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30-Day Money-Back Guarantee

This course includes:

  • 44 hours on-demand video
  • 75 articles
  • 38 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Python

Machine Learning A-Z™: Hands-On Python & R In Data Science

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
Bestseller
Rating: 4.5 out of 54.5 (139,354 ratings)
737,582 students
Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, SuperDataScience Support
Last updated 1/2021
English
English [Auto], French [Auto], 
30-Day Money-Back Guarantee

What you'll learn

  • Master Machine Learning on Python & R
  • Have a great intuition of many Machine Learning models
  • Make accurate predictions
  • Make powerful analysis
  • Make robust Machine Learning models
  • Create strong added value to your business
  • Use Machine Learning for personal purpose
  • Handle specific topics like Reinforcement Learning, NLP and Deep Learning
  • Handle advanced techniques like Dimensionality Reduction
  • Know which Machine Learning model to choose for each type of problem
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem
Curated for the Udemy for Business collection

Course content

45 sections • 322 lectures • 44h 29m total length

  • Preview03:22
  • BONUS #1: Learning Paths
    00:33
  • BONUS #2: ML vs. DL vs. AI - What’s the Difference?
    00:13
  • BONUS #3: Regression Types
    00:12
  • Why Machine Learning is the Future
    06:37
  • Important notes, tips & tricks for this course
    02:01
  • This PDF resource will help you a lot!
    01:04
  • GET ALL THE CODES AND DATASETS HERE!
    01:07
  • Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder
    16:48
  • Installing R and R Studio (Mac, Linux & Windows)
    Preview05:40
  • BONUS: Meet your instructors
    00:26
  • Some Additional Resources
    00:10
  • FAQBot!
    01:29
  • Your Shortcut To Becoming A Better Data Scientist!
    02:04

  • Welcome to Part 1 - Data Preprocessing
    00:21

  • Make sure you have your Machine Learning A-Z folder ready
    00:15
  • Getting Started
    10:50
  • Importing the Libraries
    03:34
  • Importing the Dataset
    15:42
  • For Python learners, summary of Object-oriented programming: classes & objects
    01:00
  • Taking care of Missing Data
    12:15
  • Encoding Categorical Data
    14:58
  • Splitting the dataset into the Training set and Test set
    13:47
  • Feature Scaling
    20:31

  • Welcome
    00:24
  • Getting Started
    01:35
  • Make sure you have your dataset ready
    00:08
  • Dataset Description
    01:57
  • Importing the Dataset
    02:44
  • Taking care of Missing Data
    06:22
  • Encoding Categorical Data
    06:02
  • Splitting the dataset into the Training set and Test set
    09:34
  • Feature Scaling
    09:14
  • Data Preprocessing Template
    05:15

  • Welcome to Part 2 - Regression
    00:22

  • Simple Linear Regression Intuition - Step 1
    05:45
  • Simple Linear Regression Intuition - Step 2
    03:09
  • Make sure you have your Machine Learning A-Z folder ready
    00:20
  • Simple Linear Regression in Python - Step 1
    12:48
  • Simple Linear Regression in Python - Step 2
    07:56
  • Simple Linear Regression in Python - Step 3
    04:35
  • Simple Linear Regression in Python - Step 4
    12:56
  • Simple Linear Regression in Python - BONUS
    00:30
  • Simple Linear Regression in R - Step 1
    04:40
  • Simple Linear Regression in R - Step 2
    Preview05:58
  • Simple Linear Regression in R - Step 3
    03:38
  • Simple Linear Regression in R - Step 4
    15:55
  • Simple Linear Regression
    5 questions

  • Dataset + Business Problem Description
    03:44
  • Multiple Linear Regression Intuition - Step 1
    01:02
  • Multiple Linear Regression Intuition - Step 2
    01:00
  • Multiple Linear Regression Intuition - Step 3
    07:21
  • Multiple Linear Regression Intuition - Step 4
    02:10
  • Understanding the P-Value
    11:44
  • Multiple Linear Regression Intuition - Step 5
    15:41
  • Make sure you have your Machine Learning A-Z folder ready
    00:20
  • Multiple Linear Regression in Python - Step 1
    08:30
  • Multiple Linear Regression in Python - Step 2
    09:11
  • Multiple Linear Regression in Python - Step 3
    10:37
  • Multiple Linear Regression in Python - Step 4
    12:31
  • Multiple Linear Regression in Python - Backward Elimination
    01:35
  • Multiple Linear Regression in Python - BONUS
    00:31
  • Multiple Linear Regression in R - Step 1
    07:50
  • Multiple Linear Regression in R - Step 2
    10:25
  • Multiple Linear Regression in R - Step 3
    04:26
  • Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
    17:51
  • Multiple Linear Regression in R - Backward Elimination - Homework Solution
    07:33
  • Multiple Linear Regression in R - Automatic Backward Elimination
    00:15
  • Multiple Linear Regression
    5 questions

  • Polynomial Regression Intuition
    05:08
  • Make sure you have your Machine Learning A-Z folder ready
    00:20
  • Polynomial Regression in Python - Step 1
    13:30
  • Polynomial Regression in Python - Step 2
    11:40
  • Polynomial Regression in Python - Step 3
    12:54
  • Polynomial Regression in Python - Step 4
    08:10
  • Polynomial Regression in R - Step 1
    09:12
  • Polynomial Regression in R - Step 2
    09:58
  • Polynomial Regression in R - Step 3
    19:54
  • Polynomial Regression in R - Step 4
    09:35
  • R Regression Template
    11:58

  • Preview08:09
  • Preview03:57
  • Make sure you have your Machine Learning A-Z folder ready
    00:20
  • SVR in Python - Step 1
    09:15
  • SVR in Python - Step 2
    15:10
  • SVR in Python - Step 3
    06:27
  • SVR in Python - Step 4
    08:01
  • SVR in Python - Step 5
    15:40
  • SVR in R
    11:44

  • Decision Tree Regression Intuition
    11:06
  • Make sure you have your Machine Learning A-Z folder ready
    00:20
  • Decision Tree Regression in Python - Step 1
    08:38
  • Decision Tree Regression in Python - Step 2
    05:00
  • Decision Tree Regression in Python - Step 3
    03:16
  • Decision Tree Regression in Python - Step 4
    09:50
  • Decision Tree Regression in R
    19:54

Requirements

  • Just some high school mathematics level.

Description

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:

  • Part 1 - Data Preprocessing

  • Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

  • Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

  • Part 4 - Clustering: K-Means, Hierarchical Clustering

  • Part 5 - Association Rule Learning: Apriori, Eclat

  • Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

  • Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP

  • Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

  • Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA

  • Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.

Important updates (June 2020):

  • CODES ALL UP TO DATE

  • DEEP LEARNING CODED IN TENSORFLOW 2.0

  • TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST!


Who this course is for:

  • Anyone interested in Machine Learning.
  • Students who have at least high school knowledge in math and who want to start learning Machine Learning.
  • Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
  • Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
  • Any students in college who want to start a career in Data Science.
  • Any data analysts who want to level up in Machine Learning.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.
  • Any people who want to create added value to their business by using powerful Machine Learning tools.

Featured review

Nu'man Amri Maliky
Nu'man Amri Maliky
9 courses
6 reviews
Rating: 5.0 out of 58 months ago
This is an amazing course for the beginners who want to understand about everything in machine learning. Thank you to the instructors (Hadelin de Ponteves and Kirill Eremenko) for explained it clearly and easy to understand. I hope this knowledge can help me for developing my start-up, advancing technology, and giving benefits to others.

Instructors

Kirill Eremenko
Data Scientist
Kirill Eremenko
  • 4.5 Instructor Rating
  • 494,443 Reviews
  • 1,734,784 Students
  • 121 Courses

My name is Kirill Eremenko and I am super-psyched that you are reading this!

Professionally, I am a Data Science management consultant with over five years of experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and today I leverage Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.

From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. I am also passionate about public speaking, and regularly present on Big Data at leading Australian universities and industry events.

To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you!

Hadelin de Ponteves
AI Entrepreneur
Hadelin de Ponteves
  • 4.5 Instructor Rating
  • 264,292 Reviews
  • 1,286,463 Students
  • 84 Courses

Hadelin is the co-founder and CEO at BlueLife AI, which leverages the power of cutting edge Artificial Intelligence to empower businesses to make massive profits by innovating, automating processes and maximizing efficiency. Hadelin is also an online entrepreneur who has created 70+ top-rated educational e-courses to the world on topics such as Machine Learning, Deep Learning, Artificial Intelligence and Blockchain, which have reached 1M+ students in 210 countries.

SuperDataScience Team
Helping Data Scientists Succeed
SuperDataScience Team
  • 4.5 Instructor Rating
  • 456,370 Reviews
  • 1,645,525 Students
  • 107 Courses

Hi there,

We are the SuperDataScience Social team. You will be hearing from us when new SDS courses are released, when we publish new podcasts, blogs, share cheatsheets and more!

We are here to help you stay on the cutting edge of Data Science and Technology. 

See you in class,

Sincerely,

The Real People at SuperDataScience

SuperDataScience Support
Answering All Your Questions
SuperDataScience Support
  • 4.5 Instructor Rating
  • 156,870 Reviews
  • 816,369 Students
  • 2 Courses

Hi there,

We are the SuperDataScience Support team. You will find us in the Data Science courses taught by Kirill Eremenko - we are here to help you out with any questions and make sure your journey through the courses is always smooth sailing!

The best way to get in touch is to post a discussion in the Q&A of the course you are taking. In most cases we will respond within 24 hours.

We're passionate about helping you enjoy the courses!

See you in class,

Sincerely,

The Real People at SuperDataScience

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