About this course
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries.
- Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction.
- Know the differences between various core Machine Learning models.
- Understand the Machine Learning modelling workflows.
- Use Python and scikit-learn to process real datasets, train and apply Machine Learning models
- Either Learn to Program: Python, Data Manipulation in Python and Introduction to ML using Python: Introduction & Linear Regression or Learn to Program: Python, Data Manipulation and Visualisation in Python and Introduction to ML using Python: Introduction & Linear Regression needed to attend this course. If you already have experience with programming, please check the topics covered in the Learn to Program: Python, Data Manipulation in Python, Data Manipulation and Visualisation in Python and Introduction to ML using Python: Introduction & Linear Regression courses to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training.
- Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.