Designed to help you learn complex theory, algorithms and coding libraries in a simple way.

The course is packed with practical exercises which 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.

What Will I 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

Syllabus:

•  1 - Data Preprocessing
•  2 - Regression
•  3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
•  4 - Clustering: K-Means, Hierarchical Clustering
•  5 - Natural Language Processing: Bag-of-words model and algorithms for NLP
•  6 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
•  7 - Dimensionality Reduction: PCA, LDA, Kernel PCA
•  8 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost.