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Data Science and Machine Learning Personal Development

Machine Learning for Apps

Overview: Welcome to "Machine Learning for Apps"! This course is tailored for app developers seeking to integrate machine learning capabilities into ...

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42 Lesson

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6hr 53min

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4 students enrolled

Overview:

Welcome to "Machine Learning for Apps"! This course is tailored for app developers seeking to integrate machine learning capabilities into their applications. Machine learning has revolutionized app development, enabling apps to offer personalized experiences, predictive features, and intelligent functionalities. In this course, you'll learn how to leverage machine learning algorithms and techniques to enhance your apps, making them smarter, more efficient, and more user-friendly.
  • Interactive video lectures by industry experts
  • Instant e-certificate and hard copy dispatch by next working day
  • Fully online, interactive course with Professional voice-over
  • Developed by qualified first aid professionals
  • Self paced learning and laptop, tablet, smartphone friendly
  • 24/7 Learning Assistance
  • Discounts on bulk purchases

Main Course Features:

  • Comprehensive coverage of machine learning concepts and algorithms relevant to app development
  • Hands-on projects and coding exercises focusing on integrating machine learning models into apps
  • Exploration of popular machine learning libraries and frameworks suitable for app development
  • Guidance on data preprocessing, model training, and deployment in the context of app development
  • Real-world examples and case studies demonstrating machine learning applications in various types of apps
  • Access to resources and tools for building and testing machine learning-powered apps
  • Supportive online community for collaboration and assistance throughout the course
  • Regular assessments and feedback to track progress and reinforce learning

Who Should Take This Course:

  • App developers interested in incorporating machine learning features into their apps
  • Software engineers looking to expand their skill set to include machine learning for app development
  • Students and professionals aiming to enhance their app development skills with machine learning capabilities

Learning Outcomes:

  • Understand fundamental machine learning concepts and techniques relevant to app development
  • Integrate machine learning models seamlessly into mobile and web applications
  • Utilize popular machine learning libraries and frameworks such as TensorFlow or PyTorch for app development
  • Implement predictive features, recommendation systems, and other intelligent functionalities in apps
  • Optimize machine learning models for performance and efficiency in app environments
  • Debug and troubleshoot machine learning integration issues in apps effectively
  • Develop a portfolio of machine learning-powered apps showcasing proficiency in app development and machine learning integration
  • Stay updated with the latest advancements and trends in machine learning for app development.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Assessment

At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.
Course Content
42 Lectures 6hr 53min

  • ImgWhat is Machine Learning?

  • ImgBasics of Machine Learning

  • ImgInstalling Anaconda / Python Environment

  • ImgDownloading / Setting Up Atom and Plugins

  • ImgVariables in Python

  • ImgFunctions, Conditionals, and Loops in Python

  • ImgArrays and Tuples in Python

  • ImgImporting Modules in Python

  • ImgWhat is scikit-learn? Why use it?

  • ImgInstalling scikit-learn and scipy with Anaconda

  • ImgIntro to the Iris Dataset

  • ImgDatasets: Features and Labels Explained

  • ImgLoading the Iris Dataset / Examining and Preparing Data

  • ImgCreating / Training a KNeighborsClassifier

  • ImgTesting Prediction Accuracy with Test Data

  • ImgBuilding Our Own KNeighborsClassifie

  • ImgWhat is Keras? Why use it?

  • ImgWhat is a Convolutional Neural Network (CNN)?

  • ImgInstalling Keras with Anaconda

  • ImgPreparing Dataset for a CNN

  • ImgBuilding / Visualizing a CNN using Sequential: Part 1

  • ImgBuilding / Visualizing a CNN using Sequential: Part 2

  • ImgTraining CNN / Evaluating Accuracy / Saving to Disk

  • ImgSwitching Python Environments / Converting to Core ML Model

  • ImgIntro to App-Handwriting

  • ImgBuilding Interface / Wiring Up

  • ImgDrawing on Screen

  • ImgImporting Core ML Model / Reading Metadata

  • ImgUtilizing Core ML / Vision to Make Prediction

  • ImgHandling / Displaying Prediction Results

  • ImgIntro to App -Core ML Photo Analysis

  • ImgWhat is Machine Learning?

  • ImgWhat is Core ML?

  • ImgCreating X code Project

  • ImgBuilding Image VC in Interface Builder / Wiring Up

  • ImgCreating Image Cell

  • ImgCreating Food Items Helper File

  • ImgCreating Custom 3×3 Grid UI Collection View Flow Layout

  • ImgChoosing, Downloading, Importing Core ML Model

  • ImgPassing Images through Core ML Model

  • ImgHandling Core ML Prediction Results

  • ImgChallenge –Core ML Photo Analysis

course

R39

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Includes

  • Img Enrolled: 4 students
  • Img Duration: 6hr 53min
  • Img Chapters: 42