[Download] Complete Machine Learning and Data Science: Zero to Mastery (Free)

Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!
4.6 (2,723 ratings)
19,042 students enrolled
Created by Andrei Neagoie, Daniel Bourke
Last updated 5/2020


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

all 366 lectures

4 lectures

Machine Learning 101
9 lectures

Machine Learning and Data Science Framework
15 lectures

The 2 Paths
2 lectures

Data Science Environment Setup
13 lectures

Pandas: Data Analysis
13 lectures

18 lectures

Matplotlib: Plotting and Data Visualization
20 lectures

Scikit-learn: Creating Machine Learning Models
49 lectures

Supervised Learning: Classification Regression
1 lecture

Milestone Project 1: Supervised Learning (Classification)
21 lectures

Milestone Project 2: Supervised Learning (Time Series Data)
20 lectures

Data Engineering
13 lectures

Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2
44 lectures

Storytelling Communication: How To Present Your Work
8 lectures

Career Advice Extra Bits
14 lectures

Learn Python
48 lectures

Learn Python Part 2
50 lectures

Bonus: Learn Advanced Statistics and Mathematics for FREE!
1 lecture

Where To Go From Here?
2 lectures

1 lecture

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


So I’ve just finished this course and overall I’m very pleased with it. Although I’m a beginner to this whole thing, I feel like it gave me a good grasp of how these projects/problems are generally approached. There is not much theory included (as the instructors promised), the course has the practice in its focus. And although occassionally some parts of the code are not always fully explained, I don’t think it’s a problem because if they had been then the course would have been much much longer and would have lost its focus. Another thing: I saw a one-star review which was complaining about the “Googling” part. I found that part great because it shows you the reglular workflow of building these projects and it teaches you this important skill of knowing how to search for stuff on the internet. Honestly, after this 43 hours of material I’d be surprised if I remembered most of the syntax covered in this course. But instead I remember the concepts, I know the main steps, I know what I’m looking for, I have the perspective and I know how to look the rest up on Google.

Thanks to the instructors, the course was great.

Now I’m gonna go off and build my own machine learning projects using what I’ve learned here and then proceed to learn even more.

An excellent course put by Daniel & Andrei. Really really loved it. 2 things you may wanna know about this course before taking it is,

1. they dont teach you concepts or intuition behind any algo

2. also they type, retype & correct typos (extends the course by 2 more hours.

But apart from this if you wanna get hands on in data science projects with some actual data then please enroll and complete this course. all you have to do is to code with them and see how they do things. when you complete the course, you will have 3 solid projects in your profile. I really loved the dog vision project. it is my first tensorflow project and i really didnt know that I will do such an amazing project. i am playing with custom images daily. these people teach you real knowledge required for industry then some boring theory. go ahead take it and u will be totally happy. thanks Andrei and Daniel.

‘ve just passed the half of this course and I’m looking forward the next lesson.

Considering I had no bases of python, Andrei and Daniel has done a great job so far!

Some argument seemed a little bit tricky the first time but I didn’t give up and now I’m impressed with how much I’ve learned.

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