Machine Learning for Apps

Machine Learning for Apps, available at $34.99, has an average rating of 4.55, with 42 lectures, based on 257 reviews, and has 11370 subscribers.

You will learn about Learn to code how the PROs code – not just copy and paste Build Real Projects – You'll get to build projects that help you retain what you've learned Build awesome apps that can make predictions Build amazing apps that can classify human handwriting This course is ideal for individuals who are If you have basic experience with iOS development take this course or If you have basic experience with iOS or mobile development then take this course It is particularly useful for If you have basic experience with iOS development take this course or If you have basic experience with iOS or mobile development then take this course.

Enroll now: Machine Learning for Apps

Summary

Title: Machine Learning for Apps

Price: $34.99

Average Rating: 4.55

Number of Lectures: 42

Number of Published Lectures: 42

Number of Curriculum Items: 42

Number of Published Curriculum Objects: 42

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Learn to code how the PROs code – not just copy and paste
Build Real Projects – You'll get to build projects that help you retain what you've learned
Build awesome apps that can make predictions
Build amazing apps that can classify human handwriting

Who Should Attend

If you have basic experience with iOS development take this course
If you have basic experience with iOS or mobile development then take this course

Target Audiences

If you have basic experience with iOS development take this course
If you have basic experience with iOS or mobile development then take this course

MACHINE LEARNING FOR APPS

Welcome to the most comprehensive course on Core ML, one of Apples hot new features for iOS 11. 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.

Dive in and learn the core concepts of machine learning and start building apps that can think! In this course you going to learn everything you need to know to start building more intelligent apps and your own ML Models.

WHY TAKE THIS COURSE?

Core ML is the first step if you want to start building apps with AI. Machine Learning opens an entirely new world to opportunities that will take your apps to the next level.

Here are some of the things you’ll be able to do after taking this course:

Learn to code how the PROs code – not just copy and paste
Build Real Projects – You’ll get to build projects that help you retain what you’ve learned
Build awesome apps that can make predictions
Build amazing apps that can classify human handwriting

WHAT YOU WILL LEARN:

Learn about the foundation of Machine Learning and Core ML
Learn foundational python
Build a classification model allow your apps to make predictions
Build a neural network for your app that can classify human writing
Learn core ML concepts so you can build your own ML Model
Utilize the power of Machine Learning and AI for use in iOS apps
Learn how to pass in images to Apples pre trained model – MobileNet

Don’t forget to join the free live community where you can get free help anytime from other students

Course Curriculum

Chapter 1: Intro to Course

Lecture 1: What is Machine Learning?

Lecture 2: Basics of Machine Learning

Lecture 3: Installing Anaconda / Python Environment

Lecture 4: Downloading / Setting Up Atom & Plugins

Chapter 2: Python Basics

Lecture 1: Variables in Python

Lecture 2: Functions, Conditionals, & Loops in Python

Lecture 3: Arrays & Tuples in Python

Lecture 4: Importing Modules in Python

Chapter 3: Building a Classification Model

Lecture 1: What is scikit-learn? Why use it?

Lecture 2: Installing scikit-learn & scipy with Anaconda

Lecture 3: Intro to the Iris Dataset

Lecture 4: Datasets: Features & Labels Explained

Lecture 5: Loading the Iris Dataset / Examining & Preparing Data

Lecture 6: Creating / Training a KNeighborsClassifier

Lecture 7: Testing Prediction Accuracy with Test Data

Lecture 8: Building Our Own KNeighborsClassifier

Chapter 4: Building a Convolutional Neural Network

Lecture 1: What is Keras? Why use it?

Lecture 2: What is a Convolutional Neural Network (CNN)?

Lecture 3: Installing Keras with Anaconda

Lecture 4: Preparing Dataset for a CNN

Lecture 5: Building / Visualizing a CNN using Sequential: Part 1

Lecture 6: Building / Visualizing a CNN using Sequential: Part 2

Lecture 7: Training CNN / Evaluating Accuracy / Saving to Disk

Lecture 8: Switching Python Environments / Converting to Core ML Model

Chapter 5: Building a Handwriting Recognition App

Lecture 1: Intro to App – Handwriting

Lecture 2: Building Interface / Wiring Up

Lecture 3: Drawing On Screen

Lecture 4: Importing Core ML Model / Reading Metadata

Lecture 5: Utilizing Core ML / Vision to Make Prediction

Lecture 6: Handling / Displaying Prediction Results

Chapter 6: Core ML Basics

Lecture 1: Intro to App – Core ML Photo Analysis

Lecture 2: What is Machine Learning?

Lecture 3: What is Core ML?

Lecture 4: Creating Xcode Project

Lecture 5: Building ImageVC in Interface Builder / Wiring Up

Lecture 6: Creating ImageCell & Subclass / Wiring Up

Lecture 7: Creating FoodItems Helper File

Lecture 8: Creating Custom 3×3 Grid UICollectionViewFlowLayout

Lecture 9: Choosing, Downloading, Importing Core ML Model

Lecture 10: Passing Images Through Core ML Model

Lecture 11: Handling Core ML Prediction Results

Lecture 12: Challenge – Core ML Photo Analysis

Instructors

Devslopes by Nathan Sevedge
Learn programming & app development

Rating Distribution

1 stars: 4 votes
2 stars: 6 votes
3 stars: 24 votes
4 stars: 71 votes
5 stars: 152 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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