Complete Machine Learning and Data Science: Zero to Mastery
Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!
What you'll learn
- ✓ Become a Data Scientist and get hired
- ✓ 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 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
Meet your instructors

Hi! I'm Andrei.
Senior Software Developer turned Instructor, Founder of ZTM
Andrei is the instructor of some of the highest rated programming courses on the web. Some of his students (400,000+ in the past few years) now work for some of the biggest tech companies around the world like Apple, Google, Amazon, Tesla, IBM, Shopify and many more.
He has worked 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.

Hi! I'm Daniel.
Machine Learning Engineer and Instructor
Daniel Bourke is a self-taught Machine Learning Engineer who has worked at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen, where he worked on machine learning and data problems across a wide range of industries.
He knows what it's like to try and learn a new topic, online and on your own. So he pours his soul into making sure his courses are as accessible as possible and takes complicated topics and explains them in an entertaining, yet simple and educative way.
Don't take our word for it

Luther
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. The flow of the course takes all those confusions away and you indeed go from Zero to Mastery. Andrei and Daniel have done an outstanding job producing this high-quality content. Hand's down one of the best DS/ML courses.
Erik Bustos
Andrei 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.

Jan Montalvo
The course is amazing. No, you won’t be as capable as a Phd Machine Learning expert, but you’ll get hands-on Data Science and Machine Learning experience! I love the course because 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!!

Mohamed Benosman
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!

Kevin Okinedo
Material is structured and friendly for beginners. Rather than wasting time on theory, it goes in-depth on the practical application of machine learning immediately then gives you the theory later. If you are looking to become employable in the field, look no further.
Shanay Murdock
Andrei and Daniel do an excellent job of framing the information in an understandable way. Plus I love Andrei's ongoing framework of placing learning within the context of learning "on the job" through a fictional company and simultaneously setting up expectations of what it's like to work in the field. Another Andrei Neagoie goldmine course!
Why Zero To Mastery is right for you
With so many online resources available, it can be paralyzing not only figuring out where to start but more importantly which courses will actually teach you the skills you need to get hired.
That’s why the Zero To Mastery Academy exists, to provide industry-leading courses and content to teach you the relevant skills you need to advance your career and get you hired at some of the top companies in the world.
Join now to get complete access to this course and all others for only $23/month.
Course Curriculum
To make sure this course is a good fit for you, you can start learning for free right now with over 1 hour of free lessons. Click the PREVIEW links below.
Course Curriculum
- What Is Machine Learning? (6:52)
- AI/Machine Learning/Data Science (4:51)
- Exercise: Machine Learning Playground (6:16)
- How Did We Get Here? (6:03)
- Exercise: YouTube Recommendation Engine (4:24)
- Types of Machine Learning (4:41)
- Are You Getting It Yet?
- What Is Machine Learning? Round 2 (4:44)
- Section Review (1:48)
- Section Overview (3:08)
- Introducing Our Framework (2:38)
- 6 Step Machine Learning Framework (4:59)
- Types of Machine Learning Problems (10:32)
- Types of Data (4:50)
- Types of Evaluation (3:31)
- Features In Data (5:22)
- Modelling - Splitting Data (5:58)
- Modelling - Picking the Model (4:35)
- Modelling - Tuning (3:17)
- Modelling - Comparison (9:32)
- Overfitting and Underfitting Definitions
- Experimentation (3:35)
- Tools We Will Use (3:59)
- Optional: Elements of AI
- Section Overview (1:09)
- Introducing Our Tools (3:28)
- What is Conda? (2:35)
- Conda Environments (4:30)
- Mac Environment Setup (17:26)
- Mac Environment Setup 2 (14:11)
- Windows Environment Setup (5:17)
- Windows Environment Setup 2 (23:17)
- Linux Environment Setup
- Sharing your Conda Environment
- Jupyter Notebook Walkthrough (10:20)
- Jupyter Notebook Walkthrough 2 (16:17)
- Jupyter Notebook Walkthrough 3 (8:10)
- Section Overview (2:27)
- Downloading Workbooks and Assignments
- Pandas Introduction (4:29)
- Series, Data Frames and CSVs (13:21)
- Data from URLs
- Describing Data with Pandas (9:48)
- Selecting and Viewing Data with Pandas (11:08)
- Selecting and Viewing Data with Pandas Part 2 (13:06)
- Manipulating Data (13:56)
- Manipulating Data 2 (9:56)
- Manipulating Data 3 (10:12)
- Assignment: Pandas Practice
- How To Download The Course Assignments (7:43)
- Section Overview (2:40)
- NumPy Introduction (5:17)
- Quick Note: Correction In Next Video
- NumPy DataTypes and Attributes (14:05)
- Creating NumPy Arrays (9:22)
- NumPy Random Seed (7:17)
- Viewing Arrays and Matrices (9:35)
- Manipulating Arrays (11:31)
- Manipulating Arrays 2 (9:44)
- Standard Deviation and Variance (7:10)
- Reshape and Transpose (7:26)
- Dot Product vs Element Wise (11:45)
- Exercise: Nut Butter Store Sales (13:04)
- Comparison Operators (3:33)
- Sorting Arrays (6:19)
- Turn Images Into NumPy Arrays (7:37)
- Assignment: NumPy Practice
- Optional: Extra NumPy resources
- Section Overview (1:50)
- Matplotlib Introduction (5:16)
- Importing And Using Matplotlib (11:36)
- Anatomy Of A Matplotlib Figure (9:19)
- Scatter Plot And Bar Plot (10:09)
- Histograms And Subplots (8:40)
- Subplots Option 2 (4:15)
- Quick Tip: Data Visualizations (1:48)
- Plotting From Pandas DataFrames (5:58)
- Quick Note: Regular Expressions
- Plotting From Pandas DataFrames 2 (10:33)
- Plotting from Pandas DataFrames 3 (8:32)
- Plotting from Pandas DataFrames 4 (6:36)
- Plotting from Pandas DataFrames 5 (8:28)
- Plotting from Pandas DataFrames 6 (8:27)
- Plotting from Pandas DataFrames 7 (11:20)
- Customizing Your Plots (10:09)
- Customizing Your Plots 2 (9:41)
- Saving And Sharing Your Plots (4:14)
- Assignment: Matplotlib Practice
- Section Overview (2:29)
- Scikit-learn Introduction (6:41)
- Quick Note: Upcoming Video
- Refresher: What Is Machine Learning? (5:40)
- Quick Note: Upcoming Videos
- Scikit-learn Cheatsheet (6:12)
- Typical scikit-learn Workflow (23:14)
- Optional: Debugging Warnings In Jupyter (18:57)
- Getting Your Data Ready: Splitting Your Data (8:37)
- Quick Tip: Clean, Transform, Reduce (5:03)
- Getting Your Data Ready: Convert Data To Numbers (16:54)
- Getting Your Data Ready: Handling Missing Values With Pandas (12:22)
- Extension: Feature Scaling
- Note: Correction in the upcoming video
- Getting Your Data Ready: Handling Missing Values With Scikit-learn (17:29)
- Choosing The Right Model For Your Data (14:54)
- Choosing The Right Model For Your Data 2 (Regression) (8:41)
- Quick Note: Decision Trees
- Quick Tip: How ML Algorithms Work (1:25)
- Choosing The Right Model For Your Data 3 (Classification) (12:45)
- Fitting A Model To The Data (6:45)
- Making Predictions With Our Model (8:24)
- predict() vs predict_proba() (8:33)
- Making Predictions With Our Model (Regression) (6:49)
- Evaluating A Machine Learning Model (Score) (8:57)
- Evaluating A Machine Learning Model 2 (Cross Validation) (13:16)
- Evaluating A Classification Model 1 (Accuracy) (4:46)
- Evaluating A Classification Model 2 (ROC Curve) (9:04)
- Evaluating A Classification Model 3 (ROC Curve) (7:44)
- Reading Extension: ROC Curve + AUC
- Evaluating A Classification Model 4 (Confusion Matrix) (11:01)
- Evaluating A Classification Model 5 (Confusion Matrix) (8:07)
- Evaluating A Classification Model 6 (Classification Report) (10:16)
- Evaluating A Regression Model 1 (R2 Score) (9:12)
- Evaluating A Regression Model 2 (MAE) (4:17)
- Evaluating A Regression Model 3 (MSE) (6:34)
- Machine Learning Model Evaluation
- Evaluating A Model With Cross Validation and Scoring Parameter (14:04)
- Evaluating A Model With Scikit-learn Functions (12:14)
- Improving A Machine Learning Model (11:16)
- Tuning Hyperparameters (23:15)
- Tuning Hyperparameters 2 (14:23)
- Tuning Hyperparameters 3 (14:59)
- Note: Metric Comparison Improvement
- Quick Tip: Correlation Analysis (2:28)
- Saving And Loading A Model (7:28)
- Saving And Loading A Model 2 (6:20)
- Putting It All Together (20:19)
- Putting It All Together 2 (11:34)
- Scikit-Learn Practice
- Section Overview (2:09)
- Project Overview (6:09)
- Project Environment Setup (10:58)
- Step 1~4 Framework Setup (12:06)
- Getting Our Tools Ready (9:04)
- Exploring Our Data (8:33)
- Finding Patterns (10:02)
- Finding Patterns 2 (16:47)
- Finding Patterns 3 (13:36)
- Preparing Our Data For Machine Learning (8:51)
- Choosing The Right Models (10:15)
- Experimenting With Machine Learning Models (6:31)
- Tuning/Improving Our Model (13:49)
- Tuning Hyperparameters (11:27)
- Tuning Hyperparameters 2 (11:49)
- Tuning Hyperparameters 3 (7:06)
- Quick Note: Confusion Matrix Labels
- Evaluating Our Model (10:59)
- Evaluating Our Model 2 (5:54)
- Evaluating Our Model 3 (8:49)
- Finding The Most Important Features (16:07)
- Reviewing The Project (9:13)
- Section Overview (1:07)
- Project Overview (4:24)
- Project Environment Setup (10:52)
- Step 1~4 Framework Setup (8:36)
- Downloading the data for the next two projects
- Exploring Our Data (14:16)
- Exploring Our Data 2 (6:16)
- Feature Engineering (15:24)
- Turning Data Into Numbers (15:38)
- Filling Missing Numerical Values (12:49)
- Filling Missing Categorical Values (8:27)
- Fitting A Machine Learning Model (7:16)
- Splitting Data (10:00)
- Challenge: What's wrong with splitting data after filling it?
- Custom Evaluation Function (11:13)
- Reducing Data (10:36)
- RandomizedSearchCV (9:32)
- Improving Hyperparameters (8:11)
- Preproccessing Our Data (13:15)
- Making Predictions (9:17)
- Feature Importance (13:50)
- Data Engineering Introduction (3:23)
- What Is Data? (6:42)
- What is a Data Engineer? (4:20)
- What is A Data Engineer 2? (5:35)
- What is a Data Engineer 3? (5:03)
- What is a Data Engineer 4? (3:22)
- Types of Databases (6:50)
- Quick Note: Upcoming Video
- Optional: OLTP Databases (10:54)
- Optional: Learn SQL
- Hadoop, HDFS and MapReduce (4:22)
- Apache Spark and Apache Flink (2:07)
- Kafka and Stream Processing (4:33)
- Section Overview (2:06)
- Deep Learning and Unstructured Data (13:36)
- Setting Up With Google
- Setting Up Google Colab (7:17)
- Google Colab Workspace (4:23)
- Uploading Project Data (6:52)
- Setting Up Our Data (4:40)
- Setting Up Our Data 2 (1:32)
- Importing TensorFlow 2 (12:43)
- Optional: TensorFlow 2.0 Default Issue (3:38)
- Using A GPU (8:59)
- Optional: GPU and Google Colab (4:27)
- Optional: Reloading Colab Notebook (6:49)
- Loading Our Data Labels (12:04)
- Preparing The Images (12:32)
- Turning Data Labels Into Numbers (12:11)
- Creating Our Own Validation Set (9:18)
- Preprocess Images (10:25)
- Preprocess Images 2 (11:00)
- Turning Data Into Batches (9:37)
- Turning Data Into Batches 2 (17:54)
- Visualizing Our Data (12:41)
- Preparing Our Inputs and Outputs (6:37)
- Optional: How machines learn and what's going on behind the scenes?
- Building A Deep Learning Model (11:42)
- Building A Deep Learning Model 2 (10:53)
- Building A Deep Learning Model 3 (9:05)
- Building A Deep Learning Model 4 (9:12)
- Summarizing Our Model (4:52)
- Evaluating Our Model (9:26)
- Preventing Overfitting (4:20)
- Training Your Deep Neural Network (19:09)
- Evaluating Performance With TensorBoard (7:30)
- Make And Transform Predictions (15:04)
- Transform Predictions To Text (15:19)
- Visualizing Model Predictions (14:46)
- Visualizing And Evaluate Model Predictions 2 (15:52)
- Visualizing And Evaluate Model Predictions 3 (10:39)
- Saving And Loading A Trained Model (13:34)
- Training Model On Full Dataset (15:01)
- Making Predictions On Test Images (16:54)
- Submitting Model to Kaggle (14:14)
- Making Predictions On Our Images (15:15)
- Finishing Dog Vision: Where to next?
- Endorsements On LinkedIn
- Quick Note: Upcoming Video
- What If I Don't Have Enough Experience? (15:03)
- Learning Guideline
- Quick Note: Upcoming Videos
- JTS: Learn to Learn (1:59)
- JTS: Start With Why (2:43)
- Quick Note: Upcoming Videos
- CWD: Git + Github (17:40)
- CWD: Git + Github 2 (16:52)
- Contributing To Open Source (14:44)
- Contributing To Open Source 2 (9:42)
- Coding Challenges
- Exercise: Contribute To Open Source
- What Is A Programming Language (6:24)
- Python Interpreter (7:04)
- How To Run Python Code (4:53)
- Our First Python Program (7:43)
- Latest Version Of Python (1:58)
- Python 2 vs Python 3 (6:40)
- Exercise: How Does Python Work? (2:09)
- Learning Python (2:05)
- Python Data Types (4:46)
- How To Succeed
- Numbers (11:09)
- Math Functions (4:29)
- DEVELOPER FUNDAMENTALS: I (4:07)
- Operator Precedence (3:10)
- Exercise: Operator Precedence
- Optional: bin() and complex (4:02)
- Variables (13:12)
- Expressions vs Statements (1:36)
- Augmented Assignment Operator (2:49)
- Strings (5:29)
- String Concatenation (1:16)
- Type Conversion (3:03)
- Escape Sequences (4:23)
- Formatted Strings (8:23)
- String Indexes (8:57)
- Immutability (3:13)
- Built-In Functions + Methods (10:03)
- Booleans (3:21)
- Exercise: Type Conversion (8:22)
- DEVELOPER FUNDAMENTALS: II (4:42)
- Exercise: Password Checker (7:21)
- Lists (5:01)
- List Slicing (7:48)
- Matrix (4:11)
- List Methods (10:28)
- List Methods 2 (4:24)
- List Methods 3 (4:52)
- Common List Patterns (5:57)
- List Unpacking (2:40)
- None (1:51)
- Dictionaries (6:20)
- DEVELOPER FUNDAMENTALS: III (2:40)
- Dictionary Keys (3:37)
- Dictionary Methods (4:37)
- Dictionary Methods 2 (7:04)
- Tuples (4:46)
- Tuples 2 (3:14)
- Sets (7:24)
- Sets 2 (8:45)
- Breaking The Flow (2:34)
- Conditional Logic (13:17)
- Indentation In Python (4:38)
- Truthy vs Falsey (5:17)
- Ternary Operator (4:14)
- Short Circuiting (4:02)
- Logical Operators (6:56)
- Exercise: Logical Operators (7:47)
- is vs == (7:36)
- For Loops (7:01)
- Iterables (6:43)
- Exercise: Tricky Counter (3:23)
- range() (5:38)
- enumerate() (4:37)
- While Loops (6:28)
- While Loops 2 (5:49)
- break, continue, pass (4:15)
- Our First GUI (8:48)
- DEVELOPER FUNDAMENTALS: IV (6:34)
- Exercise: Find Duplicates (3:54)
- Functions (7:41)
- Parameters and Arguments (4:24)
- Default Parameters and Keyword Arguments (5:40)
- return (13:11)
- Exercise: Tesla
- Methods vs Functions (4:33)
- Docstrings (3:47)
- Clean Code (4:38)
- *args and **kwargs (7:56)
- Exercise: Functions (4:18)
- Scope (3:37)
- Scope Rules (6:55)
- global Keyword (6:13)
- nonlocal Keyword (3:21)
- Why Do We Need Scope? (3:38)
- Pure Functions (9:23)
- map() (6:30)
- filter() (4:23)
- zip() (3:28)
- reduce() (7:31)
- List Comprehensions (8:37)
- Set Comprehensions (6:26)
- Exercise: Comprehensions (4:36)
- Python Exam: Testing Your Understanding
- Modules in Python (10:54)
- Quick Note: Upcoming Videos
- Optional: PyCharm (8:19)
- Packages in Python (10:45)
- Different Ways To Import (7:03)
- Next Steps
Course Details
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 & Machine Learning course online (we use the latest version of Python, Tensorflow 2.0 and other libraries)!
We've also just recently fully updated this course to ensure you're learning the latest skills and trends for 2021 and beyond.
Graduates of Zero To Mastery courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook + other top tech companies. This could 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. We are 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, you 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!
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 and Data Science engineer.
The course covers 2 tracks:
1️⃣ If you already know programming, you can dive right in and skip the section where we teach you Python from scratch.
2️⃣ 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, 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 will learn 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
- 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 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 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.
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 probably hear buzzwords like Artificial Neural Network or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!
Click Start Learning Now to join other ZTM Academy members getting a leg up in the industry and get hired as Data Scientists and Machine Learning Engineers. We guarantee this is better than any bootcamp or online course out there on the topic.
We'll see you inside the course!
Answers to (at least some of) your questions
Are there any prerequisites for this course?
- No prior experience is needed (not even Math and 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 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) and 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.
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 you just have limited access to good wifi.
How long does it usually take for me to build something and get hired?
Ultimately you’re the only can that can control that. However, while everyone learns at a different pace, students who put in a couple hours each day to apply what they’ve learned should be able to confidently build their own projects and start interviewing in 3-6 months. We don’t see just getting hired as the end goal though. Our advanced courses will also teach you the topics and skills you need to get promoted or hired as a senior developer.
Still have more questions specific to the Academy membership? No problem, head to the bottom of this page.
Live the life you want, starting now
Learning to code and becoming a developer provides endless opportunities to live the life you want. Whether that’s a high paying job with a world-class tech company, working remotely or building your own apps, the ZTM Academy will equip you with the skills and knowledge to achieve your dreams.
Our courses walk you through the entire journey of starting to learn to code to having a successful career as a developer. Along the way, you’ll not only be supported by Andrei, Daniel and course TAs but also your peers in the exclusive Zero To Mastery community.
Join now to take the first step to change your life.