Hands-On Python and R In Data Science

The Udemy course “Machine Learning A-Z™: Hands-On Python & R In Data Science” has been designed by two professional Data Scientists so that they can share their knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. They 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 dives deep into Machine Learning. And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.

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

In this course, you will learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates are included. You will be able to make powerful analysis, make robust Machine Learning models, create strong added value to your business and know which Machine Learning model to choose for each type of problem.

The course comprises 231 Lectures organized into the following ten parts:

————————– Part 1: Data Preprocessing ————————–

For Python learners, summary of Object-oriented programming: classes & objects
Data Preprocessing Template

—————————— Part 2: Regression ——————————

Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Support Vector Regression (SVR)
Decision Tree Regression
Random Forest Regression
Evaluating Regression Models Performance

—————————- Part 3: Classification —————————-

Logistic Regression
K-Nearest Neighbors (K-NN)
Support Vector Machine (SVM)
Kernel SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
Evaluating Classification Models Performance

—————————- Part 4: Clustering —————————-

K-Means Clustering
Hierarchical Clustering

———————- Part 5: Association Rule Learning ———————-

Apriori
Eclat

———————— Part 6: Reinforcement Learning ————————

Upper Confidence Bound (UCB)
Thompson Sampling

——————— Part 7: Natural Language Processing ———————

Natural Language Processing in Python
Natural Language Processing in R

—————————- Part 8: Deep Learning —————————-

Artificial Neural Networks
Convolutional Neural Networks

———————– Part 9: Dimensionality Reduction ———————–

Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Kernel PCA

——————— Part 10: Model Selection & Boosting ———————

Model Selection
XGBoost
Bonus Lectures

Requirements

Just some high school mathematics level

Summary of Main Course Features

Instructor: Kirill Eremenko
On-demand video: 36 hours
Articles: 19
Supplemental Resources: 71
Includes:
- 30-Day Money-Back Guarantee
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion