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

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

Online Self-Paced

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Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

Course Description

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. 

  • 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!
  • Just some high school mathematics level.

This course includes:

44 hours on-demand video

Access on mobile and TV

75 articles

SkillsFuture Credit eligible *

38 downloadable resources

Certificate of completion

Full lifetime access

* More than 7hr of learning time required

Course content

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

  • Welcome to Part 1 - Data Preprocessing

  • Make sure you have your Machine Learning A-Z folder ready
  • Getting Started
  • Importing the Libraries
  • Importing the Dataset
  • For Python learners, summary of Object-oriented programming: classes & objects
  • Taking care of Missing Data
  • Encoding Categorical Data
  • Splitting the dataset into the Training set and Test set
  • Feature Scaling

  • Welcome
  • Getting Started
  • Make sure you have your dataset ready
  • Dataset Description
  • Importing the Dataset
  • Taking care of Missing Data
  • Encoding Categorical Data
  • Splitting the dataset into the Training set and Test set
  • Feature Scaling
  • Data Preprocessing Template

  • Welcome to Part 2 – Regression

  • Simple Linear Regression Intuition - Step 1
  • Simple Linear Regression Intuition - Step 2
  • Make sure you have your Machine Learning A-Z folder ready
  • Simple Linear Regression in Python - Step 1
  • Simple Linear Regression in Python - Step 2
  • Simple Linear Regression in Python - Step 3
  • Simple Linear Regression in Python - Step 4
  • Simple Linear Regression in Python - BONUS
  • Simple Linear Regression in R - Step 1
  • Simple Linear Regression in R - Step 2
  • Simple Linear Regression in R - Step 3
  • Simple Linear Regression in R - Step 4
  • Simple Linear Regression

  • Dataset + Business Problem Description
  • Multiple Linear Regression Intuition - Step 1
  • Multiple Linear Regression Intuition - Step 2
  • Multiple Linear Regression Intuition - Step 3
  • Multiple Linear Regression Intuition - Step 4
  • Understanding the P-Value
  • Multiple Linear Regression Intuition - Step 5
  • Make sure you have your Machine Learning A-Z folder ready
  • Multiple Linear Regression in Python - Step 1
  • Multiple Linear Regression in Python - Step 2
  • Multiple Linear Regression in Python - Step 3
  • Multiple Linear Regression in Python - Step 4
  • Multiple Linear Regression in Python - Backward Elimination
  • Multiple Linear Regression in Python - BONUS
  • Multiple Linear Regression in R - Step 1
  • Multiple Linear Regression in R - Step 2
  • Multiple Linear Regression in R - Step 3
  • Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
  • Multiple Linear Regression in R - Backward Elimination - Homework Solution
  • Multiple Linear Regression in R - Automatic Backward Elimination
  • Multiple Linear Regression

  • Polynomial Regression Intuition
  • Make sure you have your Machine Learning A-Z folder ready
  • Polynomial Regression in Python - Step 1
  • Polynomial Regression in Python - Step 2
  • Polynomial Regression in Python - Step 3
  • Polynomial Regression in Python - Step 4
  • Polynomial Regression in R - Step 1
  • Polynomial Regression in R - Step 2
  • Polynomial Regression in R - Step 3
  • Polynomial Regression in R - Step 4
  • R Regression Template

Certificate of Completion

When you complete a paid Udemy course, you will receive a certificate of completion, which you can share with your friends, relatives, co-workers and potential employers. Certificates of completion help demonstrate your accomplishments, but please note Udemy is not an accredited institution, and as a result, the certificates cannot be used for formal accreditation.

Please note: free courses and courses that only include practice tests do not offer a certificate of completion.

What you'll learn

Student feedback

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Prasanth Nutheti

I knew nothing about ML and AI before starting this course. Now, I am very confident in solving problems. Looking for an advanced course now. Kirill and Hadelin are awesome

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Anuj Gupta

After going through courses on other platforms I realized that this course builds your foundation in Machine learning and not just tells you how to use the libs.

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Luca Passani

I think it's a good overview of a huge topic. I feel I am very far from being a Data Scientist, but at least I can understands the big picture and discuss AI/ML/NN in polite company without looking stupid 😉

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Jeff Colbeck

Lots of content and that part is great. Sometimes I was not able to match the results shown in the tutorial. Many people had the same problems but getting a clear response back in the Q&A section seems to be an issue.

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Adarsh Tripathy

It was very insightful and extensive. The only problems that i faced were that my queries weren't solved when i posted them. So if you could work on that, it would be perfect!!! Thank You

Who this course is for:

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