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Build Python Apps with TensorFlow and a Fun Super Tank Game

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Build Python Apps with TensorFlow and a Fun Super Tank Game, available at $49.99, has an average rating of 4.5, with 166 lectures, based on 5 reviews, and has 97 subscribers.

You will learn about Explore PyCharm and the amazing Python 3 language! Explore Android Studio 3 and the Java 8 language. Make machine learning development easier with TensorFlow. Build a linear regression model to fit a line through data. Incorporate machine learning models into Android apps Build a simple digit recognition project using the MNIST handwritten digit database. Learn how the TensorFlow estimator differs from other computational graphs. Build 2D games in Unity. Use pathfinding algorithms to move characters in games. And much more! This course is ideal for individuals who are Anyone who wants to learn the technology that is revolutionizing how we interact with the world around us! or Anyone who wants to make games or apps. or Anyone who wants to make games with smart automated features. It is particularly useful for Anyone who wants to learn the technology that is revolutionizing how we interact with the world around us! or Anyone who wants to make games or apps. or Anyone who wants to make games with smart automated features.

Enroll now: Build Python Apps with TensorFlow and a Fun Super Tank Game

Summary

Title: Build Python Apps with TensorFlow and a Fun Super Tank Game

Price: $49.99

Average Rating: 4.5

Number of Lectures: 166

Number of Published Lectures: 166

Number of Curriculum Items: 166

Number of Published Curriculum Objects: 166

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Explore PyCharm and the amazing Python 3 language!
  • Explore Android Studio 3 and the Java 8 language.
  • Make machine learning development easier with TensorFlow.
  • Build a linear regression model to fit a line through data.
  • Incorporate machine learning models into Android apps
  • Build a simple digit recognition project using the MNIST handwritten digit database.
  • Learn how the TensorFlow estimator differs from other computational graphs.
  • Build 2D games in Unity.
  • Use pathfinding algorithms to move characters in games.
  • And much more!

Who Should Attend

  • Anyone who wants to learn the technology that is revolutionizing how we interact with the world around us!
  • Anyone who wants to make games or apps.
  • Anyone who wants to make games with smart automated features.

Target Audiences

  • Anyone who wants to learn the technology that is revolutionizing how we interact with the world around us!
  • Anyone who wants to make games or apps.
  • Anyone who wants to make games with smart automated features.

Do you want to build artificial intelligence, machine learning & coding projects?This course is for you! Learn to make apps and an awesome 2D tank game with computer science!

Funded by a #1 Kickstarter Project by Mammoth Interactive!

Explore Python, Java, PyCharm and databases. Learn artificial intelligence, use the a star algorithm & code in C#. Discover applications of machine learning and where we use artificial intelligence and algorithms daily.

  • 166 Lectures

  • 24.5 hours on-demand video

  • Watch Offline via the Udemy App

  • 17 Articles

  • 11 Supplemental Resources

  • Full lifetime access

Learn How Machine Learning Works

If you want to build sophisticated and intelligent mobile apps or simply want to know more about how machine learning works in a mobile environment, this course is for you.

Included in this course is material for beginners to get comfortable with the interfaces. Please note that we reuse this content in similar courses because it is introductory material. You can find some material in this course in the following related courses:

  • Hands-On Machine Learning: Learn TensorFlow, Python, & Java!

  • Learn artificial intelligence by building games and apps

  • ​The Complete 2D Unity & AI for Games with Algorithms Course

  • Learn Unity AI by Making a Tank Game​!

Build Machine Learning Models with Data

We provide clear, concise explanations at each step along the way so that viewers can not only replicate, but also understand and expand upon what I teach. Other courses don’t do a great job of explaining exactly what is going on at each step in the process and why we choose to build models the way we do.

Everyone Is Welcome!

We will teach you all you need to know about the languages, software and technologies we use. If you have lots of experience building machine learning apps, you may find this course a little slow because it’s designed for beginners.

Jump into a High Demand Field

Machine learning changes everything. It’s bringing us self-driving cars, facial recognition and artificial intelligence. And the best part is: anyone can create such innovations.

Build Unity Games and Code in C#

  • Make a game that uses artificial intelligence!

  • Use a pathfinding algorithm called ‘A Star’ to build a Unity game.

  • Program artificial intelligence players to think on their own.

Discover the Power of Algorithms

The A* is the base algorithm for path finding. A* is artificial intelligence that will find a path. This algorithm has existed for decades. A* is also important to avoid dangers like a cliff while getting to a destination. As well – suppose a game’s level has two paths.  You can use A* in many different platforms, programming languages and more.

Learn artificial intelligence by building games and apps

With the A Star algorithm, game characters can choose a better path to avoid monsters and other obstacles. A Super Tank on a maze will find the best way to go to a point you click. The tank will collect objects along its path.

The power of this algorithm will push your games to the next level.

Enroll Now While On Sale!

Course Curriculum

Chapter 1: Introduction to Machine Learning and Software

Lecture 1: Code

Chapter 2: Intro to Android

Lecture 1: Intro and Topics List

Chapter 3: Intro to Android Studio

Lecture 1: Downloading and Installing Android Studio

Lecture 2: Exploring Interface

Lecture 3: Setting up an Emulator and Running Project

Chapter 4: Intro to Java

Lecture 1: Intro to Language Basics

Lecture 2: Variable Types

Lecture 3: Operations on Variables

Lecture 4: Array and Lists

Lecture 5: Array and List Operations

Lecture 6: If and Switch Statements

Lecture 7: While Loops

Lecture 8: For Loops

Lecture 9: Functions Intro

Lecture 10: Parameters and Return Values

Lecture 11: Classes and Objects Intro

Lecture 12: Superclass and Subclasses

Lecture 13: Static Variables and Axis Modifiers

Chapter 5: Intro to App Development

Lecture 1: Intro To Android App Development

Lecture 2: Building Basic UI

Lecture 3: Connecting UI to Backend

Lecture 4: Implementing Backend and Tidying UI

Chapter 6: Intro to ML Concepts

Lecture 1: Intro to ML

Lecture 2: Pycharm Files

Chapter 7: Intro to PyCharm

Lecture 1: Intro and Topics List

Lecture 2: Learning Python with Mammoth Interactive

Chapter 8: Introduction

Lecture 1: Downloading and Installing PyCharm and Python

Lecture 2: Exploring PyCharm

Chapter 9: Python Language Basics

Lecture 1: Intro to Variables

Lecture 2: Variables Operations and Conversions

Lecture 3: Collection Types

Lecture 4: Collections Operations

Lecture 5: Control Flow If Statements

Lecture 6: While and For Loops

Lecture 7: Functions

Lecture 8: Classes and Objects

Chapter 10: Intro to TensorFlow

Lecture 1: Intro to TensorFlow

Lecture 2: Topics List

Lecture 3: Importing 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: Source Files

Chapter 11: Machine Learning in Android Studio Projects

Lecture 1: Introduction to Upcoming Projects

Chapter 12: TensorFlow Estimator

Lecture 1: Introduction

Lecture 2: Topics List

Lecture 3: Setting up Prebuilt Estimator Model

Lecture 4: Evaluating and Predicting with Prebuilt Model

Lecture 5: Building Custom Estimator Function

Lecture 6: Testing the Custom Estimator Function

Lecture 7: Summary and Model Comparison

Lecture 8: Source Files

Chapter 13: Intro to Android Machine Learning Model Import

Lecture 1: Intro and Demo

Lecture 2: Topics List

Lecture 3: Formatting and Saving the Model

Lecture 4: Saving the Optimized Graph File

Lecture 5: Starting Android Project

Lecture 6: Building the UI

Lecture 7: Implementing Inference Functionality

Lecture 8: Testing and Error Fixing

Lecture 9: Source Files

Chapter 14: Simple MNIST

Lecture 1: Intro and Demo

Lecture 2: Topics List and Intro to MNIST Data

Lecture 3: Building Computational Graph

Lecture 4: Training and Testing the Model

Lecture 5: Saving and Freezing the Graph for Android Import

Lecture 6: Setting up Android Studio Project

Lecture 7: Building the UI

Lecture 8: Loading Digit Images

Lecture 9: Formatting Image Data

Lecture 10: Making Prediction Using Model

Lecture 11: Displaying Results and Summary

Lecture 12: Simple MNIST – Mammoth Interactive

Chapter 15: MNIST with Estimator

Lecture 1: Introduction

Lecture 2: Topics List

Lecture 3: Building Custom Estimator Function

Lecture 4: Building Input Functions, Training, and Testing

Lecture 5: Predicting Using Our Model and Model Comparisons

Lecture 6: MNIST With Estimator – Mammoth Interactive

Chapter 16: Xcode Introduction

Lecture 1: Introduction to iOS

Lecture 2: Downloading and Installing Xcode

Instructors

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Frequently Asked Questions


How long do I have access to the course materials?


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


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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