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Complete Machine Learning and Data Science Bootcamp

One of the most popular, highly rated machine learning and data science bootcamps online. It's also the most moderen and up-to-date. Guaranteed. You'll go from complete beginner with no prior experience to getting hired as a Machine Learning Engineer this year.

You'll learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence), Python, Python with Tensorflow, Pandas & more!

Last updated: March 2023

Course overview

Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way. We are confident that this is the most comprehensive, modern and up-to-date course you will find on Data Science & Machine Learning (bold statement, we know). Fully updated for 2023, we use the latest version of Python, Tensorflow 2.0, and all the latest industry skills and trends!

What you'll learn

  • Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
  • Present Data Science projects to management and stakeholders
  • Real life case studies and projects to understand how things are done in the real world
  • Implement Machine Learning algorithms
  • How to improve your Machine Learning models
  • Build a portfolio of work to have on your resume
  • Supervised and Unsupervised Learning
  • Explore large datasets using data visualization tools like Matplotlib and Seaborn
  • Learn NumPy and how it is used in Machine Learning
  • Learn to use the popular library Scikit-learn in your projects
  • Master Machine Learning and how use it on the job
  • Use modern tools that big tech companies like Google, Apple, Amazon and Facebook use
  • Learn which Machine Learning model to choose for each type of problem
  • Learn best practices when it comes to Data Science workflow
  • Learn how to program in Python using the latest Python 3
  • Learn to pre-process data, clean data, and analyze large data
  • Developer Environment setup for Data Science and Machine Learning
  • Machine Learning on Time Series data
  • Explore large datasets and wrangle data using Pandas
  • A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
  • Learn about Data Engineering and how tools like Hadoop, Spark, and Kafka are used in the industry
  • Learn how to apply Transfer Learning
  • Learn to perform Classification and Regression modelling

Machine Learning has applications in almost every industry and is becoming more widely used every single year.

  • Marketing
  • Finance
  • Healthcare and Patient Diagnosis
  • Cybersecurity
  • Retail
  • Transportation and Logistics
  • Agriculture
  • Internet of Things
  • Gaming and Entertainment
  • Fraud Detection
  • Anomaly Detection in Manufacturing
  • Government
  • Academia/Research
  • Recommendation Systems
  • Sports (every heard of Moneyball?!)

And so many more. It's everywhere. The skills you'll learn in this course can be applied to all of these industries and give you a lot of options for your career.

Graduates of Zero To Mastery are now working at Google, Tesla, Amazon, Apple, IBM, Uber, Facebook, Shopify + other top tech companies.

They are also working as top freelancers getting paid while working remotely around the world.

This can be you.

By enrolling today, you’ll also get to join our exclusive live online community classroom to learn alongside thousands of students, alumni, mentors, TAs and Instructors.

Most importantly, you will learn data science and machine learning from industry experts that have actual real-world experience having worked for top companies in Silicon Valley, Toronto and Australia.

This course is focused on efficiency so that you never have to waste your time on confusing, out of date or incomplete Machine Learning tutorials anymore.

This comprehensive and project-based course will introduce you to all of the modern skills of a Data Scientist and along the way, you will build many real-world projects to add to your portfolio.

You will also get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away!

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 are actually looking for.

The curriculum is very hands-on as we walk you from start to finish of becoming a professional Machine Learning Engineer or Data Scientist.

The course covers 2 tracks:

1️⃣ Programmer: If you already know programming, you can dive right in and skip the section where we teach you Python from scratch.

2️⃣ No Programming Experience: If you're a complete beginner, not to worry. We take you from the very beginning and teach you Python from scratch and how to use it in our real world projects.

Don't worry, you will also be going way beyond the basics.

Once we make sure you know the basics like Machine Learning 101 and Python, we dive deep 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.

You will get experience with full-fledged Data Science and Machine Learning projects and access to bonus programming resources and cheatsheets.

The topics you'll learn in this Data Science & Machine Learning Bootcamp 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
  • and more!

By the end of this course, you will be a complete Data Scientist that can get hired at any company you can imagine.

You are going to use everything you learn in this course to build professional real-world projects like a Heart Disease Detection App, 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.

What's the bottom line?

Here’s the truth: most courses teach you Data Science and that's it.

They show you how to get started, but then you don’t know where to go from there or how to build your own projects.

Or, they'll 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, 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, and get hired 💪.

And you have nothing to lose. You can start learning right now and if this course isn't everything you expected, we'll refund you 100% within 30 days. No hassles and no questions asked.

When's the best time to get started? Today!

There's never a bad time to learn in-demand skills. But the sooner, the better. So start learning data science & machine learning today by joining the ZTM Academy. You'll have a clear roadmap to developing the skills to build your own projects, get hired, and advance your career.

Start learning now
Expand Overview

What you'll build

The best way you learn is by doing. Not just watching endless tutorials. That's why a key part of this course is the real-world projects that you'll get to build. Plus they'll look great on your portfolio.

Heart Disease Detector

Heart Disease Detector

Learn how to build a machine learning-powered classification model to predict whether a patient has heart disease or not based on their health parameters and heart measurements.

Bulldozer Predictor Model

Bulldozer Predictor Model

Take into account the previous sales history of bulldozers to build a machine learning-powered regression model to answer the question, "how much is that bulldozer worth?"

Dog Vision

Dog Vision

Harness the power of computer vision neural networks to build an image classification model capable of predicting over 100 different dog breeds. You'll even be able to try it on your own dog images!

start learning now

Don't just take our word for it

Our courses and community have helped 1,000s of Zero To Mastery students go from zero to getting hired to levelling up their skills and advancing their careers to new heights.

Erik Bustos

Andrei's courses are by far the best resources online. I've done 4 of his courses and I can say that he is one of the best teachers. This course is well done, exciting and fun! I highly recommend it.

Erik Bustos

Kevin Okinedo

The course is structured and beginner-friendly. Rather than wasting time on theory, it goes in-depth on the practical application of machine learning then gives you the theory later. If you are looking to become employable in the field, look no further.

Kevin Okinedo

This course is amazing! I’ve learned so much from a practical view and even if you don’t have much knowledge in math/programming, it’s still very approachable. Andrei and Daniel don’t disappoint, it’s worth every penny!!

Jan Montalvo

One of the best DS/ML courses! Often times when you want to learn DS/ML, the field is so vast that you get overwhelmed and confused about where to even begin. But the flow of this course takes all that confusion away and you indeed go from Zero to Mastery.

Luther

Very nice course: well structured & detailed. The explanations are clear & move you step-by-step through the different topics. Andrei & Daniel did really a good job. Thank you so much guys!

Mohamed Benosman

Andrei & Daniel do an excellent job of framing everything in an understandable way. I love Andrei's way of teaching within the context of learning "on the job" through a fictional company and providing expectations of what it's like to work in the field.

Shanay Murdock

Course curriculum

To make sure this course is a good fit for you, you can start learning machine learning and data science for free right now by clicking any of the PREVIEW links below.

Introduction

5 lectures

Complete Machine Learning and Data Science: Zero to Mastery4:10

PREVIEW

Course Outline5:59

PREVIEW

Exercise: Meet Your Classmates and Instructor

START

Your First Day3:48

PREVIEW

How-to's: Speed up videos, Downloading videos, Subtitles

START

Machine Learning 101

10 lectures

What Is Machine Learning?6:52

PREVIEW

AI/Machine Learning/Data Science4:51

PREVIEW

Exercise: Machine Learning Playground6:16

PREVIEW

How Did We Get Here?6:03

PREVIEW

Exercise: YouTube Recommendation Engine4:24

PREVIEW

Types of Machine Learning4:41

PREVIEW

Are You Getting It Yet?

PREVIEW

What Is Machine Learning? Round 24:44

PREVIEW

Section Review1:48

PREVIEW

Unlimited Updates

START

Machine Learning and Data Science Framework

15 lectures

Section Overview3:08

START

Introducing Our Framework2:38

START

6 Step Machine Learning Framework4:59

PREVIEW

Types of Machine Learning Problems10:32

START

Types of Data4:50

START

Types of Evaluation3:31

START

Features In Data5:22

START

Modelling - Splitting Data5:58

START

Modelling - Picking the Model4:35

START

Modelling - Tuning3:17

START

Modelling - Comparison9:32

START

Overfitting and Underfitting Definitions

START

Experimentation3:35

START

Tools We Will Use3:59

START

Optional: Elements of AI

START

The 2 Paths

3 lectures

The 2 Paths3:27

START

Python + Machine Learning Monthly

START

Endorsements On LinkedIn

START

Data Science Environment Setup

13 lectures

Section Overview1:09

START

Introducing Our Tools3:28

PREVIEW

What is Conda?2:35

START

Conda Environments4:30

START

Mac Environment Setup17:26

START

Mac Environment Setup 214:11

START

Windows Environment Setup5:17

START

Windows Environment Setup 223:17

START

Linux Environment Setup

START

Sharing your Conda Environment

START

Jupyter Notebook Walkthrough10:20

START

Jupyter Notebook Walkthrough 216:17

START

Jupyter Notebook Walkthrough 38:10

START

Pandas: Data Analysis

13 lectures

Section Overview2:27

START

Downloading Workbooks and Assignments

START

Pandas Introduction4:29

START

Series, Data Frames and CSVs13:21

START

Data from URLs

START

Describing Data with Pandas9:48

START

Selecting and Viewing Data with Pandas11:08

START

Selecting and Viewing Data with Pandas Part 213:06

START

Manipulating Data13:56

START

Manipulating Data 29:56

START

Manipulating Data 310:12

START

Assignment: Pandas Practice

START

How To Download The Course Assignments7:43

START

NumPy

18 lectures

Section Overview2:40

START

NumPy Introduction5:17

START

Quick Note: Correction In Next Video

START

NumPy DataTypes and Attributes14:05

START

Creating NumPy Arrays9:22

START

NumPy Random Seed7:17

START

Viewing Arrays and Matrices9:35

START

Manipulating Arrays11:31

START

Manipulating Arrays 29:44

START

Standard Deviation and Variance7:10

START

Reshape and Transpose7:26

START

Dot Product vs Element Wise11:45

START

Exercise: Nut Butter Store Sales13:04

START

Comparison Operators3:33

START

Sorting Arrays6:19

START

Turn Images Into NumPy Arrays7:37

START

Assignment: NumPy Practice

START

Optional: Extra NumPy resources

START

Matplotlib: Plotting and Data Visualization

20 lectures

Section Overview1:50

START

Matplotlib Introduction5:16

START

Importing And Using Matplotlib11:36

START

Anatomy Of A Matplotlib Figure9:19

START

Scatter Plot And Bar Plot10:09

START

Histograms And Subplots8:40

START

Subplots Option 24:15

START

Quick Tip: Data Visualizations1:48

START

Plotting From Pandas DataFrames5:58

START

Quick Note: Regular Expressions

START

Plotting From Pandas DataFrames 210:33

START

Plotting from Pandas DataFrames 38:32

START

Plotting from Pandas DataFrames 46:36

START

Plotting from Pandas DataFrames 58:28

START

Plotting from Pandas DataFrames 68:27

START

Plotting from Pandas DataFrames 711:20

START

Customizing Your Plots10:09

START

Customizing Your Plots 29:41

PREVIEW

Saving And Sharing Your Plots4:14

START

Assignment: Matplotlib Practice

START

Scikit-learn: Creating Machine Learning Models

52 lectures

Section Overview2:29

START

Scikit-learn Introduction6:41

START

Quick Note: Upcoming Video

START

Refresher: What Is Machine Learning?5:40

START

Quick Note: Upcoming Videos

START

Scikit-learn Cheatsheet6:12

PREVIEW

Typical scikit-learn Workflow23:14

START

Optional: Debugging Warnings In Jupyter18:57

START

Getting Your Data Ready: Splitting Your Data8:37

START

Quick Tip: Clean, Transform, Reduce5:03

START

Getting Your Data Ready: Convert Data To Numbers16:54

START

Note: Update to next video (OneHotEncoder can handle NaN/None values)

START

Getting Your Data Ready: Handling Missing Values With Pandas12:22

START

Extension: Feature Scaling

START

Note: Correction in the upcoming video

START

Getting Your Data Ready: Handling Missing Values With Scikit-learn17:29

START

NEW: Choosing The Right Model For Your Data20:14

START

NEW: Choosing The Right Model For Your Data 2 (Regression)11:21

START

Quick Note: Decision Trees

START

Quick Tip: How ML Algorithms Work1:25

START

Choosing The Right Model For Your Data 3 (Classification)12:45

START

Fitting A Model To The Data6:45

START

Making Predictions With Our Model8:24

START

predict() vs predict_proba()8:33

START

NEW: Making Predictions With Our Model (Regression)8:48

START

NEW: Evaluating A Machine Learning Model (Score) Part 19:41

START

NEW: Evaluating A Machine Learning Model (Score) Part 26:47

START

Evaluating A Machine Learning Model 2 (Cross Validation)13:16

START

Evaluating A Classification Model 1 (Accuracy)4:46

START

Evaluating A Classification Model 2 (ROC Curve)9:04

START

Evaluating A Classification Model 3 (ROC Curve)7:44

START

Reading Extension: ROC Curve + AUC

START

Evaluating A Classification Model 4 (Confusion Matrix)11:01

START

NEW: Evaluating A Classification Model 5 (Confusion Matrix)14:22

START

Evaluating A Classification Model 6 (Classification Report)10:16

START

NEW: Evaluating A Regression Model 1 (R2 Score)9:59

START

NEW: Evaluating A Regression Model 2 (MAE)7:22

START

NEW: Evaluating A Regression Model 3 (MSE)9:49

START

Machine Learning Model Evaluation

START

NEW: Evaluating A Model With Cross Validation and Scoring Parameter25:19

START

NEW: Evaluating A Model With Scikit-learn Functions14:01

START

Improving A Machine Learning Model11:16

START

Tuning Hyperparameters23:15

START

Tuning Hyperparameters 214:23

START

Tuning Hyperparameters 314:59

START

Note: Metric Comparison Improvement

START

Quick Tip: Correlation Analysis2:28

START

Saving And Loading A Model7:28

START

Saving And Loading A Model 26:20

START

Putting It All Together20:19

START

Putting It All Together 211:34

START

Scikit-Learn Practice

START

Milestone Project 1: Supervised Learning (Classification)

23 lectures

Section Overview2:09

START

Project Overview6:09

START

Project Environment Setup10:58

START

Step 1~4 Framework Setup12:06

START

Getting Our Tools Ready9:04

START

Exploring Our Data8:33

START

Finding Patterns10:02

START

Finding Patterns 216:47

START

Finding Patterns 313:36

START

Preparing Our Data For Machine Learning8:51

START

Choosing The Right Models10:15

START

Experimenting With Machine Learning Models6:31

START

Tuning/Improving Our Model13:49

PREVIEW

Tuning Hyperparameters11:27

START

Tuning Hyperparameters 211:49

START

Tuning Hyperparameters 37:06

START

Quick Note: Confusion Matrix Labels

START

Evaluating Our Model10:59

START

Evaluating Our Model 25:54

START

Evaluating Our Model 38:49

START

Finding The Most Important Features16:07

START

Reviewing The Project9:13

START

Exercise: Imposter Syndrome2:55

START

Milestone Project 2: Supervised Learning (Time Series Data)

21 lectures

Section Overview1:07

START

Project Overview4:24

START

Downloading the data for the next two projects

START

Project Environment Setup10:52

START

Step 1~4 Framework Setup8:36

START

Exploring Our Data14:16

START

Exploring Our Data 26:16

START

Feature Engineering15:24

START

Turning Data Into Numbers15:38

START

Filling Missing Numerical Values12:49

START

Filling Missing Categorical Values8:27

START

Fitting A Machine Learning Model7:16

START

Splitting Data10:00

START

Challenge: What's wrong with splitting data after filling it?

START

Custom Evaluation Function11:13

START

Reducing Data10:36

START

RandomizedSearchCV9:32

START

Improving Hyperparameters8:11

START

Preproccessing Our Data13:15

START

Making Predictions9:17

START

Feature Importance13:50

START

Data Engineering

13 lectures

Data Engineering Introduction3:23

START

What Is Data?6:42

START

What is a Data Engineer?4:20

START

What is A Data Engineer 2?5:35

PREVIEW

What is a Data Engineer 3?5:03

START

What is a Data Engineer 4?3:22

START

Types of Databases6:50

START

Quick Note: Upcoming Video

START

Optional: OLTP Databases10:54

START

Optional: Learn SQL

START

Hadoop, HDFS and MapReduce4:22

START

Apache Spark and Apache Flink2:07

START

Kafka and Stream Processing4:33

START

Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2

44 lectures

Section Overview2:06

START

Deep Learning and Unstructured Data13:36

START

Setting Up With Google

START

Setting Up Google Colab7:17

START

Google Colab Workspace4:23

START

Uploading Project Data6:52

START

Setting Up Our Data4:40

START

Setting Up Our Data 21:32

START

Importing TensorFlow 212:43

START

Optional: TensorFlow 2.0 Default Issue3:38

START

Using A GPU8:59

START

Optional: GPU and Google Colab4:27

START

Optional: Reloading Colab Notebook6:49

START

Loading Our Data Labels12:04

START

Preparing The Images12:32

START

Turning Data Labels Into Numbers12:11

START

Creating Our Own Validation Set9:18

START

Preprocess Images10:25

START

Preprocess Images 211:00

START

Turning Data Into Batches9:37

START

Turning Data Into Batches 217:54

START

Visualizing Our Data12:41

START

Preparing Our Inputs and Outputs6:37

START

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

START

Building A Deep Learning Model11:42

START

Building A Deep Learning Model 210:53

START

Building A Deep Learning Model 39:05

START

Building A Deep Learning Model 49:12

START

Summarizing Our Model4:52

START

Evaluating Our Model9:26

START

Preventing Overfitting4:20

START

Training Your Deep Neural Network19:09

START

Evaluating Performance With TensorBoard7:30

START

Make And Transform Predictions15:04

START

Transform Predictions To Text15:19

START

Visualizing Model Predictions14:46

START

Visualizing And Evaluate Model Predictions 215:52

START

Visualizing And Evaluate Model Predictions 310:39

START

Saving And Loading A Trained Model13:34

START

Training Model On Full Dataset15:01

START

Making Predictions On Test Images16:54

START

Submitting Model to Kaggle14:14

START

Making Predictions On Our Images15:15

PREVIEW

Finishing Dog Vision: Where to next?

START

Storytelling + Communication: How To Present Your Projects

7 lectures

Section Overview2:19

START

Communicating Your Work3:21

START

Communicating With Managers2:58

START

Communicating With Co-Workers3:42

START

Weekend Project Principle6:32

START

Communicating With Outside World3:28

START

Storytelling3:05

START

Career Advice + Extra Bits

14 lectures

Endorsements On LinkedIn

START

Quick Note: Upcoming Video

START

What If I Don't Have Enough Experience?15:03

START

Learning Guideline

START

Quick Note: Upcoming Videos

START

JTS: Learn to Learn1:59

START

JTS: Start With Why2:43

START

Quick Note: Upcoming Videos

START

CWD: Git + Github17:40

START

CWD: Git + Github 216:52

START

Contributing To Open Source14:44

START

Contributing To Open Source 29:42

START

Exercise: Contribute To Open Source

START

Coding Challenges

START

Learn Python

49 lectures

What Is A Programming Language6:24

START

Python Interpreter14:08

START

How To Run Python Code9:46

START

Our First Python Program7:43

START

Latest Version Of Python3:40

START

Python 2 vs Python 313:21

START

Exercise: How Does Python Work?2:09

START

Learning Python2:05

START

Python Data Types4:46

START

How To Succeed

START

Numbers11:09

START

Math Functions4:29

START

DEVELOPER FUNDAMENTALS: I4:07

START

Operator Precedence3:10

START

Exercise: Operator Precedence

START

Optional: bin() and complex4:02

START

Variables13:12

START

Expressions vs Statements1:36

START

Augmented Assignment Operator2:49

START

Strings5:29

START

String Concatenation1:16

START

Type Conversion3:03

START

Escape Sequences4:23

START

Formatted Strings8:23

START

String Indexes8:57

START

Immutability3:13

START

Built-In Functions + Methods10:03

START

Booleans3:21

START

Exercise: Type Conversion8:22

START

DEVELOPER FUNDAMENTALS: II4:42

START

Exercise: Password Checker7:21

START

Lists5:01

START

List Slicing7:48

START

Matrix4:11

START

List Methods10:28

START

List Methods 24:24

START

List Methods 34:52

START

Common List Patterns5:57

START

List Unpacking2:40

START

None1:51

START

Dictionaries6:20

START

DEVELOPER FUNDAMENTALS: III2:40

START

Dictionary Keys3:37

START

Dictionary Methods4:37

START

Dictionary Methods 27:04

START

Tuples4:46

START

Tuples 23:14

START

Sets7:24

START

Sets 28:45

START

Learn Python Part 2

50 lectures

Breaking The Flow2:34

START

Conditional Logic13:17

START

Indentation In Python4:38

START

Truthy vs Falsey5:17

START

Ternary Operator4:14

START

Short Circuiting4:02

START

Logical Operators6:56

START

Exercise: Logical Operators7:47

START

is vs ==7:36

START

For Loops7:01

START

Iterables6:43

START

Exercise: Tricky Counter3:23

START

range()5:38

START

enumerate()4:37

START

While Loops6:28

START

While Loops 25:49

START

break, continue, pass4:15

START

Our First GUI8:48

START

DEVELOPER FUNDAMENTALS: IV6:34

START

Exercise: Find Duplicates3:54

START

Functions7:41

START

Parameters and Arguments4:24

START

Default Parameters and Keyword Arguments5:40

START

return13:11

START

Exercise: Tesla

START

Methods vs Functions4:33

START

Docstrings3:47

START

Clean Code4:38

START

*args and **kwargs7:56

START

Exercise: Functions4:18

START

Scope3:37

START

Scope Rules6:55

START

global Keyword6:13

START

nonlocal Keyword3:21

START

Why Do We Need Scope?3:38

START

Pure Functions9:23

START

map()6:30

START

filter()4:23

START

zip()3:28

START

reduce()7:31

START

List Comprehensions8:37

START

Set Comprehensions6:26

START

Exercise: Comprehensions4:36

START

Python Exam: Testing Your Understanding

START

Modules in Python10:54

START

Quick Note: Upcoming Videos

START

Optional: PyCharm8:19

START

Packages in Python10:45

START

Different Ways To Import7:03

START

Next Steps

START

Where To Go From Here?

4 lectures

Become An Alumni

START

Thank You2:44

START

Review This Course!

START

Become a ZTM Ambassador ➡ Refer new students. Earn cash.

START

Meet your instructors

Your instructors aren’t just experts with years of real-world professional experience. They have been in your shoes. They make learning fun. They make complex topics feel simple. They will motivate you. They will push you. And they go above and beyond to help you succeed.

Andrei Neagoie

Hi, I'm Andrei Neagoie!

Andrei, lead instructor of Zero To Mastery Academy, has taught 1,000,000+ students worldwide how to code and get hired. ZTM grads work for world-class companies like Apple, Google, Amazon, Tesla, IBM, Facebook, Shopify and many more.

SEE MY BIO & COURSES

Andrei Neagoie

Senior Software Developer

Daniel Bourke

Hi, I'm Daniel Bourke!

Daniel, a self-taught Machine Learning Engineer, has worked at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen, and is now using his expertise to teach thousands of students data science and machine learning.

SEE MY BIO & COURSES

Daniel Bourke

Machine Learning Engineer

Frequently asked questions

Are there any prerequisites for this course?

  • No prior experience is needed (not even Math or Statistics). We start from the very basics. There are two paths within the course for those that know programming and those that don't
  • A computer (Linux/Windows/Mac) with an internet connection
  • All tools used in this course are free for you to use

Who is this course for?

  • Anyone with zero experience (or beginner/junior) that wants to learn Machine Learning, Data Science, and Python
  • You are a programmer that wants to extend your skills into Data Science and Machine Learning to make yourself more valuable
  • Anyone who wants to learn these topics from industry experts that don’t only teach, but have actually worked in the field
  • You’re looking for one single course to teach you about Machine Learning and Data Science and get you caught up to speed with the industry
  • You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really “getting it”
  • You want to learn to use Deep Learning and Neural Networks with your projects
  • You want to add value to your own business or company you work for by using powerful Machine Learning tools

Do you provide a certificate of completion?

We definitely do and they are quite nice. You will also be able to add Zero To Mastery Academy to the education section of your LinkedIn profile as well.

Can I use the course projects in my portfolio?

Yes, you’d be crazy not to in our slightly biased opinion! All projects are downloadable and ready to use the minute you join.

Many of our students tell us the projects they built while following along with our courses were what got them interviews and because they built the projects themselves, they could confidently explain and walk through their work during the interview.

You know what that means? Job offer!

Can I download the videos?

Definitely. You can download any and all lessons for personal use. We do everything we can to make learning easy, fun and accessible. Whether that’s on your commute, on a flight or just when you have limited access to good WiFi.

Still have more questions about the Academy?

Still have more questions specific to the Academy membership? No problem, we answer some more here.

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