Machine Learning A-Z™: Python & R in Data Science [2022]

Udemy Free Discount - Machine Learning A-Z™: Python & R in Data Science [2022], Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

• BESTSELLER
• 4.5 (92,658 ratings)
• Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, SuperDataScience Support
•  English [Auto-generated], French [Auto-generated], 7 more

PREVIEW THIS COURSE - GET COUPON CODE

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

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 which 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.