Fundamentals of Machine Learning for Software Engineers

Explore machine learning's essentials for software engineers, delve into supervised learning, neural networks, and deep learning, and gain skills to tackle real-world data challenges effectively.

Beginner

93 Lessons

15h

Certificate of Completion

Explore machine learning's essentials for software engineers, delve into supervised learning, neural networks, and deep learning, and gain skills to tackle real-world data challenges effectively.

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

Get familiar with supervised and unsupervised learning, neural networks, and machine learning basics.

Introduction Programming versus Machine Learning Supervised Learning The Math Behind Machine Learning

Our First Learning Program

Get started with coding, training, and optimizing a linear regression model for sales prediction.

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

Work your way through gradient descent, optimizing parameters, and addressing overshooting issues.

The Limitations of Linear Regression

Gradient Descent Partial Derivatives Put Gradient Descent to the Test Playground (Basecamp Overshooting)

Hyperspace

Grasp the fundamentals of managing multi-dimensional data, matrix operations, and implementing multiple linear regression.

Add More Dimensions Matrix Math Upgrade the Learner Put It All Together Playground (Field Statistician)Quiz: The Gradient Descent

A Discern Machine

Take a closer look at binary classification using logistic regression and gradient descent techniques.

Linear Regression Limitation

Invasion of the Sigmoids Update the Gradient Classification in Action Playground (Weighty Decisions)

Get Real

5 Lessons

Investigate the importance of data, binary classification, and practical digit recognition techniques.

The Final Challenge

5 Lessons

Practice using multi-class classifiers, one-hot encoding, and classifier decoding for effective machine learning.

The Perceptron

4 Lessons

Break down the perceptron's role, limitations, and historical significance in AI development.

Designing the Network

2 Lessons

Get started with designing and understanding neural network architectures and key functions like Softmax.

10.

Building the Network

4 Lessons

Break apart the neural network process, from forward propagation to cross-entropy loss, for effective training.

11.

Training the Network

7 Lessons

Apply your skills to train neural networks using backpropagation, weight initialization, and effective iteration.

12.

How Classifiers Work

3 Lessons

Dig deeper into classifiers' decision boundaries and neural networks' flexibility through coding exercises.

13.

Batchin’ Up

4 Lessons

Follow the process of optimizing mini-batch gradient descent to enhance neural network training.

14.

The Zen of Testing

3 Lessons

Build on overfitting prevention, neural network development cycle, and dataset splitting issues.

15.

Let’s Do Development

6 Lessons

Step through development stages, from data preparation to optimizing neural network performance.

16.

A Deeper Kind of Network

5 Lessons

Unpack the core of neural network depth, Keras implementation, and performance balancing.

Diabetes Prediction Using Keras

Project

17.

Defeating Overfitting

6 Lessons

Explore strategies to combat overfitting in machine learning, improve model performance.

18.

Taming Deep Networks

5 Lessons

Apply your skills to manage activation functions, weight initialization, and optimization in deep networks.

19.

Beyond Vanilla Networks

5 Lessons

Map out the steps for advanced techniques in CNNs and CIFAR-10 for image recognition.

20.

Into the Deep

3 Lessons

Focus on deep learning's rise, its powerful pattern recognition, and future machine learning paths.

Recognize Handwritten Digits Using a Deep Neural Network

Project

20.

Machine Learning Fundamentals

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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