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 isfun 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,PolynomialRegression
Part 3 - Classification: Logistic Regression,SVM, Kernel SVM, Naive Bayes, Decision Tree Classification,RandomForest Classification
Part 4 - Clustering: K-Means
Part 5 - Association Rule Learning: Apriori
Part 6 - Reinforcement Learning:Upper Confidence Bound,Thompson Sampling
Part 7 - Natural Language Processing: Bag-of-words modelandalgorithms for NLP
Part 8 - Deep Learning: Artificial Neural Networks,Convolutional Neural Networks
Part 9 - Dimensionality Reduction: PCA, Kernel PCA
Part 10 - Model Selection & Boosting: k-fold Cross Validation, Grid Search.
Moreover, the course is packed with practical exercises which are based on real-lifeexamples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.
And as a bonus, this course includes bothPython and Rcode templates which you can download and use on your own projects.