Machine Learning is more than just algorithms, it's a way of thinking. This course introduces learners to the essential concepts and methods that power intelligent systems in the real world. Blending theory with hands-on practice, students will learn how machines recognize patterns, make predictions, and improve through experience. Covering core techniques in both supervised and unsupervised learning, like linear regression, decision trees, and clustering, this course equips learners with the skills to train, test, and evaluate models using real-world data. But more importantly, it challenges students to go beyond the code: to think critically about when, why, and how to apply these tools across industries such as healthcare, retail, finance, and climate science and other domains. By the end of this course, students will not only be able to build machine learning models, but also tell meaningful stories with them, bridging the gap between technical insight and impactful, ethical decision-making. Format
Self-paced and on-demand. Certificate available. Learning Outcomes or Competencies
By the end of this course, learners will be able to:
- Explain core machine learning concepts and distinguish between supervised and unsupervised learning.
- Build and evaluate models using algorithms like linear regression, logistic regression, decision trees, and clustering.
- Perform feature engineering and data preprocessing to prepare real-world datasets for modeling.
- Tune and validate models using cross-validation, hyperparameter search, and interpretability tools.
- Apply ML techniques to practical domains such as healthcare, retail, and climate science.
- Communicate insights effectively through data storytelling while considering fairness and ethical implications.
Background Knowledge Required
- Basic Python programming (variables, functions, loops, libraries like NumPy or pandas)
- Introductory statistics and linear algebra (mean, variance, vectors, matrices)
- Familiarity with Jupyter Notebook or any coding environment
Number of Learning Modules/Sections
There are 5 learning modules in this course.
- Foundations of Machine Learning
- Feature Engineering and Data Preparation
- Supervised Learning Algorithms
- Unsupervised Learning Alogirthms
- Machine Learning in the Real World
Instructor
Somita Chaudhari
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Cost
Free for learn
Certificate Cost: 100 USD
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