This course introduces learners to the fundamental understandings of machine learning – one of the hottest trends in modern computer science and engineering. Through the theoretical lectures and hands-on practices, students will learn how to analyze, pre-process data, build and apply machine learning models and algorithms to solve practical problems, e.g., to predict/classify future outcomes of academic and business data.
By the end of this course, students will achieve:
- Intermediate-Advanced programming skills with Python
- In-Depth understanding of Machine Learning algorithms (Regression, SVM, Decision Tree, K-Nearest Neighbor
- Knowledge and practical skills on how to analyze, pre-process, visualize data using advanced tools and libraries
- Understanding and hands-on experience in how to build Machine learning models, train the models with real datasets and apply them to solve practical problems (predictions of future outputs, classifications of future samples,…)
- Methods to calculate, visualize model errors, and evaluate the built Machine learning models
- Ability to work for Machine Learning projects
- Basic PC use
- Python basics (Optional)
Curriculum For This Course
- Python Basics
- Numpy, Pandas
- Simple Linear Regression
- Least Square Method
- Multiple Linear Regression
- Gradient Descent
- Logistic Regression
- Decision Tree
- Gini Index, Information Gain
- K-Nearest Neighbor
- Support Vector Machine
Below are the courses that you may want to take after this course:
Head of Artificial Intelligence and Data Science Department
- Data Science and Research Mentor at Tokyo Techies
- Master Graduate from Carnegie Mellon University.
- Researcher in an AI lab at a big global corporation.
- Global working experience in Singapore, Australia, US, Vietnam and Japan.
- A results-oriented Researcher and Data Guru with vast managerial and technical experience in financial management, marketing, business planning.
- Adapt at programming in multiple different languages, such as Java, Python and R.