Fundamentals of Machine Learning for Software Engineers

The course will help you understand machine learning fundamentals by diving into theoretical concepts and algorithms of machine learning.

Beginner

93 Lessons

15h

Certificate of Completion

The course will help you understand machine learning fundamentals by diving into theoretical concepts and algorithms of machine learning.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

65 Playgrounds

10 Quizzes

This course includes

65 Playgrounds

10 Quizzes

Course Overview

Machine learning is the future for the next generation of software professionals. This course serves as a guide to machine learning for software engineers. You’ll be introduced to three of the most relevant components of the AI/ML discipline; supervised learning, neural networks, and deep learning. You’ll grasp the differences between traditional programming and machine learning by hands-on development in supervised learning before building out complex distributed applications with neural networks. You’ll ...Show More

TAKEAWAY SKILLS

Python

Machine Learning

Neural Networks

What You'll Learn

Working knowledge of modern machine learning techniques

A strong understanding of neural networks

The ability to program behavior rather than processes in supervised learning systems

Familiarity with complex artificial intelligence and deep learning

The experience of managing real-world datasets with machine learning

What You'll Learn

Working knowledge of modern machine learning techniques

Course Content

How Machine Learning Works

This course simplifies ML for software engineers, focusing on supervised learning, neural networks and deep learning through practical coding examples.

Introduction

Programming versus Machine Learning

Supervised Learning

The Math Behind Machine Learning

Our First Learning Program

This chapter covers building a sales prediction program through linear regression, model training and parameters adjustment to improve model evaluation.

Get to Know the Problem

Coding Linear Regression

Training

Add a Bias

Playground (Tweak the Learning Rate)

Quiz: Basics of Machine Learning

Walking the Gradient

This chapter explores gradient descent, optimizing its efficiency, and challenges such as over shooting when minimizing loss for machine learning models.

The Limitations of Linear Regression

Gradient Descent

Partial Derivatives

Put Gradient Descent to the Test

Playground (Basecamp Overshooting)

Hyperspace

This chapter emphasizes the importance of multi-dimensional data in machine learning, and extending it to the linear regression problem.

Playground (Field Statistician)

Quiz: The Gradient Descent

A Discern Machine

This chapter emphasizes transitioning from linear regression to logistic regression for binary classification and significance of log loss function.

Linear Regression Limitation

Invasion of the Sigmoids

Update the Gradient

Classification in Action

Playground (Weighty Decisions)

Get Real

5 Lessons

This chapter explores the role of data, such as MNIST, in developing machine learning models for digit recognition, and measuring model accuracy with test data.

The Final Challenge

5 Lessons

This chapter delves into multi-class classification, one-hot encoding, and implements a classifier to reinforce the concepts learned up till now.

The Perceptron

4 Lessons

This chapter talks about perceptrons as foundational in supervised learning, and explains why we need more sophisticated models such as neural networks.

Designing the Network

2 Lessons

This chapter discusses constructing neural networks, emphasizing their complexity over perceptrons, and introduces the softmax activation function.

10.

Building the Network

4 Lessons

This chapter focuses on implementing forward propagation in neural networks and using cross-entropy loss for better classification.

11.

Training the Network

7 Lessons

This chapter makes a case for backpropagation for training neural networks, covers its foundations, and emphasizes weight initialization for best results.

12.

How Classifiers Work

3 Lessons

This chapter explores classifiers through decision boundaries, showcasing the benefits of neural networks over perceptrons in handling complex data separations.

13.

Batchin’ Up

4 Lessons

This chapter focuses on optimizing training efficiency in machine learning through mini-batch gradient descent, emphasizing performance impact of batch sizes.

14.

The Zen of Testing

3 Lessons

This chapter shows the importance of avoiding overfitting in machine learning models through careful dataset management and validation practices.

15.

Let’s Do Development

6 Lessons

This chapter builds a model, covering data preparation, hyperparameter tuning, and optimization strategies for enhancing performance and accuracy.

16.

A Deeper Kind of Network

5 Lessons

This chapter explores deep learning concepts through the Echidna dataset and Keras library for creating and optimizing neural networks.

17.

Defeating Overfitting

6 Lessons

This chapter emphasizes strategies to overcome overfitting in machine learning, regularization techniques and the importance of model complexity management.

18.

Taming Deep Networks

5 Lessons

This chapter explores key concepts in deep learning, focusing on activation functions, network optimization techniques and practical experiments for improvement

19.

Beyond Vanilla Networks

5 Lessons

This chapter explores advanced neural networks, particularly CNNs, and reveals their effectiveness in image classification on complex datasets like CIFAR-10.

20.

Into the Deep

3 Lessons

This chapter looks at the evolution of deep learning, explains why they have proved highly effective, and mentions exciting opportunities in this domain.

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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Adaptive Learning

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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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