Beginner
14h
Updated this week
Fundamentals of Machine Learning: A Pythonic Introduction
Learn machine learning with scikit-learn, covering supervised learning, clustering, regression, SVMs, autoencoders, and ensemble methods through practical Python projects.
This course focuses on core concepts, algorithms, and machine learning techniques. It explores the fundamentals, implements algorithms from scratch, and compares the results with scikit-learn, the Python machine learning library. This course contains examples, theoretical knowledge, and codes for various ML algorithms.
You’ll start by learning the essentials of machine learning and its applications. Then, you’ll learn about supervised learning, clustering, and constructing a bag of visual words project, followed by generalized linear regression, support vector machines, logistic regression, ensemble learning, and principal component analysis. You’ll also learn about autoencoders and variational autoencoders and end with three exciting projects.
By the end, you’ll have a solid understanding of machine learning and its algorithms, hands-on experience implementing such algorithms and applying them to different problems, and an understanding of how each algorithm works with the provided examples.
This course focuses on core concepts, algorithms, and machine learning techniques. It explores the fundamentals, implements algo...Show More
WHAT YOU'LL LEARN
An understanding of the fundamental machine learning algorithms
Proficiency in strong problem-solving skills through hands-on projects
Working knowledge of applying machine learning algorithms to real-world datasets, addressing classification, regression, clustering, and dimensionality reduction tasks
Hands-on experience assessing and comparing the performance of machine learning models
An understanding of the fundamental machine learning algorithms
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Content
1.
Course Overview
3 Lessons
Get familiar with foundational machine learning concepts, hands-on projects, and algorithm implementation.
2.
Supervised Learning
10 Lessons
Get started with supervised learning, focusing on regression, classifiers, validation, and sklearn.
3.
Clustering
9 Lessons
Examine clustering techniques including k-means, DBSCAN, agglomerative clustering, and their practical applications.
4.
Generalized Linear Regression
8 Lessons
Grasp the fundamentals of generalized linear regression, kernel methods, and feature transformations.
5.
Support Vector Machine
8 Lessons
Dig into Support Vector Machines for classification, leveraging hyperplanes, kernels, and optimization techniques.
6.
Logistic Regression
8 Lessons
Investigate logistic regression, BCE optimization, kernel methods, multiclass extension, and neural network transition.
7.
Ensemble Learning
8 Lessons
Master the fundamentals of ensemble learning and explore techniques to enhance predictive accuracy.
8.
Decoding Dimensions: PCA and Autoencoders
5 Lessons
Solve problems in dimensionality reduction using PCA, Autoencoders, and VAEs.
9.
Appendix
6 Lessons
Get started with CVXPY, mathematical and convex optimization, gradient descent, and Lagrangian duality.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
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