Course Purchase Includes

Each purchase comes with a 10 day money-back guarantee

  • 7 hours of content

  • Unlimited access

  • Access to code forums

  • Devslopes Certificate of Completion

Start building more intelligent apps

The goal with Machine Learning is to mimic the human mind. It can be used to identify things like objects or images, make predictions and even analyze and identify speech. .

Code Help

You'll get access to our code forums where you can post questions and get help from teachers and the community. This is also a great place to meet other developers!

Guided Learning

The course is designed to take you from absolute beginner to advanced levels. If you are an experienced programmer the lessons are designed in a way to help you get up and running fast.

Course curriculum

  • 1

    Intro to Course

    • What is Machine Learning?

    • Basics of Machine Learning

    • Installing Anaconda / Python Environment

    • Downloading / Setting Up Atom & Plugins
  • 2

    Python Basics

    • Variables in Python

    • Functions, Conditionals, & Loops in Python

    • Arrays & Tuples in Python

    • Importing Modules in Python
  • 3

    Building a Classification Model

    • What is scikit-learn? Why use it?

    • Installing scikit-learn & scipy with Anaconda

    • Intro to the Iris Dataset

    • Datasets: Features & Labels Explained

    • Loading the Iris Dataset / Examining & Preparing Data

    • Creating / Training a KNeighborsClassifier

    • Testing Prediction Accuracy with Test Data

    • Building Our Own KNeighborsClassifier
  • 4

    Building a Convolutional Neural Network

    • What is Keras? Why use it?

    • What is a Convolutional Neural Network (CNN)?

    • Installing Keras with Anaconda

    • Preparing Dataset for a CNN

    • Building / Visualizing a CNN using Sequential: Part 1

    • Building / Visualizing a CNN using Sequential: Part 2

    • Training CNN / Evaluating Accuracy / Saving to Disk

    • Switching Python Environments / Converting to Core ML Model
  • 5

    Building a Handwriting Recognition App

    • Intro to App – Handwriting

    • Building Interface / Wiring Up

    • Drawing On Screen

    • Importing Core ML Model / Reading Metadata

    • Utilizing Core ML / Vision to Make Prediction

    • Handling / Displaying Prediction Results

    • Handwriting app final source
  • 6

    Core ML Basics

    • Intro to App – Core ML Photo Analysis

    • What is Machine Learning?

    • What is Core ML?

    • Core ML assets

    • Creating Xcode Project

    • Building ImageVC in Interface Builder / Wiring Up

    • Creating ImageCell & Subclass / Wiring Up

    • Creating FoodItems Helper File

    • Creating Custom 3x3 Grid UICollectionViewFlowLayout

    • Choosing, Downloading, Importing Core ML Model

    • Passing Images Through Core ML Model

    • Handling Core ML Prediction Results

    • Challenge – Core ML Photo Analysis

    • Core ML final source