Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
Would you like to know the machine? So it’s for you this semester!
Two trained data scientists have developed this course to enable us share our experience and understand complicated science, algorithms and libraries easily.
We walk into the field of machine learning step by step. You learn different skills and appreciate this demanding yet lucrative aspect of the data science with each tutorial.
This course is simple and enjoyable, while at the same time we immerse ourselves in machine learning. The following form is structured:
- 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
In fact, the course provides activities focused on real-life scenarios. So you’ll not only study theory, but also know how to create your own versions.
Also, as a bonus, you can access and use Python and R application models for your own designs.