The Machine Learning for Business course provides a comprehensive introduction to the dynamic field of machine learning. We are living in an age where data is growing exponentially, and the ability to harness this data through machine learning offers organizations a competitive edge in their respective domains.

This course introduces the foundations of various types of machine learning, including:

1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
4. Deep Learning

We aim to provide a thorough overview of the most commonly used algorithms in practice, using real-world examples and datasets. The "black box" of Machine Learning is opened up to you, helping you understand in detail how it works, its capabilities, and limitations.

The course is structured with lectures and practical coding exercises. We do not require prior knowledge beyond a general understanding of programming.

The main emphasis of this course is understanding the actual methods that power Artificial Intelligence (AI) applications, along with their implementation in Python. The main learning outcomes, both theoretical and practical, are as follows:

1. Understand the machine learning workflow of training, testing, and prediction.
2. Become familiar with well-established machine learning methods for regression and classification.
3. Understand the fundamentals of neural networks for image recognition and natural language processing (NLP).
4. Have a solid understanding of the capabilities and limitations of the main ML algorithms.
5. Be able to select the right algorithm for a given problem and implement it in Python.

At the end of the course, you should be able to confidently interact with ML programmers and discuss the merits and limitations of the main ML algorithms currently in use.

1. Get Started Before the Course Begins: Introduction to Notebooks, Python, and Data Exploration
2. Class 1: Introduction to ML and Linear Regression
3. Class 2: Binary Classification: Logistic Regression and Measures of Performance
4. Class 3: Binary Classification: Decision Trees and Ensemble Algorithms
5. Class 4: Unsupervised Learning
6. Class 5: Neural Networks and Deep Learning
7. Class 6: Image Recognition and CNNs
8. Class 7: Natural Language Processing and Generative AI
9. Class 8: Reinforcement Learning and the Future of ML