MACHINE LEARNING COURCE

MACHINE LEARNING

Machine Learning and Artificial Intelligence (AI) are everywhere. The course is aimed to teach business professionals complex theory, algorithms and coding libraries in a simple way.

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers.

The course will be structured in the following way:

• Part 1: Data Preprocessing.
• Part 2: Regression.
• Part 3: 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).

By the end of the course, business professionals will be able to:

• Make accurate predictions.
• Make robust Machine Learning models.
• Use Machine Learning for personal purposes.
• Handle advanced techniques like Dimensionality Reduction.
• Build an army of powerful Machine Learning models and know how to combine them to solve any problem.
• Have a great intuition of many Machine Learning models.
• Make powerful analysis.
• Create strong added value to your business.
• Handle specific topics like Reinforcement Learning, NLP and Deep Learning.
• Know which Machine Learning model to choose for each type of problem.