Data Science and Machine Learning
Build practical capability through guided lessons, exercises and real-world projects.
Build understanding you can apply.
This course introduces students to the world of Data Science and Machine Learning, providing the skills needed to analyze large datasets and build predictive models. Students will learn how to use tools like Python, pandas, and machine learning libraries (e.g., scikit-learn) to clean, explore, and model data. By the end of the course, students will be able to apply data science techniques and machine learning algorithms to solve real-world problems. What Students Will Learn: Fundamentals of data science and the data analysis pipeline Data cleaning, preprocessing, and exploration with pandas Introduction to machine learning concepts and algorithms Supervised learning techniques (e.g., regression, classification) Unsupervised learning techniques (e.g., clustering, dimensionality reduction) Evaluating model performance and fine-tuning models Using Python libraries like scikit-learn, matplotlib, and seaborn Introduction to deep learning and neural networks (optional) By the end of the course, students will be able to analyze data, build machine learning models, and gain insights to solve complex problems in fields like business, healthcare, and technology.
- Instructor-led practical sessions
- Hands-on exercises and projects
- Guidance and feedback
- Real-world problem solving
More than a lecture.
