About This Course

This course aims to equip learners with the abilities to handle, analyze, and manipulate the vast amounts of data using Python in the realm of data science. It introduces fundamental concepts of Machine Learning (ML) and Artificial Intelligence (AI) through hands-on practice and challenging real-world applications, utilizing vital programming tools and libraries.

What Students Will Learn

• Hands-on experience in solving data science problems using Python.
• Proficiency in Python programming related to data modeling, statistics, and presentation.
• Exploration and implementation of popular Python libraries like Pandas, numPy, matplotlib, and sklearn.
• Ability to develop, assess, and deploy basic machine learning models to tackle practical issues.
• Foundational preparation for advanced studies in machine learning and artificial intelligence.

Prerequisites or Skills Necessary

Participants are expected to possess a baseline understanding of programming, particularly in Python, and foundational knowledge of statistics. Preliminary Python skills can be gained from CS50's Introduction to Programming with Python, and baseline statistical knowledge from courses such as Fat Chance or Stat110 from HarvardX.

Course Coverage

• Use of Python for developing various regression and classification models.
• Implementation techniques involving machine learning libraries like sklearn.
• Statistical analysis techniques in Python for live data interpretation.
• Real-world problem solving using machine learning algorithms.

Who This Course is For

This course is intended for individuals looking to enhance their technical proficiency in data science using Python, including students and professionals who aim to understand machine learning applications and data handling at a practical level.

Real-World Application of Skills

Skills acquired from this course can be applied in numerous real-world settings, such as in tech industries handling big data, academic research requiring detailed data analysis, or in any sector where predictive analysis and data-driven decision-making are valuable.

Syllabus

1. Introduction to Linear Regression
2. Exploring Multiple and Polynomial Regression
3. Techniques in Model Selection and Cross-Validation
4. Understanding Bias, Variance, and Hyperparameters
5. Classification Models and Logistic Regression
6. Advanced Topics: Multi-logistic Regression and Data Completeness
7. Statistical Methods: Bootstrap, Confidence Intervals, and Testing
8. Capstone Project: Application of Acquired Skills