP technique to reduce the dimensionality of our dataset.
We also visualize the new dimensional space with 2 components. This is a visualization in 2 dimensions.
ISOMAP with 3 Dimensions27:45
On this lesson we use the ISOMAP technique to reduce the dimensionality of our dataset.
We also visualize the new dimensional space with 3 components. This is a visualization in 3 dimensions.

Introduction to Fisher Discriminant Analysis2:43
On this lesson we make a brief introduction to the Fisher Discriminant Analysis dimensionality reduction technique.
Dataset Information0:47
On this lesson we mention that we will use the crabs.csv dataset on this section.
This is a dataset we already worked with previously. Those that are familiar with this
dataset can skip the following lesson and just go to the next one.
Introduction to the Dataset14:38
On this lesson we introduce the dataset crabs.csv.
Later we will use Fisher Discriminant Analysis for the visualization of this dataset and the separation of the crabs.
Fisher Discriminant Analysis with 2 Dimensions27:08
On this lesson we use the Fisher Discriminant Analysis technique to reduce the dimensionality of our dataset.
We also visualize the new dimensional space with 2 components. This is a visualization in 2 dimensions.
Fisher Discriminant Analysis with 3 Dimensions23:25
On this lesson we use the Fisher Discriminant Analysis technique to reduce the dimensionality of our dataset.
We also visualize the new dimensional space with 3 components. This is a visualization in 3 dimensions.

Images0:31
On this lesson we explain that images can be transformed for use by dimensionality reduction methods by converting them to arrays of numeric values.
Introduction to the Image Dataset17:28
In this lesson we introduce the image dataset digits.
Later we will use Dimensionality Reduction techniques for the visualization of this dataset and the separation of the images.
Fisher Discriminant Analysis25:24
On this lesson we use the Fisher Discriminant Analysis technique to reduce the dimensionality of our dataset.
We visualize a new dimensional space with 2 components in 2 dimensions.
We then also visualize a new dimensional space with 3 components in 3 dimensions.
Locally Linear Embedding24:14
On this lesson we use the Locally Linear Embedding technique to reduce the dimensionality of our dataset.
We visualize a new dimensional space with 2 components in 2 dimensions.
We then also visualize a new dimensional space with 3 components in 3 dimensions.
Principal Component Analysis21:38
On this lesson we use Principal Component Analysis to reduce the dimensionality of our dataset.
We visualize a new dimensional space with 2 components in 2 dimensions.
We then also visualize a new dimensional space with 3 components in 3 dimensions.
ISOMAP20:32
On this lesson we use ISOMAP to reduce the dimensionality of our dataset.
We visualize a new dimensional space with 2 components in 2 dimensions.
We then also visualize a new dimensional space with 3 components in 3 dimensions.

Requirements

No Machine Learning experience is necessary to take this course! I take you from beginner to expert!
Any computer and OS will work — Windows, macOS or Linux. We will set up your code environment in the course.

Description

You’ve just stumbled upon the most complete, in-depth Machine Learning course online.

Whether you want to:

- build the skills you need to get your first machine learning job

- move to a more senior software developer position

- become a computer scientist mastering in machine learning

- or just learn Machine Learning to be able to create your own projects quickly.

...this complete Machine Learning Masterclass is the course you need to do all of this, and more.

This course is designed to give you the machine learning skills you need to become a machine learning expert. By the end of the course, you will understand the machine learning method extremely well and be able to apply it in your own machine learning projects and be productive as a computer scientist and developer.

What makes this course a bestseller?

Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.

Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete Machine Learning course. It’s designed with simplicity and seamless progression in mind through its content.

This course assumes no previous machine learning experience and takes you from absolute beginner core concepts. You will learn the core machine learning skills and master machine learning. It's a one-stop shop to learn machine learning. If you want to go beyond the core content you can do so at any time.

Here’s just some of what you’ll learn

(It’s okay if you don’t understand all this yet. You will in the course)

Understand the core concepts of supervised, unsupervised, and reinforcement learning
Build predictive models using popular algorithms like linear regression, decision trees, and support vector machines
Master classification and clustering techniques, including KNN, Naive Bayes, and K-means
Dive into neural networks and deep learning using frameworks like TensorFlow or PyTorch
Perform feature engineering, data cleaning, and preprocessing with real datasets
Understand model evaluation metrics like accuracy, precision, recall, F1-score, and ROC-AUC
Learn how to reduce overfitting using regularization techniques like L1/L2, dropout, and cross-validation
Apply dimensionality reduction methods like PCA and t-SNE to optimize performance
Gain practical experience through hands-on projects across domains like finance, healthcare, and marketing
Become the kind of professional who can walk into a room, explain a machine learning model clearly, and design one from scratch with confidence

What if I have questions?

As if this course wasn’t complete enough, I offer full support, answering any questions you have.

This means you’ll never find yourself stuck on one lesson for days on end. With my hand-holding guidance, you’ll progress smoothly through this course without any major roadblocks.

There’s no risk either!

This course comes with a full 30-day money-back guarantee. Meaning if you are not completely satisfied with the course or your progress, simply let me know and I’ll refund you 100%, every last penny no questions asked.

You either end up with Machine Learning skills, go on to develop great programs and potentially make an awesome career for yourself, or you try the course and simply get all your money back if you don’t like it…

You literally can’t lose.

Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

And as a bonus, this course includes Python code templates which you can download and use on your own projects.

Ready to get started, developer?

Enroll now using the “Add to Cart” button on the right, and get started on your way to creative, advanced Machine Learning brilliance. Or, take this course for a free spin using the preview feature, so you know you’re 100% certain this course is for you.

See you on the inside (hurry, Machine Learning is waiting!)

Who this course is for:

Anyone interested in Machine Learning.
Any people who have been trying to learn Machine Learning but: 1) still don't really understand it, or 2) still don't feel confident to take a job interview.
Any college students who want to start a career in Machine Learning.
Any people who want to create added value to their business by using powerful Machine Learning tools.
Anyone who wants to work as a Machine Learning Engineer in research, economics, finance, marketing, engineering, or healthcare sectors.

The Complete Machine Learning Course: From Zero to Expert!

What you'll learn

Explore related topics

Course content

Code Environment Setup1 lecture • 3min

Machine Learning Fundamentals3 lectures • 13min

Introduction - Preprocessing and Analysis2 lectures • 40min

Visualization - Principal Component Analysis4 lectures • 2hr 3min

Visualization - Locally Linear Embedding (LLE)5 lectures • 1hr 13min

Visualization - t-Stochastic Neighbor Embedding (t-SNE)6 lectures • 1hr 29min

Visualization - Multidimensional Scaling (MDS)3 lectures • 43min

Visualization - ISOMAP3 lectures • 1hr

Visualization - Fisher Discriminant Analysis5 lectures • 1hr 9min

Visualization Final Project - Images6 lectures • 1hr 50min

Requirements

Description

Who this course is for: