Understand the importance of machine learning explainability. Learn to interpret and explain complex ML models using techniques like SHAP, LIME, and other methods to ensure transparency and trust in AI predictions.

A complete guide to master machine learning concepts and create real world ML solutions

Understand the fundamentals of Machine Learning and how it's applied in the business world.

Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.

In this course you'll learn how to use data science for several common marketing tasks.

From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.

Learn how to apply machine learning to sequential data, such as time series or text. Master algorithms like RNNs and LSTMs to model dependencies and make predictions based on sequential patterns in data, from stock prices to natural language.

This course focuses on feature engineering and machine learning for time series data.

Discover how to apply machine learning techniques to trading strategies and financial markets.

Explore machine learning techniques for developing trading algorithms and strategies in financial markets.

Apply machine learning techniques to solve physics problems

This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases.