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
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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
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