Course Description
The Data Science Intermediate training program is meticulously designed to provide participants with an in-depth understanding and practical experience in Machine Learning using Python. Throughout the program, participants will delve into advanced concepts and techniques in both supervised and unsupervised learning. The training is carefully crafted to empower participants to develop sophisticated machine learning models tailored for real-world scenarios. By leveraging hands-on exercises, case studies, and interactive sessions, participants will gain the expertise needed to build, evaluate, and optimize machine learning models effectively.
Level
Intermediate
Duration
5 days (9.00am – 5.00pm)
Training Methodology
i. Interactive lecture
ii. Hands-on practice
iii. Case-based learning
iv. Q&A with tool demonstration
v. Group discussion
Requirement
i. Basic computer literacy
ii. Good command of english
iii. No prior experience in data science required
iv. Participants must have access to a laptop or computer with a stable internet connection throughout the training sessions
v. It is mandatory to have Jupyter Notebook installed on the computer before the training begins.
vi. Participants are required to have a solid understanding of Python programming, including data structures, loops, functions, and libraries such as NumPy and pandas.
Learning outcomes
i. Understand the principles and algorithms of supervised and unsupervised learning
ii. Learn how to preprocess and clean data for machine learning tasks
iii. Build and evaluate supervised learning
iv. Implement unsupervised learning algorithms
v. Explore techniques for evaluating and optimizing machine learning models for improved performance
Course outline
i. Introduction to Machine Learning
ii. Supervised Learning Techniques
iii. Model Evaluation and Optimization
iv. Unsupervised Learning Techniques
v. Advanced Machine Learning Concepts
vi. Real World Applications of Machine Learning
vii. Hands-on Projects and Practical Exercises
Lessons:
