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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.
4.6
93 Lessons
2 Projects
15h
Updated 4 weeks ago
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- 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
Learning Roadmap
1.
How Machine Learning Works
How Machine Learning Works
Get familiar with supervised and unsupervised learning, neural networks, and machine learning basics.
2.
Our First Learning Program
Our First Learning Program
Get started with coding, training, and optimizing a linear regression model for sales prediction.
3.
Walking the Gradient
Walking the Gradient
5 Lessons
5 Lessons
Work your way through gradient descent, optimizing parameters, and addressing overshooting issues.
4.
Hyperspace
Hyperspace
6 Lessons
6 Lessons
Grasp the fundamentals of managing multi-dimensional data, matrix operations, and implementing multiple linear regression.
5.
A Discern Machine
A Discern Machine
5 Lessons
5 Lessons
Take a closer look at binary classification using logistic regression and gradient descent techniques.
6.
Get Real
Get Real
5 Lessons
5 Lessons
Investigate the importance of data, binary classification, and practical digit recognition techniques.
7.
The Final Challenge
The Final Challenge
5 Lessons
5 Lessons
Practice using multi-class classifiers, one-hot encoding, and classifier decoding for effective machine learning.
8.
The Perceptron
The Perceptron
4 Lessons
4 Lessons
Break down the perceptron's role, limitations, and historical significance in AI development.
9.
Designing the Network
Designing the Network
2 Lessons
2 Lessons
Get started with designing and understanding neural network architectures and key functions like Softmax.
10.
Building the Network
Building the Network
4 Lessons
4 Lessons
Break apart the neural network process, from forward propagation to cross-entropy loss, for effective training.
11.
Training the Network
Training the Network
7 Lessons
7 Lessons
Apply your skills to train neural networks using backpropagation, weight initialization, and effective iteration.
12.
How Classifiers Work
How Classifiers Work
3 Lessons
3 Lessons
Dig deeper into classifiers' decision boundaries and neural networks' flexibility through coding exercises.
13.
Batchin’ Up
Batchin’ Up
4 Lessons
4 Lessons
Follow the process of optimizing mini-batch gradient descent to enhance neural network training.
14.
The Zen of Testing
The Zen of Testing
3 Lessons
3 Lessons
Build on overfitting prevention, neural network development cycle, and dataset splitting issues.
15.
Let’s Do Development
Let’s Do Development
6 Lessons
6 Lessons
Step through development stages, from data preparation to optimizing neural network performance.
16.
A Deeper Kind of Network
A Deeper Kind of Network
5 Lessons
5 Lessons
Unpack the core of neural network depth, Keras implementation, and performance balancing.
17.
Defeating Overfitting
Defeating Overfitting
6 Lessons
6 Lessons
Explore strategies to combat overfitting in machine learning, improve model performance.
18.
Taming Deep Networks
Taming Deep Networks
5 Lessons
5 Lessons
Apply your skills to manage activation functions, weight initialization, and optimization in deep networks.
19.
Beyond Vanilla Networks
Beyond Vanilla Networks
5 Lessons
5 Lessons
Map out the steps for advanced techniques in CNNs and CIFAR-10 for image recognition.
20.
Into the Deep
Into the Deep
3 Lessons
3 Lessons
Focus on deep learning's rise, its powerful pattern recognition, and future machine learning paths.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
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 go even further by layering networks to create deep learning systems. You’ll work with complex real-world datasets to explore machine behavior from scratch at each phase.
By the end of this course, you’ll have a working knowledge of modern machine learning techniques. Using software engineering, you’ll be prepared to build complex neural networks and wrangle real-world data challenges.
ABOUT THE AUTHOR
The Pragmatic Programmers
We create timely, practical books and learning resources on classic and cutting-edge topics to help you practice your craft and accelerate your career.
Trusted by 2.9 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
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