Srbija Posted September 14, 2023 Share #1 Posted September 14, 2023 Make Predictions With Python Machine Learning For Apps Last updated 5/2018 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 11.28 GB | Duration: 17h 25m Leverage TensorFlow models to build & improve apps! Use Google's deep learning framework w/ Java & AI. Beginner-friendly What you'll 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 Requirements No experience required! We will show you how to get all required programs for free This course was recorded on a Mac, but you can use a PC Description Go through 3 ultimate levels 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 KickstarterUse Google's deep learning framework TensorFlow with Python. Leverage machine learning to improve your appsPrediction Models MasterclassBy 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 prediction project, 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 problemWe'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 BeginnersWith 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! Overview Section 1: Resources Lecture 1 Resources Section 2: Intro to Android Studio Lecture 2 Intro to Android and Project Outline Lecture 3 Downloading and Installing Android Studio Lecture 4 Exploring Interface Lecture 5 Setting up Emulator and Running Project Section 3: Intro to Java Lecture 6 Java Language Basics Lecture 7 Variable Types Lecture 8 Operations on Variables Lecture 9 Arrays and Lists Lecture 10 Array and List Operations Lecture 11 If Statements and Switch Statements Lecture 12 While Loops Lecture 13 For Loops Lecture 14 Functions Lecture 15 Parameters and Return Values Lecture 16 Classes and Objects Lecture 17 Superclass and Subclasses Lecture 18 Static Variables and Axis Modifiers Section 4: -------------App Development------------- Lecture 19 Android App Development Lecture 20 Building Basic User Interface Lecture 21 Connecting UI to Backend Lecture 22 Implementing Backend and Tidying UI Section 5: Machine Learning Concepts Lecture 23 ML Concepts Introduction Lecture 24 Intro to PyCharm and Project Outline Lecture 25 How to Install PyCharm and Python Lecture 26 Let's Explore PyCharm Lecture 27 (Files) Source Code Section 6: Python Language Basics Lecture 28 Variables Lecture 29 Variable Operations and Conversions Lecture 30 Collection Types Lecture 31 Operations on Collections Lecture 32 Control Flow: If Statements Lecture 33 While and For Loops Lecture 34 Functions Lecture 35 Classes and Objects Lecture 36 (Files) Source Code Section 7: TensorFlow Lecture 37 TensorFlow Introduction Lecture 38 Project Outline Lecture 39 How to Import TensorFlow to PyCharm Lecture 40 Constant Nodes and Sessions Lecture 41 Variable Nodes Lecture 42 Placeholder Nodes Lecture 43 Operation Nodes Lecture 44 Loss, Optimizers, and Training Lecture 45 Building a Linear Regression Model Lecture 46 (Files) Source Code Section 8: -------------Machine Learning in Android Studio Projects------------- Lecture 47 Introduction to ML for Android Section 9: TensorFlow Estimator Lecture 48 TensorFlow Estimator Introduction Lecture 49 Project Outline Lecture 50 Setting up Prebuilt Estimator Model Lecture 51 Evaluating and Predicting with Model Lecture 52 Building Custom Estimator Function Lecture 53 Testing Custom Estimator Function Lecture 54 Summary and Model Comparison Lecture 55 (Files) Source Code Section 10: Importing Android Machine Learning Model Lecture 56 Intro & Demo: ML Model Import Lecture 57 Project Outline Lecture 58 Formatting and Saving Model Lecture 59 Saving Optimized Graph File Lecture 60 Starting Android Project Lecture 61 Building UI Lecture 62 Implementing Inference Functionality Lecture 63 Testing and Error Handling Lecture 64 (Files) Source Code Section 11: Simple MNIST Lecture 65 Intro & Demo: Simple MNIST Lecture 66 Project Outline and Intro to MNIST Data Lecture 67 Building Computational Graph Lecture 68 Training and Testing Model Lecture 69 Saving Graph for Android Import Lecture 70 Setting up Android Studio Project Lecture 71 Building User Interface Lecture 72 Loading Digit Images Lecture 73 Formatting Image Data Lecture 74 Making Prediction Using Model Lecture 75 Displaying Results and Summary Lecture 76 (Files) Source Code Section 12: MNIST with Estimator Lecture 77 MNIST With Estimator Introduction Lecture 78 Project Outline Lecture 79 Building Custom Estimator Function Lecture 80 Training & Testing Input Functions Lecture 81 Predicting Using Model & Comparisons Lecture 82 (Files) Source Code Section 13: -------------Build Image Recognition Apps------------- Lecture 83 Introduction to Image Recognition Apps Section 14: Weather Prediction Lecture 84 Intro and Demo: Weather Prediction Lecture 85 Project Outline Lecture 86 Retrieving Data Lecture 87 Formatting Datasets Lecture 88 Building Computational Graphs Lecture 89 Writing, Training, Testing, & Evaluating Lecture 90 Training, Testing, and Freezing Model Lecture 91 Setting up Android Project Lecture 92 Building UI Lecture 93 Build App Backend and Project Summary Lecture 94 (Files) Source Code Section 15: Text Prediction Lecture 95 Intro and Demo: Text Prediction Lecture 96 Project Outline Lecture 97 Processing Text Data Lecture 98 Building Datasets and Model Builder Lecture 99 Building Computational Graph Lecture 100 Writing, Training, and Testing Code Lecture 101 Training, Testing, and Freezing Graph Lecture 102 Setting up Android Project Lecture 103 Setting up UI Lecture 104 Setting up Vocab Dictionary Lecture 105 Formatting Input and Running Through Model Lecture 106 (Files) Source Code Section 16: Stock Market Prediction Lecture 107 Intro & Demo: Stock Market Prediction Lecture 108 Project Outline Lecture 109 Retrieving Data via RESTful API Call Lecture 110 Parsing JSON Data PyCharm Style Lecture 111 Formatting Data Lecture 112 Building the Model Lecture 113 Training and Testing Model Lecture 114 Freezing Graph Lecture 115 Setting up Android Project Lecture 116 Building UI Lecture 117 Requesting Data Via AsyncTask Lecture 118 Parsing JSON Data Android Style Lecture 119 Running Inference and Displaying Results Lecture 120 (Files) Source Code Section 17: -------------Bonus------------- Lecture 121 Please rate this course Lecture 122 Bonus Lecture: Community Newsletter 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 Hidden Content Give reaction to this post to see the hidden content. 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