[100]%OFF Machine Learning A-Z™: Hands-On Python & R In Data Science

                                   

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

What you'll learn:

1)Master Machine Learning on Python & R and Have a great intuition of many Machine Learning models.

2)Make accurate predictions and Make powerful analysis.

3)Make robust Machine Learning models and Create strong added value to your business.

4)Use Machine Learning for personal purpose and 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.

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

Description:

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 SamplinPart 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

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