Machine Learning: Theory and Hands-on Practice with Python

Description

In this course, you will :

• Examine a number of classic Supervised and Unsupervised Learning algorithms, as well as some introductory Deep Learning topics.
• Create and evaluate Machine Learning models using popular Python libraries, and compare the strengths and weaknesses of each algorithm.
• Explain which Machine Learning models would be best to use for a Machine Learning task based on the properties of the data.
• Tune hyperparameters and use techniques like sampling and regularisation to improve model performance.

Syllabus :

• Introduction to Machine Learning: Supervised Learning
• Unsupervised Algorithms in Machine Learning
• Introduction to Deep Learning