Make predictions with Python machine learning for apps

Make predictions with Python machine learning for apps, available at $39.99, has an average rating of 3.6, with 122 lectures, based on 45 reviews, and has 569 subscribers.

You will learn about Master the basics: become an expert in Python and Java while learning core machine learning concepts Machine learning goes mobile: learn how to incorporate machine learning models into Android apps Optimize for intelligent apps: discover the TensorFlow mobile framework and build scientific analysis apps This course is ideal for individuals who are People who want to learn machine learning concepts through practical projects with PyCharm, Python, Android Studio, Java, and TensorFlow or Anyone who wants to learn the technology that is shaping how we interact with the world around us or Anyone who is interested in predictive modeling for handling the stock market, weather, and text It is particularly useful for People who want to learn machine learning concepts through practical projects with PyCharm, Python, Android Studio, Java, and TensorFlow or Anyone who wants to learn the technology that is shaping how we interact with the world around us or Anyone who is interested in predictive modeling for handling the stock market, weather, and text.

Enroll now: Make predictions with Python machine learning for apps

Summary

Title: Make predictions with Python machine learning for apps

Price: $39.99

Average Rating: 3.6

Number of Lectures: 122

Number of Published Lectures: 122

Number of Curriculum Items: 122

Number of Published Curriculum Objects: 122

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Master the basics: become an expert in Python and Java while learning core machine learning concepts

Machine learning goes mobile: learn how to incorporate machine learning models into Android apps

Optimize for intelligent apps: discover the TensorFlow mobile framework and build scientific analysis apps

Who Should Attend

People who want to learn machine learning concepts through practical projects with PyCharm, Python, Android Studio, Java, and TensorFlow

Anyone who wants to learn the technology that is shaping how we interact with the world around us

Anyone who is interested in predictive modeling for handling the stock market, weather, and text

Target Audiences

People who want to learn machine learning concepts through practical projects with PyCharm, Python, Android Studio, Java, and TensorFlow

Anyone who wants to learn the technology that is shaping how we interact with the world around us

Anyone who is interested in predictive modeling for handling the stock market, weather, and text

Go through 3 ultimatelevels of artificial intelligence for beginners!

Learn artificial intelligence, machine learning, and mobile dev with Java, Android, TensorFlow Estimator, PyCharm, and MNIST. Woah! That’s a lot of content for one course.

This course was funded by a wildly successful Kickstarter

Use Google’s deep learning framework TensorFlow with Python. Leverage machine learning to improve your apps

Prediction Models Masterclass

By the end of this course you will have 3 complete mobile machine learning models and apps. We will build a simple weather prediction project, stock market predictionproject, and text-response project. 

For each we will build a basic version in PyCharm, save the trained model, export the trained model to Android Studio, and build an app around model.

No experience? No problem

We’ll give you all necessary information to succeed from newbie to pro. We will install PyCharm 2017.2.3 and explore the interface. I will show you every step of the way. You will learn crucial Python 3.6.2 language fundamentals. Even if you have coding knowledge, going back to the basics is the key to success as a programmer. We will build and run Python projects. I teach through practical examples, follow-alongs, and over-the-shoulder tutorials. You won’t need to go anywhere else.

Then we will install Android Studio 3 and explore the interface. You will learn how to add a simulator and build simple User Interfaces (UIs). For coding, you will learn Java 8 language fundamentals. Java is a HUGE language that you must know, and I will tell you all about it. We will build and run Android projects directly in the course, and you will have solid examples to apply your knowledge immediately.

Complete Image Recognition and Machine Learning for Beginners

With this course I will help you understand what machine learning is and compare it to Artificial Intelligence (AI). Together we will discover applications of machine learning and where we use machine learning daily. Machine learning, neural networks, deep learning, and artificial intelligence are all around us, and they’re not going away. I will show you how to get a grasp on this ever-growing technology in this course. We will explore different machine learning mechanisms and commonly used algorithms. These are popular and ones you should know.

Next I’ll teach you what TensorFlow 1.4.1 is and how it makes machine learning development easier. You will learn how to install TensorFlow and access its libraries through PyCharm. You’ll understand the basic components of TensorFlow.

Follow along with me to build a complete computational model. We’ll train and test a model and use it for future predictions. I’ll also show you how to build a linear regression model to fit a line through data. You’ll learn to train and test the model, evaluate model accuracy, and predict values using the model.

Stock Market, Weather & Text – Let’s Go!

Course Curriculum

Chapter 1: Resources

Lecture 1: Resources

Chapter 2: Intro to Android Studio

Lecture 1: Intro to Android and Project Outline

Lecture 2: Downloading and Installing Android Studio

Lecture 3: Exploring Interface

Lecture 4: Setting up Emulator and Running Project

Chapter 3: Intro to Java

Lecture 1: Java Language Basics

Lecture 2: Variable Types

Lecture 3: Operations on Variables

Lecture 4: Arrays and Lists

Lecture 5: Array and List Operations

Lecture 6: If Statements and Switch Statements

Lecture 7: While Loops

Lecture 8: For Loops

Lecture 9: Functions

Lecture 10: Parameters and Return Values

Lecture 11: Classes and Objects

Lecture 12: Superclass and Subclasses

Lecture 13: Static Variables and Axis Modifiers

Chapter 4: -App Development-

Lecture 1: Android App Development

Lecture 2: Building Basic User Interface

Lecture 3: Connecting UI to Backend

Lecture 4: Implementing Backend and Tidying UI

Chapter 5: Machine Learning Concepts

Lecture 1: ML Concepts Introduction

Lecture 2: Intro to PyCharm and Project Outline

Lecture 3: How to Install PyCharm and Python

Lecture 4: Lets Explore PyCharm

Lecture 5: (Files) Source Code

Chapter 6: Python Language Basics

Lecture 1: Variables

Lecture 2: Variable Operations and Conversions

Lecture 3: Collection Types

Lecture 4: Operations on Collections

Lecture 5: Control Flow: If Statements

Lecture 6: While and For Loops

Lecture 7: Functions

Lecture 8: Classes and Objects

Lecture 9: (Files) Source Code

Chapter 7: TensorFlow

Lecture 1: TensorFlow Introduction

Lecture 2: Project Outline

Lecture 3: How to Import TensorFlow to PyCharm

Lecture 4: Constant Nodes and Sessions

Lecture 5: Variable Nodes

Lecture 6: Placeholder Nodes

Lecture 7: Operation Nodes

Lecture 8: Loss, Optimizers, and Training

Lecture 9: Building a Linear Regression Model

Lecture 10: (Files) Source Code

Chapter 8: -Machine Learning in Android Studio Projects-

Lecture 1: Introduction to ML for Android

Chapter 9: TensorFlow Estimator

Lecture 1: TensorFlow Estimator Introduction

Lecture 2: Project Outline

Lecture 3: Setting up Prebuilt Estimator Model

Lecture 4: Evaluating and Predicting with Model

Lecture 5: Building Custom Estimator Function

Lecture 6: Testing Custom Estimator Function

Lecture 7: Summary and Model Comparison

Lecture 8: (Files) Source Code

Chapter 10: Importing Android Machine Learning Model

Lecture 1: Intro & Demo: ML Model Import

Lecture 2: Project Outline

Lecture 3: Formatting and Saving Model

Lecture 4: Saving Optimized Graph File

Lecture 5: Starting Android Project

Lecture 6: Building UI

Lecture 7: Implementing Inference Functionality

Lecture 8: Testing and Error Handling

Lecture 9: (Files) Source Code

Chapter 11: Simple MNIST

Lecture 1: Intro & Demo: Simple MNIST

Lecture 2: Project Outline and Intro to MNIST Data

Lecture 3: Building Computational Graph

Lecture 4: Training and Testing Model

Lecture 5: Saving Graph for Android Import

Lecture 6: Setting up Android Studio Project

Lecture 7: Building User Interface

Lecture 8: Loading Digit Images

Lecture 9: Formatting Image Data

Lecture 10: Making Prediction Using Model

Lecture 11: Displaying Results and Summary

Lecture 12: (Files) Source Code

Chapter 12: MNIST with Estimator

Lecture 1: MNIST With Estimator Introduction

Lecture 2: Project Outline

Lecture 3: Building Custom Estimator Function

Lecture 4: Training & Testing Input Functions

Lecture 5: Predicting Using Model & Comparisons

Lecture 6: (Files) Source Code

Chapter 13: -Build Image Recognition Apps-

Lecture 1: Introduction to Image Recognition Apps

Chapter 14: Weather Prediction

Lecture 1: Intro and Demo: Weather Prediction

Lecture 2: Project Outline

Lecture 3: Retrieving Data

Instructors

Mammoth Interactive
Top-Rated Instructor, 3.3 Million+ Students

John Bura
Best Selling Instructor Web/App/Game Developer 1Mil Students

Rating Distribution

1 stars: 2 votes

2 stars: 2 votes

3 stars: 6 votes

4 stars: 15 votes

5 stars: 20 votes

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