Srbija Posted November 17, 2022 Share #1 Posted November 17, 2022 Learn To Master Python: From Beginner To Expert Last updated 4/2019 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 65.56 GB | Duration: 115h 31m Use Google's deep learning framework TensorFlow with Python. Leverage machine learning to make game changing apps. What you'll learn Code in the Python programming language. Create basic line and scatter plots with Matplotlib Customize our graphs with visuals, a title, labels, text and a legend. Optimize for intelligent apps: discover the TensorFlow mobile framework and build scientific analysis apps Machine learning goes mobile: learn how to incorporate machine learning models into Android apps And More! Requirements Download Anaconda 4.2.0, the free data science platform by Continuum, which contains Python 3.5.2 and Matplotlib. Otherwise, you can download and install Python 3.5.2 and Matplotlib for free online. Topics involve intermediate math, so familiarity with university-level math is helpful. This course was recorded on a Mac, but you can use a PC Description We at Mammoth Interactive value input from students like you. Feel free to leave us your feedback.This course was funded through a massively successful Kickstarter campaign.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.Use 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 problemDo you want to learn how to visualize data? Enroll in this course to learn how to do so directly in code. Make 2D & 3D Graphs in Python with Matplotlib for Beginners! is suitable for coding beginners because we begin with a complete introduction to coding. Then we delve deep into using Matplotlib, a Python 2D plotting library.In Part 1, you learn how to use Python, a popular coding language used for websites like YouTube and Instagram. You learn the basics of programming, including topics like variables, functions, and if statements. You learn about data structures such as lists, dictionaries, and sets. We cover how to use for and while loops, how to handle user input and output, file input and output. We apply our knowledge to build a fully functional tic-tac-toe game. You learn classes, methods, attributes, instancing, and class inheritance. We make an additional Blackjack game! You learn how to solve errors that can occur when you work as a programmer.In Part 2, you take your Python knowledge and apply it to Matplotlib. We go over many cool features of Matplotlib that we can use for data visualization. We show you how to make line plots, scatter plots, candlestick plots. You learn how to customize the visuals of your graph and to add text and annotate graphs. And much more!Why choose Mammoth Interactive? We prioritize learning by doing. We blend theory with practical projects to ensure you get a hands-on experience by building projects alongside your instructor. Our experienced instructors know how to explain topics clearly at a logical pace. Check out our huge catalog of courses for more content.Also now included in these bundles are our extra courses. If you want to learn to use other programs such as Camtasia or Sketch, you get more content than what you paid for this way!We really hope you decide to purchase this course and take your knowledge to the next level. Let's get started.Enroll now to join the Mammoth community! Overview Section 1: Make Predictions with Python Machine Learning for Apps - Resources Lecture 1 Make Predictions with Python Machine Learning for Apps - Resources Section 2: Introduction to Machine Learning and Software Lecture 2 Source Code Section 3: Intro to Android Lecture 3 Intro and Topics List Section 4: Intro to Android Studio Lecture 4 Downloading and Installing Android Studio Lecture 5 Exploring Interface Lecture 6 Setting up an Emulator and Running Project Section 5: Intro to Java Lecture 7 Intro to Language Basics Lecture 8 Variable Types Lecture 9 Operations on Variables Lecture 10 Array and Lists Lecture 11 Array and List Operations Lecture 12 If and Switch Statements Lecture 13 While Loops Lecture 14 For Loops Lecture 15 Functions Intro Lecture 16 Parameters and Return Values Lecture 17 Classes and Objects Intro Lecture 18 Superclass and Subclasses Lecture 19 Static Variables and Axis Modifiers Section 6: Intro to App Development Lecture 20 Intro To Android App Development Lecture 21 Building Basic UI Lecture 22 Connecting UI to Backend Lecture 23 Implementing Backend and Tidying UI Section 7: Intro to ML Concepts Lecture 24 Intro to ML Lecture 25 Pycharm Files Section 8: Intro to Pycharm Lecture 26 Intro and Topics List Lecture 27 Learning Python with Mammoth Interactive Section 9: Introduction Lecture 28 Downloading and Installing Pycharm and Python Lecture 29 Support for Python Problems or Questions Lecture 30 Exploring Pycharm Section 10: Python Language Basics Lecture 31 Intro to Variables Lecture 32 Variables Operations and Conversions Lecture 33 Collection Types Lecture 34 Collections Operations Lecture 35 Control Flow If Statements Lecture 36 While and For Loops Lecture 37 Functions Lecture 38 Classes and Objects Section 11: Intro to Tensorflow Lecture 39 Intro Lecture 40 Installing TensorFlow Lecture 41 Topics List Lecture 42 Importing Tensorflow to Pycharm Lecture 43 Constant Nodes and Sessions Lecture 44 Variable Nodes Lecture 45 Placeholder Nodes Lecture 46 Operation nodes Lecture 47 Loss, Optimizers, and Training Lecture 48 Building a Linear Regression Model Lecture 49 Source Files Section 12: Machine Learning in Android Studio Projects Lecture 50 Introduction to Level 2 Section 13: Tensorflow Estimator Lecture 51 Introduction Lecture 52 Topics List Lecture 53 Setting up Prebuilt Estimator Model Lecture 54 Evaluating and Predicting with Prebuilt Model Lecture 55 Building Custom Estimator Function Lecture 56 Testing the Custom Estimator Function Lecture 57 Summary and Model Comparison Lecture 58 Source Files Section 14: Intro to Android Machine Learning Model Import Lecture 59 Intro and Demo Lecture 60 Topics List Lecture 61 Formatting and Saving the Model Lecture 62 Saving the Optimized Graph File Lecture 63 Starting Android Project Lecture 64 Building the UI Lecture 65 Implementing Inference Functionality Lecture 66 Testing and Error Fixing Lecture 67 Source Files Section 15: Simple MNIST Lecture 68 Intro and Demo Lecture 69 Topics List and Intro to MNIST Data Lecture 70 Building Computational Graph Lecture 71 Training and Testing the Model Lecture 72 Saving and Freezing the Graph for Android Import Lecture 73 Setting up Android Studio Project Lecture 74 Building the UI Lecture 75 Loading Digit Images Lecture 76 Formatting Image Data Lecture 77 Making Prediction Using Model Lecture 78 Displaying Results and Summary Lecture 79 Simple MNIST - Mammoth Interactive Section 16: MNIST with Estimator Lecture 80 Introduction Lecture 81 Topics List Lecture 82 Building Custom Estimator Function Lecture 83 Building Input Functions, Training, and Testing Lecture 84 Predicting Using Our Model and Model Comparisons Lecture 85 MNIST With Estimator - Mammoth Interactive Section 17: Build Image Recognition Apps Lecture 86 Introduction to Level 3 Lecture 87 Source Code Section 18: Stock Market Prediction Lecture 88 Project Demo Lecture 89 Project Overview Lecture 90 Retrieving Data via RESTful API Call Lecture 91 Parsing JSON Data Pycharm Style Lecture 92 Formatting Data Lecture 93 Building the Model Lecture 94 Training and Testing The model Lecture 95 Freezing Graph Lecture 96 Setting up Android Project Lecture 97 Building UI Lecture 98 Requesting Data Via AsyncTask Lecture 99 Parsing JSON Data Android Style Lecture 100 Running Inference and Displaying Results Lecture 101 Stock Market Prediction Project Files- Mammoth Interactive Section 19: Text Prediction Lecture 102 Intro and Demo Lecture 103 Tasks List Lecture 104 Processing Text Data Lecture 105 Building Data Sets and Model Builder Function Lecture 106 Building Computational Graph Lecture 107 Writing Training and Testing Code Lecture 108 Training, Testing, and Freezing Graph Lecture 109 Setting up Android Project Lecture 110 Setting up UI Lecture 111 Setting up Vocab Dictionary Lecture 112 Formatting Input and Running Through Model Lecture 113 Text Prediction - Mammoth Interactive Section 20: Weather Prediction Lecture 114 Intro and Demo Lecture 115 Tasks List Lecture 116 Retrieving the Data Lecture 117 Formatting Data Sets Lecture 118 Building Computational Graph Lecture 119 Writing Training, Testing, and Evaluating Functions Lecture 120 Training, Testing, and Freezing the Model Lecture 121 Setting up Android Project Lecture 122 Building the UI Lecture 123 Build App Backend and Project Summary Lecture 124 Weather Prediction - Mammoth Interactive Section 21: Introduction to Python Programming Lecture 125 Introduction to Python Lecture 126 Variables Lecture 127 Functions Lecture 128 if Statements Section 22: Lists Lecture 129 Introduction to Lists Section 23: Loops Lecture 130 Introduction to and Examples of using Loops Lecture 131 Getting familiar with while Loops Lecture 132 Breaking and Continuing in Loops Lecture 133 Making Shapes with Loops Lecture 134 Nested Loops and Printing a Tic-Tac-Toe field Section 24: Sets and Dictionaries Lecture 135 Understanding Sets and Dictionaries Lecture 136 An Example for an Invetory List Section 25: Input and Output Lecture 137 Introduction and Implementation of Input and Output Lecture 138 Introduction to and Integrating File Input and Output Lecture 139 An example for a Tic-Tac-Toe Game Lecture 140 An example of a Tic-Tac-Toe Game (Cont'd) Lecture 141 An Example writing Participant data to File Lecture 142 An Example Reading Participant Data from File Lecture 143 Doing some simple statistics with Participant data from File Section 26: Classes Lecture 144 A First Look at Classes Lecture 145 Inheritance and Classes Lecture 146 An Example of Classes using Pets Lecture 147 An Example of Classes using Pets - Dogs Lecture 148 An examples of Classes using Pets - Cats Lecture 149 Taking The Pets Example further and adding humans Section 27: Importing Lecture 150 Introduction to Importing and the Random Library Lecture 151 Another way of importing and using lists with random Lecture 152 Using the Time Library Lecture 153 Introduction to The Math Library Lecture 154 Creating a User guessing Game with Random Lecture 155 Making the Computer guess a random number Section 28: Project Blackjack Game Lecture 156 BlackJack Game Part 1 - Creating and Shuffling a Deck Lecture 157 Blackjack Game Part 2 - Creating the player class Lecture 158 Blackjack Game Part 3 - Expanding the Player Class Lecture 159 Blackjack Game Part 4 - Implementing a bet and win Lecture 160 Blackjack Game part 5 - Implementing the player moves Lecture 161 Blackjack Game Part 6 - Running the Game (Final) Section 29: Error Handling Lecture 162 Getting started with error handling Section 30: Matplotlib Fundamentals Lecture 163 Introduction to Matplotlib Lecture 164 Setup and Installation Lecture 165 Creating Our First Scatter Plot Lecture 166 Line Plots Section 31: Graph Customization Lecture 167 Labels, Title, and a Legend Lecture 168 Changing The Axis Ticks Lecture 169 Adding text into our graphs Lecture 170 Annotating our graph Lecture 171 Changing Figure Size and Saving the Figure Lecture 172 Changing the axis scales Section 32: Advanced Plots Lecture 173 Creating Histograms Lecture 174 Building More Histograms Lecture 175 Changing Histogram Types Lecture 176 Bar Plots Lecture 177 Stack Plots Lecture 178 Pie Charts Lecture 179 Box And Whisker Plots Section 33: Finance Graphs Lecture 180 Creating Figures and Subplots Lecture 181 Getting and Parsing csv data for plotting Lecture 182 Creating a Candlestick plot Lecture 183 Setting Dates for our Candlestick Plot Lecture 184 Reading data directly from Yahoo Lecture 185 Customizing our OHLC graph Section 34: Advanced Graph Customization Lecture 186 Adding grids Lecture 187 Taking a closer look at tick labels Lecture 188 Customising grid lines Lecture 189 Live Graphs Lecture 190 Styles and rcParameters Lecture 191 Sharing an X axis between two plots Lecture 192 Setting Axis Spines Lecture 193 Creating multiple axes in our figure Lecture 194 Creating multiple axes in our figure (cont'd) Lecture 195 Plotting into the multiple axes Lecture 196 Plotting into the multiple axes (cont'd) Section 35: 3D Plotting Lecture 197 Getting started with 3D plotting Lecture 198 Surface plots and colormaps Lecture 199 Wireframes and contour plots Lecture 200 Stacks of histograms and text for 3D plotting Section 36: Sketch Lecture 201 Course Intro and Sketch Tools Lecture 202 Sketch Files - Sketch Tools Lecture 203 Sketch Basics and Online Resources Lecture 204 Plug-ins and Designing your First Mobile app Lecture 205 Your First Mobile App Continued Lecture 206 Sketch Files - Your First Mobile App Lecture 207 Shortcuts and Extra tips Lecture 208 Sketch Files - Shortcuts by Mammoth Interactive Section 37: Learn to Code in HTML Lecture 209 Intro to HTML Lecture 210 Writing our first HTML Lecture 211 Intro to Lists and Comments Lecture 212 Nested Lists Lecture 213 Loading Images Lecture 214 Loading Images in Lists Lecture 215 Links Lecture 216 Images as Link Lecture 217 Mailto Link Lecture 218 Div Element Section 38: Learn to Code in CSS Lecture 219 Introduction Lecture 220 Introducing the Box Model Lecture 221 Writing our First CSS Lecture 222 More CSS Examples Lecture 223 Inheritance Lecture 224 More on Type Selectors Lecture 225 Getting Direct Descendents Lecture 226 Class Intro Lecture 227 Multiple Classes Lecture 228 id Intro Lecture 229 CSS Specificity Lecture 230 Selecting Multiple Pseudo Classes and Sibling Matching Lecture 231 Styling Recipe Page Lecture 232 Loading External Stylesheet Section 39: D3.js Lecture 233 Introduction to Course and D3 Lecture 234 Source Code Lecture 235 Handling Data and Your First Project Lecture 236 Source code Lecture 237 Continuing your First Project Lecture 238 Understanding Scale Lecture 239 Source Code Lecture 240 Complex charts, Animations and Interactivity Lecture 241 Source Code Section 40: Flask Lecture 242 Setting Up and Basic Flask Lecture 243 Setting up Terminals on Windows 7 and Mac Lecture 244 Terminal basic commands and symbols Lecture 245 Source Code - Setting up Flask Lecture 246 Source Code - Basic Flask HTML & CSS Lecture 247 Basic Flask Database Lecture 248 Source Code - Basic Flask Database Lecture 249 Flask Session and Resources Lecture 250 Source Code - Flask Session Lecture 251 Flask Digital Ocean Lecture 252 Flask Digital Ocean Continued Section 41: Xcode Fundamentals Lecture 253 Intro and Demo Lecture 254 General Interface Lecture 255 Files System Lecture 256 ViewController Lecture 257 Storyboard File Lecture 258 Connecting Outlets and Actions Lecture 259 Running an Application Lecture 260 Debugging an Application Lecture 261 Source Code and Art Assets Section 42: Swift 4 Language Basics Lecture 262 Language Basics Topics List Section 43: Variable and Constants Lecture 263 Learning Goals Lecture 264 Intro to Variables and Constants Lecture 265 Primitive types Lecture 266 Strings Lecture 267 Nil Values Lecture 268 Tuples Lecture 269 Type Conversions Lecture 270 Assignment Operators Lecture 271 Conditional Operators Lecture 272 Variables and Constants Text.playground Section 44: Collection Types Lecture 273 Topics List and Learning Objectives Lecture 274 Intro to Collection Types Lecture 275 Creating Arrays Lecture 276 Common Array Operations Lecture 277 Multidimensional Arrays Lecture 278 Ranges Lecture 279 Collection Types Text.playground Section 45: Control flow Lecture 280 Topics List and Learning Objectives Lecture 281 Intro to If and Else Statements Lecture 282 Else If Statements Lecture 283 Multiple Simultaneous Tests Lecture 284 Intro To Switch Statements Lecture 285 Advanced Switch Statement Techniques Lecture 286 Testing for Nil Values Lecture 287 Intro to While Loops Lecture 288 Intro to for...in Loops Lecture 289 Intro to For...In Loops (Cont'd) Lecture 290 Complex Loops and Loop Control statements Lecture 291 Control Flow Text.playground Section 46: Functions Lecture 292 Intro to Functions Lecture 293 Function Parameters Lecture 294 Return Statements Lecture 295 Parameter Variations - Argument Labels Lecture 296 Parameter Variations - Default Values Lecture 297 Parameters Variations - InOut Parameters Lecture 298 Parameter Variations - Variadic Parameters Lecture 299 Returning Multiple Values Simultaneously Lecture 300 Functions Text.playground Section 47: Classes, Struct and Enums Lecture 301 Topics List and Learning Objectives Lecture 302 Intro to Classes Lecture 303 Properties as fields - Add to Class Implementation Lecture 304 Custom Getters and Setters Lecture 305 Calculated Properties Lecture 306 Variable Scope and Self Lecture 307 Lazy and Static Variables Lecture 308 Behaviour as Instance Methods and Class type Methods Lecture 309 Behaviour and Instance Methods Lecture 310 Class Type Methods Lecture 311 Class Instances as Field Variables Lecture 312 Inheritance, Subclassing and SuperClassing Lecture 313 Overriding Initializers Lecture 314 Overriding Properties Lecture 315 Overriding Methods Lecture 316 Structs Overview Lecture 317 Enumerations Lecture 318 Comparisons between Classes, Structs and Enums Lecture 319 Classes, Structs, Enums Text.playground Section 48: Practical MacOS BootCamps Lecture 320 Introduction and UI Elements Lecture 321 Calculator Setup and Tax Calculator Lecture 322 Calculate Tax And Tip - Mammoth Interactive Source Code Lecture 323 Tip Calculator and View Controller Lecture 324 View Controller - Mammoth Interactive Source Code Lecture 325 Constraints Lecture 326 Constraints - Mammoth Interactive Source Code Lecture 327 Constraints Code Lecture 328 Refactor Lecture 329 Refactor - Mammoth Interactive Source Code Lecture 330 MacOsElements - Mammoth Interactive Source Code Section 49: Data Mining With Python Lecture 331 Data Wrangling and Section 1 Lecture 332 Project Files - Data Mining with Mammoth Interactive Lecture 333 Project Files - Data Wrangling with Mammoth Interactive Lecture 334 Data Mining Fundamentals Lecture 335 Project Files - Data Mining fundamentals with Mammoth Interactive Lecture 336 Framework Explained, Taming Big Bank with Data Lecture 337 Project Files - Frameworks with Mammoth Interactive Lecture 338 Mining and Storing Data Lecture 339 Project Files - Mining and Storing with Mammoth Interactive Lecture 340 NLP (Natural Language Processing) Lecture 341 Project Files - NLP with Mammoth Interactive Lecture 342 Summary Challenge Lecture 343 Conclusion Files - Mammoth Interactive Section 50: Introduction to Video Editing Lecture 344 Introduction to the Course Lecture 345 Installing Camtasia Lecture 346 Exploring the Interface Lecture 347 Camtasia Project Files Section 51: Setting Up a Screen Recording Lecture 348 Introduction and Tips for Recording Lecture 349 Creating a Recording Account Lecture 350 Full Screen vs Window Mode Lecture 351 Setting the Recording Resolution Lecture 352 Different Resolutions and their Uses Lecture 353 Tips to Improve Recording Quality and Summary Section 52: Camtasia Recording Lecture 354 Introduction and Workflow Lecture 355 Tools Options Menu Lecture 356 Your First Recording Lecture 357 Viewing your Test Lecture 358 Challenge - VIDEO GAME NARRATION Lecture 359 Mic Etiqutte Lecture 360 Project - Recording Exercise Lecture 361 Webcam, Telprompter, and Summary Section 53: Camtasia Screen Layout Lecture 362 Introduction and Tools Panel Lecture 363 Canvas Lecture 364 Zoom N Pan Lecture 365 Annotations Lecture 366 Yellow Snap Lines Lecture 367 TimeLine Basics, Summary and Challenge Section 54: Camtasia Editing Lecture 368 Introduction and Importing Media Lecture 369 Markers Lecture 370 Split Lecture 371 Working with Audio Lecture 372 Clip Speed Lecture 373 Locking and Disabling tracks Lecture 374 Transitions Lecture 375 Working with Images Lecture 376 Voice Narration Lecture 377 Noise Removal Lecture 378 Smart Focus Lecture 379 Summary and Challenge Section 55: Advance Editing Introduction Lecture 380 Advance Editing Introduction Lecture 381 Zooming Multiple Tracks Lecture 382 Easing Lecture 383 Animations Lecture 384 Behaviors Lecture 385 Color Adjustment Lecture 386 Clip Speed Lecture 387 Remove a Color Lecture 388 Device Frame Lecture 389 Theme Manager Lecture 390 Libraries Lecture 391 Media and Summary Section 56: Camtasia Resources and Tips Lecture 392 Resources and Tips Introduction Lecture 393 Masking Lecture 394 Extending Frames Lecture 395 Working with Video Section 57: Exporting a Project for Youtube Lecture 396 Exporting a Project for Youtube Section 58: Code with C# Lecture 397 Introduction to Course and Types Lecture 398 Operator, Classes , and Additional Types Lecture 399 Statements & Loops Lecture 400 Arrays, Lists, and Strings Lecture 401 Files, Directories, and Debugs Lecture 402 Source code Section 59: Learn to Code with R Lecture 403 Intro to R Lecture 404 Control Flow and Core Concepts Lecture 405 Matrices, Dataframes, Lists and Data Manipulation Lecture 406 GGplot and Intro to Machine learning Lecture 407 Conclusion Lecture 408 Source Code Section 60: Advanced R Lecture 409 Course Overview and Data Setup Lecture 410 Source Code - Setting Up Data - Mammoth Interactive Lecture 411 Functions in R Lecture 412 Source Code - Functions - Mammoth Interactive Lecture 413 Regression Model Lecture 414 Source Code - Regression Models - Mammoth Interactive Lecture 415 Regression Models Continued and Classification Models Lecture 416 Source Code - Classification Models - Mammoth Interactive Lecture 417 Classification Models Continued, RMark Down and Excel Lecture 418 Source Code - RMarkDown And Excel - Mammoth Interactive Lecture 419 Datasets - Mammoth Interactive Section 61: Learn to Code with Java Lecture 420 Introduction and setting up Android Studio Lecture 421 Introduction - Encryption Source Code Lecture 422 Setting up Continued Lecture 423 Java Programming Fundamentals Lecture 424 Source Code - Java Programming Fundamentals Lecture 425 Additional Java fundamentals Lecture 426 Source Code - Additional fundamentals Lecture 427 Classes Lecture 428 Source Code - Classes Lecture 429 Please rate this course Lecture 430 Bonus Lecture - Mammoth Interactive Deals People who want to learn machine learning concepts through practical projects with PyCharm, Python, Android Studio, Java, and TensorFlow,Absolute beginners who want to learn to code for the web in the popular Python programming language.,Anyone who wants to learn the technology that is shaping how we interact with the world around us,Beginners who want to learn how to use data science to make graphs.,Experienced programmers who want to learn a 2D plotting library for Python.,Anyone who is interested in predictive modeling for handling the stock market, weather, and text Homepage Hidden Content Give reaction to this post to see the hidden content. 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