Machine Learning — From Basics to Advanced
Self-paced video training — 5 modules, 16 lessons. Work through it at your own pace — every lesson is real recorded training, not slide-reading.
▶INSIDE THE COURSE · 16 LESSONSABOUT THIS COURSE
This beginner course provides a comprehensive introduction to machine learning. Students will learn essential skills in data wrangling using NumPy and Pandas, explore linear models and decision trees for regression and classification, and delve into model evaluation and feature selection techniques. Additionally, the curriculum covers clustering methods like Naive Bayes and Nearest Neighbors, equipping learners with the foundational knowledge needed to build and evaluate machine learning models.
CURRICULUM
5 modules, in order.
01Introduction to Machine Learning Data Wrangling3 LESSONS
learn the basics of machine learning and understand its black box nature.
explore essential numpy functionalities for machine learning tasks.
discover essential pandas techniques for handling data in machine learning.
02Linear Models Trees Preprocessing3 LESSONS
understand linear models used for regression and classification in machine learning.
learn various preprocessing techniques using scikit-learn for machine learning.
explore decision trees and their application in machine learning.
03Model Evaluation Feature Selection Pipelining3 LESSONS
gain insights into model selection and evaluation methods in machine learning.
learn about feature selection techniques to improve machine learning models.
understand how to use composite estimators with pipelines and feature unions.
04Bayes Nearest Neighbours Clustering3 LESSONS
explore the naive bayes algorithm and its applications in machine learning.
learn about nearest neighbors and their role in machine learning.
discover techniques for conducting cluster analysis in machine learning.
05SVM Anomalies Imbalanced Classes Ensemble Methods4 LESSONS
understand anomaly detection techniques used in machine learning.
learn methods for handling imbalanced classes in machine learning.
explore support vector machines and their applications in machine learning.
understand ensemble methods and their use in improving machine learning models.
CURATED BY
Selected from experienced educators' training libraries and kept whole — modules, worked examples and walkthroughs exactly as taught.
GOOD TO KNOW
Lessons stream in the browser and remember where you stopped.
Watch on two devices at once — phone, tablet or desktop.
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$39 · ONE PAYMENT
Own every lesson, for life.
One payment unlocks all 16 lessons instantly — start with lesson one the moment you check out.