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Machine Learning for Beginners
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Gain insights into ML fundamentals, foundational mathematics, coding models, and real-world apps. Discover making perceptrons and exploring scikit-learn for classification, regression, and clustering.

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Overview

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This course explains machine learning for absolute beginners by building a visual understanding of the underlying concepts. It covers some foundational mathematics behind the machine learning models and then guides you in coding for models to solve real-world machine learning problems. You’ll begin by understanding the limitations of traditional coding techniques in solving machine learning problems. Next, you’ll get familiar with the machine learning process. Then, you’ll build your first machine learning model from scratch—a single perceptron. The course then takes you from a single neuron to a multilayer perceptron to solve a non-linearly separable classification dataset. Finally, the course introduces Python’s library, scikit-learn, where you’ll learn to build models for classification, regression, and unsupervised clustering. This course aims to make you a lifelong learner and serves as a great starting point for a successful career in machine learning.

This course explains machine learning for absolute beginners by building a visual understanding of the underlying concepts. It c...Show More

WHAT YOU'LL LEARN

An understanding of the fundamentals of machine learning

The ability to build the model of a simple perceptron from scratch

Hands-on experience building a multilayer neural network from single neurons

Hands-on experience solving machine learning problems such as classification, regression, and clustering using Python and sklearn

An understanding of the fundamentals of machine learning

Content

18 Lessons5 Quizzes

The Machine Learning Problem

4 Lessons

Get familiar with human-like pattern recognition via machine learning techniques for image analysis.

Understanding Images Through Programming

How Do Humans Learn?

Problem-Solving Through Machine Learning

The Machine Learning Process Applied on Machine Learning Problems

The Machine Learning Process

5 Lessons

Grasp the fundamentals of data acquisition, modeling, training, prediction, and evaluation in machine learning.

From a Single Neuron to Artificial Neural Networks

4 Lessons

Work your way through non-linear data challenges, neural networks, gradient descent, and MLP using scikit-learn.

Data—Not Neatly Separable

Model—Artificial Neural Networks

Training—Learning Through Gradient Descent

The ML Process Using scikit-learn

Code for Machine Learning Using scikit-learn

4 Lessons

Apply your skills to using scikit-learn for multiclass classification, regression, clustering, and ML challenges.

Recognizing More Objects—Multiclass Classification

Prediction with Regression

Unsupervised Learning Through Clustering

ML Challenge

Concluding Thoughts

1 Lessons

Dig into the nuances of machine learning's place within the broader AI landscape.

The Place of ML in Artificial Intelligence (AI)

How to Predict the Traffic Volume Using Machine Learning

Project

Certificate of Completion

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Hands-on Learning Powered by AI

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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 Mock Interviews

Test your skills in a simulated interview setting. Receive personalized feedback based on your performance. Available for Coding Interviews, System Design, and more.

Adaptive Learning

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Explain with AI

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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!

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Machine Learning for Beginners Save

Machine Learning for Beginners
Save