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Machine Learning for Apps

£50 £20
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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.

We guarantee that all our online courses will meet or exceed your expectations. If you are not fully satisfied with a course - for any reason at all - simply request a full refund. We guarantee no hassles. That's our promise to you.

Go ahead and order with confidence!

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Course Curriculum

Section 01: Intro to Course
What is Machine Learning?
Basics of Machine Learning
Installing Anaconda / Python Environment
Downloading / Setting Up Atom and Plugins
Section 02: Python Basics
Variables in Python
Functions, Conditionals, and Loops in Python
Arrays and Tuples in Python
Importing Modules in Python
Section 03: Building a Classification Model
What is scikit-learn? Why use it?
Installing scikit-learn and scipy with Anaconda
Intro to the Iris Dataset
Datasets: Features and Labels Explained
Loading the Iris Dataset / Examining and Preparing Data
Creating / Training a KNeighborsClassifier
Testing Prediction Accuracy with Test Data
Building Our Own KNeighborsClassifie
Section 04: 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
Section 05: 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
Section 06: Core ML Basics
Intro to App -Core ML Photo Analysis
What is Machine Learning?
What is Core ML?
Creating X code Project
Building Image VC in Interface Builder / Wiring Up
Creating Image Cell
Creating Food Items Helper File
Creating Custom 3×3 Grid UI Collection View Flow Layout
Choosing, Downloading, Importing Core ML Model
Passing Images through Core ML Model
Handling Core ML Prediction Results
Challenge –Core ML Photo Analysis