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412 Bài học

Approximately 42.6h to complete

There are 1199 participants

## What you'll learn

Master Machine Learning on Python & R

Make accurate predictions

Make robust Machine Learning models

Use Machine Learning for personal purpose

Handle advanced techniques like Dimensionality Reduction

Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Have a great intuition of many Machine Learning models

Make powerful analysis

Create strong added value to your business

Handle specific topics like Reinforcement Learning, NLP and Deep Learning

Know which Machine Learning model to choose for each type of problem

## Course content

46 Sections

• 412 Lessons

• 42h 40m

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## Describe

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

This course has been designed by a **Data Scientist and a Machine Learning expert** so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

**Over 1 Million students** world-wide trust this course.

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 can be completed by either doing either the **Python tutorials, or R tutorials,** or both - Python & R. Pick the programming language that you need for your career.

This course is fun and exciting, and 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

Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and **learn what you need for your career right now**.

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

And last but not least, this course **includes both Python and R code templates **which you can download and use on your own projects.

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