Data Science


Complete Machine Learning & Data Science Bootcamp 2021

Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!

4.67 (6705 reviews)



43 hours


Apr 2021

Last Update
Regular Price

What you will learn

Become a Data Scientist and get hired

Master Machine Learning and use it on the job

Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0

Use modern tools that big tech companies like Google, Apple, Amazon and Facebook use

Present Data Science projects to management and stakeholders

Learn which Machine Learning model to choose for each type of problem

Real life case studies and projects to understand how things are done in the real world

Learn best practices when it comes to Data Science Workflow

Implement Machine Learning algorithms

Learn how to program in Python using the latest Python 3

How to improve your Machine Learning Models

Learn to pre process data, clean data, and analyze large data.

Build a portfolio of work to have on your resume

Developer Environment setup for Data Science and Machine Learning

Supervised and Unsupervised Learning

Machine Learning on Time Series data

Explore large datasets using data visualization tools like Matplotlib and Seaborn

Explore large datasets and wrangle data using Pandas

Learn NumPy and how it is used in Machine Learning

A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided

Learn to use the popular library Scikit-learn in your projects

Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry

Learn to perform Classification and Regression modelling

Learn how to apply Transfer Learning


This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, + other top tech companies. You will go from zero to mastery!

Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).

This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.

The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don't worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!

The topics covered in this course are:

- Data Exploration and Visualizations

- Neural Networks and Deep Learning

- Model Evaluation and Analysis

- Python 3

- Tensorflow 2.0

- Numpy

- Scikit-Learn

- Data Science and Machine Learning Projects and Workflows

- Data Visualization in Python with MatPlotLib and Seaborn

- Transfer Learning

- Image recognition and classification

- Train/Test and cross validation

- Supervised Learning: Classification, Regression and Time Series

- Decision Trees and Random Forests

- Ensemble Learning

- Hyperparameter Tuning

- Using Pandas Data Frames to solve complex tasks

- Use Pandas to handle CSV Files

- Deep Learning / Neural Networks with TensorFlow 2.0 and Keras

- Using Kaggle and entering Machine Learning competitions

- How to present your findings and impress your boss

- How to clean and prepare your data for analysis

- K Nearest Neighbours

- Support Vector Machines

- Regression analysis (Linear Regression/Polynomial Regression)

- How Hadoop, Apache Spark, Kafka, and Apache Flink are used

- Setting up your environment with Conda, MiniConda, and Jupyter Notebooks

- Using GPUs with Google Colab

By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.

Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don't really explain things well enough for you to go off on your own and solve real life machine learning problems.

Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows.

Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.

You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!

Click “Enroll Now” and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course!

Taught By:

Daniel Bourke:
A self-taught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages.

My experience in machine learning comes from working at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen.

I've worked on machine learning and data problems across a wide range of industries including healthcare, eCommerce, finance, retail and more.

Two of my favourite projects include building a machine learning model to extract information from doctors notes for one of Australia's leading medical research facilities, as well as building a natural language model to assess insurance claims for one of Australia's largest insurance groups.

Due to the performance of the natural language model (a model which reads insurance claims and decides which party is at fault), the insurance company were able to reduce their daily assessment load by up to 2,500 claims.

My long-term goal is to combine my knowledge of machine learning and my background in nutrition to work towards answering the question "what should I eat?".

Aside from building machine learning models on my own, I love writing about and making videos on the process. My articles and videos on machine learning on Medium, personal blog and YouTube have collectively received over 5-million views.

I love nothing more than a complicated topic explained in an entertaining and educative matter. I know what it's like to try and learn a new topic, online and on your own. So I pour my soul into making sure my creations are accessible as possible.

My modus operandi (a fancy term for my way of doing things) is learning to create and creating to learn. If you know the Japanese word for this concept, please let me know.

Questions are always welcome.


Andrei Neagoie:
Andrei is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc... He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life. 

Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time.   Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities. 

Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way. 

Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible.  

See you inside the course!


Complete Machine Learning & Data Science Bootcamp 2021
Complete Machine Learning & Data Science Bootcamp 2021
Complete Machine Learning & Data Science Bootcamp 2021
Complete Machine Learning & Data Science Bootcamp 2021



Course Outline

Join Our Online Classroom!

Exercise: Meet The Community

Your First Day

Machine Learning 101

What Is Machine Learning?

AI/Machine Learning/Data Science

Exercise: Machine Learning Playground

How Did We Get Here?

Exercise: YouTube Recommendation Engine

Types of Machine Learning

Are You Getting It Yet?

What Is Machine Learning? Round 2

Section Review

Machine Learning and Data Science Framework

Section Overview

Introducing Our Framework

6 Step Machine Learning Framework

Types of Machine Learning Problems

Types of Data

Types of Evaluation

Features In Data

Modelling - Splitting Data

Modelling - Picking the Model

Modelling - Tuning

Modelling - Comparison


Tools We Will Use

Optional: Elements of AI

The 2 Paths

The 2 Paths

Python + Machine Learning Monthly

Data Science Environment Setup

Section Overview

Introducing Our Tools

What is Conda?

Conda Environments

Mac Environment Setup

Mac Environment Setup 2

Windows Environment Setup

Windows Environment Setup 2

Linux Environment Setup

Sharing your Conda Environment

Jupyter Notebook Walkthrough

Jupyter Notebook Walkthrough 2

Jupyter Notebook Walkthrough 3

Pandas: Data Analysis

Section Overview

Downloading Workbooks and Assignments

Pandas Introduction

Series, Data Frames and CSVs

Data from URLs

Describing Data with Pandas

Selecting and Viewing Data with Pandas

Selecting and Viewing Data with Pandas Part 2

Manipulating Data

Manipulating Data 2

Manipulating Data 3

Assignment: Pandas Practice

How To Download The Course Assignments


Section Overview

NumPy Introduction

Quick Note: Correction In Next Video

NumPy DataTypes and Attributes

Creating NumPy Arrays

NumPy Random Seed

Viewing Arrays and Matrices

Manipulating Arrays

Manipulating Arrays 2

Standard Deviation and Variance

Reshape and Transpose

Dot Product vs Element Wise

Exercise: Nut Butter Store Sales

Comparison Operators

Sorting Arrays

Turn Images Into NumPy Arrays

Assignment: NumPy Practice

Optional: Extra NumPy resources

Matplotlib: Plotting and Data Visualization

Section Overview

Matplotlib Introduction

Importing And Using Matplotlib

Anatomy Of A Matplotlib Figure

Scatter Plot And Bar Plot

Histograms And Subplots

Subplots Option 2

Quick Tip: Data Visualizations

Plotting From Pandas DataFrames

Quick Note: Regular Expressions

Plotting From Pandas DataFrames 2

Plotting from Pandas DataFrames 3

Plotting from Pandas DataFrames 4

Plotting from Pandas DataFrames 5

Plotting from Pandas DataFrames 6

Plotting from Pandas DataFrames 7

Customizing Your Plots

Customizing Your Plots 2

Saving And Sharing Your Plots

Assignment: Matplotlib Practice

Scikit-learn: Creating Machine Learning Models

Section Overview

Scikit-learn Introduction

Quick Note: Upcoming Video

Refresher: What Is Machine Learning?

Quick Note: Upcoming Videos

Scikit-learn Cheatsheet

Typical scikit-learn Workflow

Optional: Debugging Warnings In Jupyter

Getting Your Data Ready: Splitting Your Data

Quick Tip: Clean, Transform, Reduce

Getting Your Data Ready: Convert Data To Numbers

Getting Your Data Ready: Handling Missing Values With Pandas

Note: Correction in the upcoming video

Getting Your Data Ready: Handling Missing Values With Scikit-learn

Choosing The Right Model For Your Data

Choosing The Right Model For Your Data 2 (Regression)

Quick Note: Decision Trees

Quick Tip: How ML Algorithms Work

Choosing The Right Model For Your Data 3 (Classification)

Fitting A Model To The Data

Making Predictions With Our Model

predict() vs predict_proba()

Making Predictions With Our Model (Regression)

Evaluating A Machine Learning Model (Score)

Evaluating A Machine Learning Model 2 (Cross Validation)

Evaluating A Classification Model 1 (Accuracy)

Evaluating A Classification Model 2 (ROC Curve)

Evaluating A Classification Model 3 (ROC Curve)

Evaluating A Classification Model 4 (Confusion Matrix)

Evaluating A Classification Model 5 (Confusion Matrix)

Evaluating A Classification Model 6 (Classification Report)

Evaluating A Regression Model 1 (R2 Score)

Evaluating A Regression Model 2 (MAE)

Evaluating A Regression Model 3 (MSE)

Machine Learning Model Evaluation

Evaluating A Model With Cross Validation and Scoring Parameter

Evaluating A Model With Scikit-learn Functions

Improving A Machine Learning Model

Tuning Hyperparameters

Tuning Hyperparameters 2

Tuning Hyperparameters 3

Quick Tip: Correlation Analysis

Saving And Loading A Model

Saving And Loading A Model 2

Putting It All Together

Putting It All Together 2

Scikit-Learn Practice

Supervised Learning: Classification + Regression

Milestone Projects!

Milestone Project 1: Supervised Learning (Classification)

Section Overview

Project Overview

Project Environment Setup

Step 1~4 Framework Setup

Getting Our Tools Ready

Exploring Our Data

Finding Patterns

Finding Patterns 2

Finding Patterns 3

Preparing Our Data For Machine Learning

Choosing The Right Models

Experimenting With Machine Learning Models

Tuning/Improving Our Model

Tuning Hyperparameters

Tuning Hyperparameters 2

Tuning Hyperparameters 3

Evaluating Our Model

Evaluating Our Model 2

Evaluating Our Model 3

Finding The Most Important Features

Reviewing The Project

Milestone Project 2: Supervised Learning (Time Series Data)

Section Overview

Project Overview

Project Environment Setup

Step 1~4 Framework Setup

Exploring Our Data

Exploring Our Data 2

Feature Engineering

Turning Data Into Numbers

Filling Missing Numerical Values

Filling Missing Categorical Values

Fitting A Machine Learning Model

Splitting Data

Custom Evaluation Function

Reducing Data


Improving Hyperparameters

Preproccessing Our Data

Making Predictions

Feature Importance

Data Engineering

Data Engineering Introduction

What Is Data?

What Is A Data Engineer?

What Is A Data Engineer 2?

What Is A Data Engineer 3?

What Is A Data Engineer 4?

Types Of Databases

Quick Note: Upcoming Video

Optional: OLTP Databases

Optional: Learn SQL

Hadoop, HDFS and MapReduce

Apache Spark and Apache Flink

Kafka and Stream Processing

Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2

Section Overview

Deep Learning and Unstructured Data

Setting Up With Google

Setting Up Google Colab

Google Colab Workspace

Uploading Project Data

Setting Up Our Data

Setting Up Our Data 2

Importing TensorFlow 2

Optional: TensorFlow 2.0 Default Issue

Using A GPU

Optional: GPU and Google Colab

Optional: Reloading Colab Notebook

Loading Our Data Labels

Preparing The Images

Turning Data Labels Into Numbers

Creating Our Own Validation Set

Preprocess Images

Preprocess Images 2

Turning Data Into Batches

Turning Data Into Batches 2

Visualizing Our Data

Preparing Our Inputs and Outputs

Optional: How machines learn and what's going on behind the scenes?

Building A Deep Learning Model

Building A Deep Learning Model 2

Building A Deep Learning Model 3

Building A Deep Learning Model 4

Summarizing Our Model

Evaluating Our Model

Preventing Overfitting

Training Your Deep Neural Network

Evaluating Performance With TensorBoard

Make And Transform Predictions

Transform Predictions To Text

Visualizing Model Predictions

Visualizing And Evaluate Model Predictions 2

Visualizing And Evaluate Model Predictions 3

Saving And Loading A Trained Model

Training Model On Full Dataset

Making Predictions On Test Images

Submitting Model to Kaggle

Making Predictions On Our Images

Finishing Dog Vision: Where to next?

Storytelling + Communication: How To Present Your Work

Section Overview

Communicating Your Work

Communicating With Managers

Communicating With Co-Workers

Weekend Project Principle

Communicating With Outside World


Communicating and sharing your work: Further reading

Career Advice + Extra Bits

Endorsements On LinkedIn

Quick Note: Upcoming Video

What If I Don't Have Enough Experience?

Learning Guideline

Quick Note: Upcoming Videos

JTS: Learn to Learn

JTS: Start With Why

Quick Note: Upcoming Videos

CWD: Git + Github

CWD: Git + Github 2

Contributing To Open Source

Contributing To Open Source 2

Coding Challenges

Exercise: Contribute To Open Source

Learn Python

What Is A Programming Language

Python Interpreter

How To Run Python Code

Our First Python Program

Python 2 vs Python 3

Exercise: How Does Python Work?

Learning Python

Python Data Types

How To Succeed


Math Functions


Operator Precedence

Exercise: Operator Precedence

Optional: bin() and complex


Expressions vs Statements

Augmented Assignment Operator


String Concatenation

Type Conversion

Escape Sequences

Formatted Strings

String Indexes


Built-In Functions + Methods


Exercise: Type Conversion


Exercise: Password Checker


List Slicing


List Methods

List Methods 2

List Methods 3

Common List Patterns

List Unpacking




Dictionary Keys

Dictionary Methods

Dictionary Methods 2


Tuples 2


Sets 2

Learn Python Part 2

Breaking The Flow

Conditional Logic

Indentation In Python

Truthy vs Falsey

Ternary Operator

Short Circuiting

Logical Operators

Exercise: Logical Operators

is vs ==

For Loops


Exercise: Tricky Counter



While Loops

While Loops 2

break, continue, pass

Our First GUI


Exercise: Find Duplicates


Parameters and Arguments

Default Parameters and Keyword Arguments


Exercise: Tesla

Methods vs Functions


Clean Code

*args and **kwargs

Exercise: Functions


Scope Rules

global Keyword

nonlocal Keyword

Why Do We Need Scope?

Pure Functions





List Comprehensions

Set Comprehensions

Exercise: Comprehensions

Python Exam: Testing Your Understanding

Modules in Python

Quick Note: Upcoming Videos

Optional: PyCharm

Packages in Python

Different Ways To Import

Next Steps

Bonus: Learn Advanced Statistics and Mathematics for FREE!

Statistics and Mathematics

Where To Go From Here?

Become An Alumni

Thank You


Bonus: Special Thank You Gift


Emily2 October 2020

All of Andrei's courses are the best! He is so personable and engaging. I would recommend this to everyone.

Roberto1 October 2020

As a developer I'm a bit bored as things are getting repeated too often. But I understand the point to go slow on it for non-devs who are watching this. Currently watching on x1.5 speed.

Assaf29 September 2020

The online community is really nice thing and quite unique. The way the course is outlined seems logical and interesting

Matthew28 September 2020

Best course i have taken so far on Udemy. Instructor knows what is doing and explains in a way that is easy to follow and reproduce.

Casimiro26 September 2020

Very good course. Very well organized and the topics very well explained with all the resources available. I learned a lot about Machine Learning. Thanks.

Gerardo26 February 2020

I totally recommend this course, the way it is designed, the tools that are given and the passion shown and delivered to the students made me rediscover my love for science, Both instructors are accesible and truly faciliate the learning of these topics. I would like to have a different example of Classification problem since we have worked with that one from the begining and address a unsupervised problem too. Thank you Daniel! Thank you Andrei!

Etienne26 February 2020

This course offers a hands-on approach to learning Data Science concepts and Machine Learning techniques. Additionally, a large chunk of the content is additional to the "typical ML curriculum" and I really appreciated the thought and foresight to include these topics. Finally, the established ZTM community makes it easy to interact (often not the case with other courses) and ultimately makes this course invaluable for anyone, at any technical level.

Faizan24 February 2020

The instructor explains code by code. The instructor also codes along with you which is very helpful. Real life mimicking projects are used to trach which is a bonus. Will recommend this course to anybody who wants to learn data science.

Deviprasad23 February 2020

The narrator is really nice , teaches everything in most simplified way , I really loved it . The course matches my expectations so far.

Kashyap23 February 2020

Exceptionally good. Loved every bit of it. I know its bit much to ask but would help a lot if you could add explanations about the algorithms behind it too.

Pratesh23 February 2020

It's been two days starting this course, stick to the plan which is made my Andrei and Daniel. This course covers all important libraries and modules, which are very demanding in today's date eg. Numpy, Pandas, Scikit-Learn etc. just go through the Overview of this course and I am sure you guys will really get surprised . Have it a try if you guys are not satisfied with the course then you have a good option i.e Money back Guaranty lol. Keep coding.

Mario21 February 2020

I like the way it explains things and I think is gonna be very important for my development as a future data scientist

Sean20 February 2020

So far, it's well explained and the structure of the course seems like it will lend itself well to getting familiar with concepts. I'm only 3 videos in, though, so it's hard to say exactly how much I like it yet.

Godnon20 February 2020

This is the best machine learning course. Both Andrei and Daniel are top tier instructors. Totally worth buying this

Yeswanth18 February 2020

Its an wonderful experience for me to be a part of this course, the way of explanation is too good and more interactive.


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