Machine Learning A-Z: Hands-On Python & R In Data Science

machine learning with python


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 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.

Course Information


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