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Stone River Elearning – Machine Learning for Apps

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Stone River Elearning – Machine Learning for Apps

Stone River Elearning - Machine Learning for Apps

CourseΒ Description

Welcome to the most comprehensive course on Core ML, one of Apple’s hot new features for iOS 11. The goal of 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.

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

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction to Course
4 Lectures
  • play iconWhat is Machine Learning?07:31
  • play iconBasics of Machine Learning06:17
  • play iconInstalling Anaconda / Python Environment07:02
  • play iconDownloading / Setting Up Atom & Plugins08:43
Python Basics
4 Lectures
  • play iconVariables in Python08:07
  • play iconArrays & Tuples in Python13:35
  • play iconFunctions, Conditionals, & Loops in Python09:33
  • play iconImporting Modules in Python05:05
Building a Classification Model
8 Lectures
  • play iconWhat is scikit-learn- Why use it03:34
  • play iconInstalling scikit-learn & scipy with Anaconda03:11
  • play iconIntro to the Iris Dataset03:11
  • play iconDatasets- Features & Labels Explained07:22
  • play iconLoading the Iris Dataset – Examining & Preparing Data09:10
  • play iconCreating – Training a KNeighborsClassifier09:25
  • play iconTesting Prediction Accuracy with Test Data11:48
  • play iconBuilding Our Own KNeighbors Classifier17:43
Building a Convolutional Neural Network
8 Lectures
  • play iconWhat is Keras- Why use it07:44
  • play iconWhat is a Convolutional Neural Network (CNN)26:12
  • play iconInstalling Keras with Anaconda04:21
  • play iconPreparing Dataset for a CNN17:21
  • play iconBuilding – Visualizing a CNN using Sequential- Part 113:50
  • play iconBuilding – Visualizing a CNN using Sequential- Part 219:23
  • play iconTraining CNN – Evaluating Accuracy – Saving to Disk17:36
  • play iconSwitching Python Environments – Converting to Core ML Model13:21
Building a Handwriting Recognition App
6 Lectures
  • play iconIntro to App – Handwriting02:38
  • play iconBuilding Interface – Wiring Up11:25
  • play iconDrawing On Screen20:44
  • play iconImporting Core ML Model – Reading Metadata04:59
  • play iconUtilizing Core ML – Vision to Make Prediction17:13
  • play iconHandling – Displaying Prediction Results14:54
Core ML Basics
12 Lectures
  • play iconIntro to App – Core ML Photo Analysis04:08
  • play iconWhat is Machine Learning07:31
  • play iconWhat is Core ML04:48
  • play iconCreating Xcode Project02:26
  • play iconBuilding ImageVC in Interface Builder – Wiring Up07:23
  • play iconCreating ImageCell & Subclass – Wiring Up07:55
  • play iconCreating FoodItems Helper File06:45
  • play iconCreating Custom 3×3 Grid UICollectionViewFlowLayout08:54
  • play iconChoosing, Downloading, Importing Core ML Model05:04
  • play iconPassing Images Through Core ML Model12:02
  • play iconHandling Core ML Prediction Results09:25
  • play iconChallenge – Core ML Photo Analysis01:00

InstructorΒ Details

Stone River eLearning

Stone River eLearning

At Stone River eLearning, technology is all we teach. If you’re interested in IT, programming, development or design – we have it covered.

Check out our huge catalog of courses and join the over 1,200,000+ students currently taking Stone River eLearning courses. We currently offer 800+ different technology training courses on our Stone River eLearning website and are adding new courses on hot and trending topics every month. A subscription option is available for those with a real passion for learning.

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Original price was: $99.00.Current price is: $22.00.

Welcome to the most comprehensive course on Core ML, one of Apple's hot new features for iOS 11. File Size: 2 GB.

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