Complete Machine Learning and Data Science: Zero to Mastery
4.6 (3,269 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
22,283 students enrolled

Complete Machine Learning and Data Science: Zero to Mastery

Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!
4.6 (3,269 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
22,283 students enrolled
Last updated 5/2020
English
English [Auto-generated]
Current price: $129.99 Original price: $199.99 Discount: 35% off
13 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 42.5 hours on-demand video
  • 50 articles
  • 13 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
Training 5 or more people?

Get your team access to 4,000+ top Udemy courses anytime, anywhere.

Try Udemy for Business
What you'll 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
Course content
Expand all 368 lectures 42:47:57
+ Introduction
4 lectures 11:58
Join Our Online Classroom!
00:58
Exercise: Meet The Community
01:13
+ Machine Learning 101
9 lectures 39:46
Exercise: YouTube Recommendation Engine
04:24
Types of Machine Learning
04:41
Are You Getting It Yet?
00:07
What Is Machine Learning? Round 2
04:44
Section Review
01:48
+ Machine Learning and Data Science Framework
15 lectures 01:07:30
Section Overview
03:08
Introducing Our Framework
02:38
Types of Machine Learning Problems
10:32
Types of Data
04:50
Types of Evaluation
03:31
Features In Data
05:22
Modelling - Splitting Data
05:58
Modelling - Picking the Model
04:35
Modelling - Tuning
03:17
Modelling - Comparison
09:32
Overfitting and Underfitting Definitions
01:01
Experimentation
03:35
Tools We Will Use
04:00
Optional: Elements of AI
00:32
+ The 2 Paths
2 lectures 03:45
The 2 Paths
03:27
Python + Machine Learning Monthly
00:18
+ Data Science Environment Setup
13 lectures 01:48:10
Section Overview
01:09

Note: If you already have Anaconda installed, feel free to keep using it. The following videos will walk-through downloading Miniconda and setting up an environment using Conda.

Preview 03:28
What is Conda?
02:35
Conda Environments
04:30
Mac Environment Setup
17:26
Mac Environment Setup 2
14:11
Windows Environment Setup
05:17
Windows Environment Setup 2
23:17
Linux Environment Setup
00:23
Sharing your Conda Environment
01:06
Jupyter Notebook Walkthrough
10:20
Jupyter Notebook Walkthrough 2
16:17
Jupyter Notebook Walkthrough 3
08:10
+ Pandas: Data Analysis
13 lectures 01:37:47
Section Overview
02:27
Downloading Workbooks and Assignments
00:25
Pandas Introduction
04:29
Series, Data Frames and CSVs
13:21
Data from URLs
00:24
Describing Data with Pandas
09:48
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
09:56
Manipulating Data 3
10:12
Assignment: Pandas Practice
00:52
How To Download The Course Assignments
07:43
+ NumPy
18 lectures 02:08:29
Section Overview
02:40
NumPy Introduction
05:17
Quick Note: Correction In Next Video
00:41
NumPy DataTypes and Attributes
14:05
Creating NumPy Arrays
09:22
NumPy Random Seed
07:17
Viewing Arrays and Matrices
09:35
Manipulating Arrays
11:31
Manipulating Arrays 2
09:44
Standard Deviation and Variance
07:10
Reshape and Transpose
07:26
Dot Product vs Element Wise
11:45
Exercise: Nut Butter Store Sales
13:04
Comparison Operators
03:33
Sorting Arrays
06:19
Turn Images Into NumPy Arrays
07:37
Assignment: NumPy Practice
00:56
Optional: Extra NumPy resources
00:26
+ Matplotlib: Plotting and Data Visualization
20 lectures 02:18:06
Section Overview
01:50
Matplotlib Introduction
05:16
Importing And Using Matplotlib
11:36
Anatomy Of A Matplotlib Figure
09:19
Scatter Plot And Bar Plot
10:09
Histograms And Subplots
08:40
Subplots Option 2
04:15
Quick Tip: Data Visualizations
01:48
Plotting From Pandas DataFrames
05:58
Quick Note: Regular Expressions
00:23
Plotting From Pandas DataFrames 2
10:33
Plotting from Pandas DataFrames 3
08:32
Plotting from Pandas DataFrames 4
06:36
Plotting from Pandas DataFrames 5
08:28
Plotting from Pandas DataFrames 6
08:27
Plotting from Pandas DataFrames 7
11:20
Customizing Your Plots
10:09
Saving And Sharing Your Plots
04:14
Assignment: Matplotlib Practice
00:51
+ Scikit-learn: Creating Machine Learning Models
50 lectures 07:11:41
Section Overview
02:29
Scikit-learn Introduction
06:41
Quick Note: Upcoming Video
00:18
Refresher: What Is Machine Learning?
05:40
Quick Note: Upcoming Videos
00:44
Typical scikit-learn Workflow
23:14
Optional: Debugging Warnings In Jupyter
18:57
Getting Your Data Ready: Splitting Your Data
08:37
Quick Tip: Clean, Transform, Reduce
05:03
Getting Your Data Ready: Convert Data To Numbers
16:54
Getting Your Data Ready: Handling Missing Values With Pandas
12:22
Extension: Feature Scaling
01:17
Note: Correction in the upcoming video (splitting data)
00:46
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)
08:41
Quick Note: Decision Trees
00:08
Quick Tip: How ML Algorithms Work
01:25
Choosing The Right Model For Your Data 3 (Classification)
12:45
Fitting A Model To The Data
06:45
Making Predictions With Our Model
08:24
predict() vs predict_proba()
08:33
Making Predictions With Our Model (Regression)
06:49
Evaluating A Machine Learning Model (Score)
08:57
Evaluating A Machine Learning Model 2 (Cross Validation)
13:16
Evaluating A Classification Model 1 (Accuracy)
04:46
Evaluating A Classification Model 2 (ROC Curve)
09:04
Evaluating A Classification Model 3 (ROC Curve)
07:44
Reading Extension: ROC Curve + AUC
00:39
Evaluating A Classification Model 4 (Confusion Matrix)
11:01
Evaluating A Classification Model 5 (Confusion Matrix)
08:07
Evaluating A Classification Model 6 (Classification Report)
10:16
Evaluating A Regression Model 1 (R2 Score)
09:12
Evaluating A Regression Model 2 (MAE)
04:17
Evaluating A Regression Model 3 (MSE)
06:34
Machine Learning Model Evaluation
02:37
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
00:49
Quick Tip: Correlation Analysis
02:28
Saving And Loading A Model
07:28
Saving And Loading A Model 2
06:20
Putting It All Together
20:19
Putting It All Together 2
11:34
Scikit-Learn Practice
00:51
Requirements
  • No prior experience is needed (not even Math and Statistics). We start from the very basics.
  • A computer (Linux/Windows/Mac) with internet connection.
  • Two paths for those that know programming and those that don't.
  • All tools used in this course are free for you to use.
Description

This is a brand new Machine Learning and Data Science course just launched January 2020 and updated this month with the latest trends and skills! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 270,000+ 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.


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:

Andrei Neagoie 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!

Who this course is for:
  • Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science and Python
  • You are a programmer that wants to extend their 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.