Machine Learning A-Z™ – Hands-On Python & R In Data Science by Kirill Eremenko, Hadelin de Ponteves, Sup

Salepage : Machine Learning A-Z™ – Hands-On Python & R In Data Science by Kirill Eremenko, Hadelin de Ponteves, Sup
Archive : Machine Learning A-Z™ – Hands-On Python & R In Data Science by Kirill Eremenko, Hadelin de Ponteves, Sup Digital Download
Delivery : Digital Download Immediately

What you’ll discover

Learn Python and R Machine Learning.

Have a strong understanding of numerous Machine Learning models.

Make precise projections

Create a strong analysis

Create strong Machine Learning models.

Create significant additional value for your company.

Use Machine Learning for your own benefit.

Handle specialized subjects such as Reinforcement Learning, NLP, and Deep Learning.

Master advanced methods such as Dimensionality Reduction.

Understand the Machine Learning model to use for each problem category.

Create a formidable army of Machine Learning models and understand how to combine them to tackle any problem.

Are you interested in Machine Learning? Then this is the course for you!

This course was created by two expert Data Scientists to share our knowledge and assist you in learning difficult theory, algorithms, and code libraries in a straightforward manner.

We will guide you through the world of machine learning step by step. With each session, you will learn new abilities and get a better grasp of this difficult yet profitable topic of Data Science.

This course is entertaining and thrilling, but it also delves deeply into Machine Learning. It is organized in the following manner:

Part 1: Data Preparation

Regression (Part 2): Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

Classification in Part 3: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Part 4 – Clustering Techniques: K-Means and Hierarchical Clustering

Part 5 – Learning Association Rules: Apriori, Eclat

Reinforcement Learning: Upper Confidence Bound, Thompson Sampling, Part 6

Part 7 – Natural Language Processing: The Bag-of-Words Model and NLP Algorithms

Deep Learning in Part 8: Artificial Neural Networks and Convolutional Neural Networks

Part 9 – Dimensionality Reduction Techniques: PCA, LDA, and Kernel PCA

Model Selection and Boosting (Part 10): k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Furthermore, the course is jam-packed with practical activities based on real-life experiences. So you’ll not only study the theory, but you’ll also get some hands-on experience developing your own models.

This course also provides Python and R code templates that you can download and use on your own projects as a bonus.

Important revisions (June 2020):

ALL CODES ARE CURRENT

TENSORFLOW 2.0 CODED DEEP LEARNING

XGBOOST AND CATBOOST ARE TWO OF THE BEST GRADIENT BOOSTING MODELS!

Size of the file: 6.66GB

More from Categories : Hypnosis & NPL