HomeCoursesFundamentals of Machine Learning for Software Engineers

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Fundamentals of Machine Learning for Software Engineers
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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.

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Overview

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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.

Machine learning is the future for the next generation of software professionals. This course serves as a guide to machine lear...Show More

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

Working knowledge of modern machine learning techniques

TAKEAWAY SKILLS

Python

Machine Learning

Neural Networks

Content

93 Lessons10 Quizzes

How Machine Learning Works

4 Lessons

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

6 Lessons

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

5 Lessons

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

6 Lessons

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

Playground (Field Statistician)

Quiz: The Gradient Descent

A Discern Machine

5 Lessons

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.

Data Comes First

Our Own MNIST Library

The Real Thing

Playground (Tricky Digits)

Quiz: A Discerning Machine and Getting Real

The Final Challenge

5 Lessons

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

Multi-class Classifier

One Hot Encoding

Decode the Classifier’s Answers

Launch the Classifier

Playground (Minesweeper)

The Perceptron

4 Lessons

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

Introduction to Perceptron

Where Perceptrons Fail

A Tale of Perceptrons

Quiz: The Perceptrons

Designing the Network

2 Lessons

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

Assembling a Neural Network from Perceptrons

Introduction to Softmax

10.

Building the Network

4 Lessons

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

Code Forward Propagation

Writing the Algorithm (Softmax and Classification)

Cross Entropy

Playground (Time Travel Testing)

11.

Training the Network

7 Lessons

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

The Case for Backpropagation

From the Chain Rule to Backpropagation

Apply Backpropagation

Initialize the Weights

Train the Network

Playground

Quiz: Design, Build and Train the Network

12.

How Classifiers Work

3 Lessons

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

Trace a Boundary

Bend the Boundary

Playground

13.

Batchin’ Up

4 Lessons

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

Learning of the Model

Introduction to Batch

Understand Batches

Playground (The Smallest Batch)

14.

The Zen of Testing

3 Lessons

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

The Threat of Overfitting

The Development Cycle of Neural Networks

Playground (Thinking About Testing)

15.

Let’s Do Development

6 Lessons

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

Preparing Data

Tune Hyperparameters

Tune Learning Rate and Batch Size

The Final Test

Playground (Achieving 99%-MNIST)

Quiz: Develop the Network

16.

A Deeper Kind of Network

5 Lessons

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

The Echidna Dataset

Build a Neural Network with Keras

Keras in Action

Playground (Keras)

Quiz: A Deeper Kind of Network

Diabetes Prediction Using Keras

Project

17.

Defeating Overfitting

6 Lessons

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

Overfitting Explained

Review of the Deep Network

Regularize the Model

A Regularization Toolbox

Playground (Keeping it simple)

Quiz: Defeat Overfitting

18.

Taming Deep Networks

5 Lessons

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

Understand Activation Functions

Beyond the Sigmoid

Techniques to Improve Neural Network

Playground (The 10 Epochs Challenge)

Quiz: Tame Deep Networks

19.

Beyond Vanilla Networks

5 Lessons

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

The CIFAR-10 Dataset

The Building Blocks of CNNs

Run on Convolutions

Playground (Hyperparameters Galore)

Quiz on Deep Networks

20.

Into the Deep

3 Lessons

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

The Rise of Deep Learning

Unreasonable Effectiveness

Where Now?

Recognize Handwritten Digits Using a Deep Neural Network

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

The Pragmatic Programmers

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Fundamentals of Machine Learning for Software Engineers Save

Fundamentals of Machine Learning for Software Engineers
Save