Name: [Udemy Course] Machine Learning A-Z™ - Hands-On Python & R In Data Science by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, SuperDataScience Support
SKU: 5f7fcfff080100003b462595
Price: 15.0 MYR
Availability: InStock

Description

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

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!

💢💢💢💢💢💢💢💢

Product Details: https://bit.ly/34DyoIf

File size: 6.66GB

Payment and delivery:

1. Please provide your EMAIL address in “message:” during checkout.

2. The download link will be emailed to you after payment has been confirmed.

🔥 All files will be delivered through Google Drive.

🔥 Lifetime Access

Kindly PM us if you are looking for other ebooks/ Video Courses.

Enjoy learning!

[Udemy Course] Machine Learning A-Z™ - Hands-On Python & R In Data Science by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, SuperDataScience Support

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