The Complete Machine Learning Course: From Zero to Expert!

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

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Lucas Bazilio

Phụ đề Việt chất lượng cao Phụ đề tiếng Anh Bán chạy Đánh giá cao

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26.5 giờ video

104 bài giảng

Truy cập vĩnh viễn

Học trên mọi thiết bị

Cập nhật: 28/05/2026

#Data Science #Development #Machine #Machine Learning

Bạn sẽ học được gì

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).

Yêu cầu

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.

Đối tượng phù hợp

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.

Nội dung khóa học

23 phần • 97 bài giảng

1 Google Colab for Programming in Python

1 Introduction to Machine Learning

2 Supervised Learning

3 Unsupervised Learning

1 Initial Study of the Dataset

2 Basic Visualization

1 Introduction to PCA

2 Introduction to the Dataset

3 Initial Visualization

4 Using PCA

1 Introduction to LLE

2 Locally Linear Embedding Algorithm

3 Introduction to the Dataset

4 Using LLE

5 LLE with 3 Dimensions

1 Introduction to t-SNE

2 Dataset

3 Introduction to the Dataset

4 t-SNE on Raw Data

5 t-SNE on Scaled Data

6 t-SNE on Standardized Data

1 Introduction to MDS

2 Using MDS with 2 Dimensions

3 Using MDS with 3 Dimensions

1 Introducción to ISOMAP

2 ISOMAP with 2 Dimensions

3 ISOMAP with 3 Dimensions

1 Introduction to Fisher Discriminant Analysis

2 Dataset Information

3 Introduction to the Dataset

4 Fisher Discriminant Analysis with 2 Dimensions

5 Fisher Discriminant Analysis with 3 Dimensions

1 Images

2 Introduction to the Image Dataset

3 Fisher Discriminant Analysis

4 Locally Linear Embedding

5 Principal Component Analysis

6 ISOMAP

1 Introduction to the Dataset

2 Preprocessing

3 Linear Regression

4 Metrics

5 Cross Validation

1 Ridge Regression and Cross Validation

1 Lasso Regression and Cross Validation

1 Analysis

2 Data Scaling

3 One-Hot Encoding

4 Regularization

5 Final Results

1 Introduction to the Dataset

2 Partition of the Dataset: Train and Test

3 Preprocessing

4 Principal Component Analysis

5 Linear Discriminant Analysis

6 Naive Bayes Classifier

7 Quadratic Classifier

8 Logistic Regression

1 Introduction to Support Vector Machines

2 Introduction to the Dataset

3 Partition of the Dataset - Target Variable

4 Partition of the Dataset - Time Series Windows

5 Support Vector Machine - Linear Kernel

6 Support Vector Machines - Polynomial Kernels

7 Support Vector Machine - Radial Basis Function (RBF) Kernel

1 Introduction to Support Vector Machines

2 Introduction to the Dataset

3 Partition of the Dataset

4 Transformation to Data Matrix

5 Dimensionality Reduction

6 Support Vector Machine - Linear Kernel

7 Support Vector Machines - Polynomial Kernels

8 Support Vector Machine - Radial Basis Function (RBF) Kernel

1 Introduction to Neural Networks

1 Dataset Information

2 Introduction to the Dataset

3 Partition of the Dataset - Target Variable

4 Partition of the Dataset - Time Series Windows

5 Multilayer Perceptron Neural Network

1 Introduction to the Dataset

2 Partition of the Dataset

3 Neural Network - Preprocessing

4 Neural Network - Training

5 Neural Network - Performance Evaluation

1 Introduction to K-Nearest Neighbors

1 Dataset Information

2 Introduction to the Dataset

3 Partition of the Dataset - Target Variable

4 Partition of the Dataset - Time Series Windows

5 KNN - Training

6 KNN - Performance Evaluation

1 Dataset Information

2 Electronic Devices Dataset

3 Partition of the Dataset

4 KNN - Preprocessing

5 KNN - Training

6 KNN - Performance Evaluation

Giới thiệu khóa học

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!)