Home>Courses>Fundamentals of Machine Learning: A Pythonic Introduction

Fundamentals of Machine Learning: A Pythonic Introduction

Gain insights into machine learning concepts, implement and compare algorithms with scikit-learn, and explore supervised learning, clustering, regression, SVMs, autoencoders, and ensemble learning with practical projects using Python.

Beginner

65 Lessons

14h

Certificate of Completion

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

6 Projects

148 Playgrounds

23 Quizzes

Course Overview

What You'll Learn

Course Content

Apply Your Skills

Recommendations

Course Overview

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, fo...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

Course Content

Course Overview

2 Lessons

Get familiar with foundational machine learning concepts, hands-on projects, and algorithm implementation.

Introduction to the Course

The Essentials of Machine Learning

Supervised Learning

10 Lessons

Get started with supervised learning, focusing on regression, classifiers, validation, and sklearn.

Inputs, Features, and Targets

Classifiers and Thresholds

Parameters

Regression

Overfitting and Underfitting

Occam’s Razor

Regularization

Ridge and Lasso Regression

Validation

Supervised Learning with Sklearn

Clustering

9 Lessons

Examine clustering techniques including k-means, DBSCAN, agglomerative clustering, and their practical applications.

Introduction to Clustering

K-Means Clustering

K-Means Walk-Through Example

DBSCAN

DBSCAN Walk-Through Example

Agglomerative Clustering

Agglomerative Clustering Walk-Through Example

Clustering Algorithms Comparison

Similarity and Dissimilarity Measures

Project: Bag of Visual Words

Project

Generalized Linear Regression

8 Lessons

Grasp the fundamentals of generalized linear regression, kernel methods, and feature transformations.

Feature Space

Generalized Linear Models

Generalized Linear Regression

Generalized Linear Regression for Multiple Targets

Kernels

Kernel Linear Regression

Kernel Linear Discriminant

Kernel Trick Can Be Dangerous

Face Recognition Using Kernel Linear Discriminant

Project

Support Vector Machine

8 Lessons

Dig into Support Vector Machines for classification, leveraging hyperplanes, kernels, and optimization techniques.

Introduction to SVM

Hard-Margin SVM

Identifying Support Vectors

Linear Separability

Soft-Margin SVM

Dual Formulation

Kernel SVM and Sparsity

SVM in Sklearn

Logistic Regression

8 Lessons

Investigate logistic regression, BCE optimization, kernel methods, multiclass extension, and neural network transition.

Overview of Probabilistic Models

Introduction to Logistic Regression

Optimizing BCE Loss

Kernel Logistic Regression

Multiclass Formulation

From Logistic Regression to Neural Networks

Implementation of a Neural Network

Quiz on Logistic Regression

Ensemble Learning

8 Lessons

Master the fundamentals of ensemble learning and explore techniques to enhance predictive accuracy.

Introduction to Ensemble Learning

Types of Ensemble Learning

Bagging

Decision Trees

Random Forest

Gradient Boosting: Implementation from Scratch

Gradient Boosting: Implementation Using Scikit-learn

AdaBoost

Early Stage Diabetes Prediction Using Ensemble Learning

Project

11.

Decoding Dimensions: PCA and Autoencoders

5 Lessons

Solve problems in dimensionality reduction using PCA, Autoencoders, and VAEs.

Principal Component Analysis

Autoencoders

Autoencoders in Action

Variational Autoencoders

VAEs in Action

Image Reconstruction Using PCA

Project

Image Colorization using Autoencoders

Project

Colorful Face Generation with VAEs

Project

15.

Appendix

6 Lessons

Get started with CVXPY, mathematical and convex optimization, gradient descent, and Lagrangian duality.

Introduction to CVXPY

Mathematical Optimization

Convex Optimization

Convex Optimization Problems

Lagrangian Duality

Gradient Descent

16.

Wrapping Up

1 Lessons

Examine the comprehensive introduction to machine learning using Python and practical applications.

Conclusion

Trusted by 2.5 million developers working at companies

"These are high-quality courses. Trust me. I own around 10 and the price is worth it for the content quality. EducativeInc came at the right time in my career. I'm understanding topics better than with any book or online video tutorial I've done. Truly made for developers. Thanks"

Anthony Walker

@_webarchitect_

"Just finished my first full #ML course: Machine learning for Software Engineers from Educative, Inc. ... Highly recommend!"

Evan Dunbar

ML Engineer

"You guys are the gold standard of crash-courses... Narrow enough that it doesn't need years of study or a full blown book to get the gist, but broad enough that an afternoon of Googling doesn't cut it."

Carlos Matias La Borde

Software Developer

"I spend my days and nights on Educative. It is indispensable. It is such a unique and reader-friendly site"

Souvik Kundu

Front-end Developer

"Your courses are simply awesome, the depth they go into and the breadth of coverage is so good that I don't have to refer to 10 different websites looking for interview topics and content."

Vinay Krishnaiah

Software Developer

"I've tried probably 5-7 different sites and Educative is easily the best. It perfectly blends explanation with interactivity"

Eric Downs

Musician/Entrepeneur

Hands-on Learning Powered by AI

See how Educative uses AI to make your learning more immersive than ever before.

Instant Code Feedback

Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

AI-Powered Mock Interviews

Put your skills to the test in a simulated interview setting. Receive personalized feedback based on your performance. Available in Premium & Premium Plus plans.

Adaptive Learning

At various checkpoints throughout Educative courses, you will be prompted to take a quick assessment. Receive a condensed curriculum tailored to your strengths and skill gaps.

Explain with AI

Select any text within any Educative course, and get an instant explanation — without ever leaving your browser.

AI Code Mentor

AI Code Mentor helps you quickly identify errors in your code, learn from your mistakes, and nudge you in the right direction — just like a 1:1 tutor!

Recommended before starting this course

Course

Linear Algebra for Data Science Using Python

Gain insights into linear algebra essentials for data science, focusing on vectors, matrices, and tensors. Explore practical Python applications, engaging visuals, real datasets, and hands-on projects.

beginner

10 hours

Course

Python 3: From Beginner to Advanced

Gain insights into Python by exploring basic to advanced concepts, including loops, classes, and GUIs with Tkinter. Delve into testing and become a productive Python programmer.

beginner

4 hours

Course

Grokking Data Science

Gain insights into data science with easy-to-follow, hands-on explanations. Explore essential concepts quickly and efficiently, even without prior statistics knowledge, for a career boost.

beginner

10 hours

Course

Linear Algebra for Data Science Using Python

beginner

10 hour

Course

Python 3: From Beginner to Advanced

beginner

4 hour

Course

Grokking Data Science

beginner

10 hour

Related Courses and Skill Paths

Course

Natural Language Processing with Machine Learning

Gain insights into processing text data, creating word embeddings, and using LSTMs for semantic analysis and machine translation. Explore industry-relevant NLP techniques with Python and TensorFlow.

advanced

9 hours

Course

Image Recognition with Machine Learning

Gain insights into image data processing and CNNs for image classification using TensorFlow. Delve into CNN architectures and their applications, requiring Python and TensorFlow knowledge.

advanced

7 hours

Course

Introduction to Deep Learning & Neural Networks

Gain insights into basic and intermediate deep learning concepts, including CNNs, RNNs, GANs, and transformers. Delve into fundamental architectures to enhance your machine learning model training skills.

intermediate

4 hours 30 mins

Course

Natural Language Processing with Machine Learning

advanced

9 hour

Course

Image Recognition with Machine Learning

advanced

7 hour

Course

Introduction to Deep Learning & Neural Networks