Interested in the Machine Learning sector? This course is for you and that!
Two experienced data scientists have developed this course so that we can share our expertise and help you to learn complex library theories, algorithms, and coding in an easy way.
We're going to walk you step-by-step into the Machine Learning World. You will learn new abilities with every tutorial and boost your understanding of this challenging but valuable Data Science sub-field.
This course is very interesting and exciting way, but we start digging into Machine Learning at the same time. The following manner is structured way:
Phase 1 - Preprocessing Data
Phase 2 - Regression: Simple Linear Regression, Multiple Linear Regression, SVR, Decision Tree Regression, Random Forest Regression, Polynomial Regression
Phase 3 - Classification: Logistic Regression, K-NN, SVM, SVM Kernel, Naive Bayes, Classification Decision Tree, Random Forest Classification
Phase 4 - Clustering: K-Means, Clustering in Hierarchy
Phase 5 - The Learning of the Association Rule:
Phase 6 - Learning for Reinforcement: Upper Confidence Bound, Thompson Sampling
Phase 7 - Production of natural language
Phase 8 - Deep learning: Convolutionary Neural Networks, Artificial Neural Networks
Phase 9 - Reduction of Dimensionality: PCA, LDA, Kernel PCA
Phase 10 - Collection & Boosting of Models: k-fold Cross Validation, Tuning of Parameters, Grid Scan, XGBoost
In addition, the course is filled with practical lessons that are based on examples from real life. But not only are you going to learn the theory, you're going to get some hands-on experience designing your own models as well.
And as a bonus, this course contains templates of Python and R code that can be downloaded and used on your own projects.
Relevant (June 2020) updates:
ALL CODES UP TO DATE