Machine Learning Fundamentals - Fundamentals of Machine Learning for Software Engineers

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How Machine Learning Works

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

Our First Learning Program

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

The Limitations of Linear Regression Gradient Descent Partial Derivatives Put Gradient Descent to the Test Playground (Basecamp Overshooting)

Hyperspace

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

A Discern Machine

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

Get Real

Data Comes First Our Own MNIST Library The Real Thing Playground (Tricky Digits)Quiz: A Discerning Machine and Getting Real

The Final Challenge

Multi-class Classifier One Hot Encoding Decode the Classifier’s Answers Launch the Classifier Playground (Minesweeper)

The Perceptron

Introduction to Perceptron Where Perceptrons Fail A Tale of Perceptrons Quiz: The Perceptrons

Designing the Network

Assembling a Neural Network from Perceptrons Introduction to Softmax

Building the Network

Code Forward Propagation Writing the Algorithm (Softmax and Classification)Cross Entropy Playground (Time Travel Testing)

Training the Network

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

How Classifiers Work

Trace a Boundary Bend the Boundary Playground

Batchin’ Up

Learning of the Model Introduction to Batch Understand Batches Playground (The Smallest Batch)

The Zen of Testing

The Threat of Overfitting The Development Cycle of Neural Networks Playground (Thinking About Testing)

Let’s Do Development

Preparing Data Tune Hyperparameters Tune Learning Rate and Batch Size The Final Test Playground (Achieving 99%-MNIST)Quiz: Develop the Network

A Deeper Kind of Network

The Echidna Dataset Build a Neural Network with Keras Keras in Action Playground (Keras)Quiz: A Deeper Kind of Network

Project

Diabetes Prediction Using Keras

Defeating Overfitting

Overfitting Explained Review of the Deep Network Regularize the Model A Regularization Toolbox Playground (Keeping it simple)Quiz: Defeat Overfitting

Taming Deep Networks

Understand Activation Functions Beyond the Sigmoid Techniques to Improve Neural Network Playground (The 10 Epochs Challenge)Quiz: Tame Deep Networks

Beyond Vanilla Networks

The CIFAR-10 Dataset The Building Blocks of CNNs Run on Convolutions Playground (Hyperparameters Galore)Quiz on Deep Networks

Into the Deep

The Rise of Deep Learning Unreasonable Effectiveness Where Now?

Project

Recognize Handwritten Digits Using a Deep Neural Network

Machine Learning Fundamentals