Learn the foundations of machine learning using Python. The course introduces data preparation, supervised and unsupervised learning, model evaluation, feature engineering, practical projects, and responsible use of artificial intelligence.
This beginner-friendly course introduces learners to the fundamental concepts and practical techniques used in machine learning. Students will learn how computers identify patterns from data and use those patterns to make predictions or support decisions.
The course covers data collection, data cleaning, exploratory data analysis, feature preparation, model training, model evaluation, and interpretation of results. Learners will work with Python libraries such as NumPy, pandas, Matplotlib, and scikit-learn.
Practical examples will include predicting house prices, classifying health-related observations, identifying customer groups, and evaluating the performance of machine-learning models. By the end of the course, students will be able to prepare a dataset, train basic machine-learning models, compare their performance, and explain the results.
Introduces artificial intelligence, machine learning, datasets, models, features, labels, and common applications.
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Shija
MITSOL learning instructor