Srbija Posted September 17, 2023 Share #1 Posted September 17, 2023 Data Science And Machine Learning With Python And Tensorflow Last updated 8/2019 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 64.64 GB | Duration: 114h 33m Create Apps using Machine learning and Data Science to Create Visual Diagrams and graphic bars with Python! What you'll learn Create apps with Python Learn Java language fundamentals Read finance data directly from Yahoo Train and test a model and use it for future predictions Customise our graphs with visuals, a title, labels, text and a legend Understand basic 3D plotting Build apps, learn PyCharm, Android Studio, Machine Learning, TensorFlow models, TensorBoard, and so much more in this epic artificial intelligence course 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. Description We at Mammoth Interactive value input from students like you. Feel free to leave us your feedback. Machine learning is a way for a program to analyze previous data (or past experiences) to make decisions or predict the future.This course was funded through a massively successful Kickstarter campaign.We use frameworks like TensorFlow that make it easy to build, train, test, and use machine learning models. TensorFlow makes machine learning so much more accessible to programmers everywhere.You can expect a complete and comprehensive course that guides you through the basics, then through some simple models. You will end up with a portfolio of apps driven by machine learning, as well as the know-how to create more and expand upon what we build together. Tools, tips, and tricks (with Android support, Python & Java)I start by teaching you the basics of the languages, programs, and underlying concepts of machine learning. You will become an expert ready to build your own machine learning-driven mobile apps, which are the future in mobile app development.Do you also want to learn how to visualize data? Enroll in this course to learn how to do so directly in code. 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: Intro to Android Studio Lecture 1 Intro and Topics List Lecture 2 Downloading and Installing Android Studio Lecture 3 Exploring Interface Lecture 4 Setting up an Emulator and Running Project Lecture 5 Code Section 2: Intro to Java Lecture 6 Intro to Language Basics Lecture 7 Variable Types Lecture 8 Operations on Variables Lecture 9 Array and Lists Lecture 10 Array and List Operations Lecture 11 If and Switch Statements Lecture 12 While Loops Lecture 13 For Loops Lecture 14 Functions Intro Lecture 15 Parameters and Return Values Lecture 16 Classes and Objects Intro Lecture 17 Superclass and Subclasses Lecture 18 Static Variables and Axis Modifiers Section 3: Intro to App Development Lecture 19 Intro To Android App Development Lecture 20 Building Basic UI Lecture 21 Connecting UI to Backend Lecture 22 Implementing Backend and Tidying UI Section 4: Intro to ML Concepts Lecture 23 Intro to ML Lecture 24 Pycharm Files Section 5: Introduction to PyCharm for Python Lecture 25 Intro and Topics List Lecture 26 Downloading and Installing Pycharm and Python Lecture 27 Exploring the Pycharm Interface Lecture 28 Support for Python Problems or Questions Lecture 29 Learning Python with Mammoth Interactive Section 6: Python Language Basics Lecture 30 Intro to Variables Lecture 31 Variables Operations and Conversions Lecture 32 Collection Types Lecture 33 Collections Operations Lecture 34 Control Flow If Statements Lecture 35 While and For Loops Lecture 36 Functions Lecture 37 Classes and Objects Section 7: Intro to Tensorflow Lecture 38 Intro Lecture 39 Topics List Lecture 40 Installing TensorFlow Lecture 41 Importing Tensorflow to Pycharm Lecture 42 Constant Nodes and Sessions Lecture 43 Variable Nodes Lecture 44 Placeholder Nodes Lecture 45 Operation nodes Lecture 46 Loss, Optimizers, and Training Lecture 47 Building a Linear Regression Model Lecture 48 Source Files Section 8: Machine Learning in Android Studio Projects Lecture 49 Coming Up - Machine Learning in Android Studio Projects Section 9: Tensorflow Estimator Lecture 50 Introduction Lecture 51 Topics List Lecture 52 Setting up Prebuilt Estimator Model Lecture 53 Evaluating and Predicting with Prebuilt Model Lecture 54 Building Custom Estimator Function Lecture 55 Testing the Custom Estimator Function Lecture 56 Summary and Model Comparison Lecture 57 Source Files Section 10: Intro to Android Machine Learning Model Import Lecture 58 Intro and Demo Lecture 59 Topics List Lecture 60 Formatting and Saving the Model Lecture 61 Saving the Optimized Graph File Lecture 62 Starting Android Project Lecture 63 Building the UI Lecture 64 Implementing Inference Functionality Lecture 65 Testing and Error Fixing Lecture 66 Source Files Section 11: Simple MNIST Lecture 67 Intro and Demo Lecture 68 Topics List and Intro to MNIST Data Lecture 69 Building Computational Graph Lecture 70 Training and Testing the Model Lecture 71 Saving and Freezing the Graph for Android Import Lecture 72 Setting up Android Studio Project Lecture 73 Building the UI Lecture 74 Loading Digit Images Lecture 75 Formatting Image Data Lecture 76 Making Prediction Using Model Lecture 77 Displaying Results and Summary Lecture 78 Simple MNIST - Mammoth Interactive Section 12: MNIST with Estimator Lecture 79 Introduction Lecture 80 Topics List Lecture 81 Building Custom Estimator Function Lecture 82 Building Input Functions, Training, and Testing Lecture 83 Predicting Using Our Model and Model Comparisons Lecture 84 MNIST With Estimator - Mammoth Interactive Section 13: Advanced MNIST Lecture 85 Intro and Demo Lecture 86 Topics List Lecture 87 Building Neuron Functions Lecture 88 Building the Convolutional Layers Lecture 89 Building Dense, Dropout, and Readout Layers Lecture 90 Writing Loss and Optimizer Functions and Training and Testing Lecture 91 Optimizing Saved Graph Lecture 92 Setting up Android Project Lecture 93 Setting Up UI Lecture 94 Load and Display Digit Images Lecture 95 Formatting Model Input Lecture 96 Displaying Results and Summary Lecture 97 Source Files Section 14: Intro to Tensorboard Lecture 98 Introduction Lecture 99 Examining Computational Graph In Tensorboard Lecture 100 Analyzing Scalars and Histograms Lecture 101 Modifying Model Parameters Across Multiple Runs Lecture 102 Source Code Section 15: Increase Efficiency of Machine Learning Models Lecture 103 Coming Up - Building Efficient Models Lecture 104 Intro to Tensorflow Lite Lecture 105 Source Code Section 16: Text Summarizer Lecture 106 Introduction Lecture 107 Exploring How Model Is Built Lecture 108 Exploring Training and Summarizing Mechanisms Lecture 109 Exploring Training and Summarizing Code Lecture 110 Testing the Model Lecture 111 Text Summarizer Pycharm Source Files Section 17: Object Localization Lecture 112 Introductions Lecture 113 Examining Project Code Lecture 114 Testing with a Mobile Device Section 18: Object Recognition Lecture 115 Introduction Lecture 116 Examining Code Lecture 117 Testing on Mobile Device Section 19: Introduction to Python Programming Lecture 118 Introduction to Python Lecture 119 Variables Lecture 120 Functions Lecture 121 if Statements Section 20: Lists Lecture 122 Introduction to Lists Section 21: Loops Lecture 123 Introduction to and Examples of using Loops Lecture 124 Getting familiar with while Loops Lecture 125 Breaking and Continuing in Loops Lecture 126 Making Shapes with Loops Lecture 127 Nested Loops and Printing a Tic-Tac-Toe field Section 22: Sets and Dictionaries Lecture 128 Understanding Sets and Dictionaries Lecture 129 An Example for an Invetory List Section 23: Input and Output Lecture 130 Introduction and Implementation of Input and Output Lecture 131 Introduction to and Integrating File Input and Output Lecture 132 An example for a Tic-Tac-Toe Game Lecture 133 An example of a Tic-Tac-Toe Game (Cont'd) Lecture 134 An Example writing Participant data to File Lecture 135 An Example Reading Participant Data from File Lecture 136 Doing some simple statistics with Participant data from File Section 24: Classes Lecture 137 A First Look at Classes Lecture 138 Inheritance and Classes Lecture 139 An Example of Classes using Pets Lecture 140 An Example of Classes using Pets - Dogs Lecture 141 An examples of Classes using Pets - Cats Lecture 142 Taking The Pets Example further and adding humans Section 25: Importing Lecture 143 Introduction to Importing and the Random Library Lecture 144 Another way of importing and using lists with random Lecture 145 Using the Time Library Lecture 146 Introduction to The Math Library Lecture 147 Creating a User guessing Game with Random Lecture 148 Making the Computer guess a random number Section 26: Project Blackjack Game Lecture 149 BlackJack Game Part 1 - Creating and Shuffling a Deck Lecture 150 Blackjack Game Part 2 - Creating the player class Lecture 151 Blackjack Game Part 3 - Expanding the Player Class Lecture 152 Blackjack Game Part 4 - Implementing a bet and win Lecture 153 Blackjack Game Part 5 - Implementing the player moves Lecture 154 Blackjack Game Part 6 - Running the Game (Final) Section 27: Error Handling Lecture 155 Getting started with error handling Section 28: Matplotlib Fundamentals Lecture 156 Introduction to Matplotlib Lecture 157 Setup and Installation Lecture 158 Creating Our First Scatter Plot Lecture 159 Line Plots Section 29: Graph Customization Lecture 160 Labels, Title, and a Legend Lecture 161 Changing The Axis Ticks Lecture 162 Adding text into our graphs Lecture 163 Annotating our graph Lecture 164 Changing Figure Size and Saving the Figure Lecture 165 Changing the axis scales Section 30: Advanced Plots Lecture 166 Creating Histograms Lecture 167 Building More Histograms Lecture 168 Changing Histogram Types Lecture 169 Bar Plots Lecture 170 Stack Plots Lecture 171 Pie Charts Lecture 172 Box And Whisker Plots Section 31: Finance Graphs Lecture 173 Creating Figures and Subplots Lecture 174 Getting and Parsing csv data for plotting Lecture 175 Creating a Candlestick plot Lecture 176 Setting Dates for our Candlestick Plot Lecture 177 Reading data directly from Yahoo Lecture 178 Customizing our OHLC graph Section 32: Advanced Graph Customization Lecture 179 Adding Grids Lecture 180 Taking a Closer Look at Tick Labels Lecture 181 Customising Grid Lines Lecture 182 Live Graphs Lecture 183 Styles and rcParameters Lecture 184 Sharing an X axis between two plots Lecture 185 Setting Axis Spines Lecture 186 Creating Multiple Axes in Our Figure Lecture 187 Creating Multiple Axes in Our Figure (cont'd) Lecture 188 Plotting into the Multiple Axes Lecture 189 Plotting into the Multiple Axes (cont'd) Section 33: 3D Plotting Lecture 190 Getting started with 3D plotting Lecture 191 Surface Plots and Colormaps Lecture 192 Wireframes and Contour Plots Lecture 193 Stacks of Histograms and Text for 3D Plotting Section 34: Sketch Lecture 194 Course Intro and Sketch Tools Lecture 195 Sketch Files - Sketch Tools Lecture 196 Sketch Basics and Online Resources Lecture 197 Plug-ins and Designing your First Mobile app Lecture 198 Your First Mobile App Continued Lecture 199 Sketch Files - Your First Mobile App Lecture 200 Shortcuts and Extra tips Lecture 201 Sketch Files - Shortcuts by Mammoth Interactive Section 35: Learn to Code in HTML Lecture 202 Intro to HTML Lecture 203 Writing our first HTML Lecture 204 Intro to Lists and Comments Lecture 205 Nested Lists Lecture 206 Loading Images Lecture 207 Loading Images in Lists Lecture 208 Links Lecture 209 Images as Link Lecture 210 Mailto Link Lecture 211 Div Element Section 36: Learn to Code in CSS Lecture 212 Introduction Lecture 213 Introducing the Box Model Lecture 214 Writing our First CSS Lecture 215 More CSS Examples Lecture 216 Inheritance Lecture 217 More on Type Selectors Lecture 218 Getting Direct Descendents Lecture 219 Class Intro Lecture 220 Multiple Classes Lecture 221 id Intro Lecture 222 CSS Specificity Lecture 223 Selecting Multiple Pseudo Classes and Sibling Matching Lecture 224 Styling Recipe Page Lecture 225 Loading External Stylesheet Section 37: D3.js Lecture 226 Introduction to Course and D3 Lecture 227 Source Code Lecture 228 Handling Data and Your First Project Lecture 229 Source code Lecture 230 Continuing your First Project Lecture 231 Understanding Scale Lecture 232 Source Code Lecture 233 Complex charts, Animations and Interactivity Lecture 234 Source Code Section 38: Flask Lecture 235 Setting Up and Basic Flask Lecture 236 Setting up Terminals on Windows 7 and Mac Lecture 237 Terminal basic commands and symbols Lecture 238 Source Code - Setting up Flask Lecture 239 Source Code - Basic Flask HTML & CSS Lecture 240 Basic Flask Database Lecture 241 Source Code - Basic Flask Database Lecture 242 Flask Session and Resources Lecture 243 Source Code - Flask Session Lecture 244 Flask Digital Ocean Lecture 245 Flask Digital Ocean Continued Section 39: Xcode Fundamentals Lecture 246 Intro and Demo Lecture 247 General Interface Lecture 248 Files System Lecture 249 ViewController Lecture 250 Storyboard File Lecture 251 Connecting Outlets and Actions Lecture 252 Running an Application Lecture 253 Debugging an Application Lecture 254 Source Code and Art Assets Section 40: Swift 4 Language Basics Lecture 255 Language Basics Topics List Section 41: Variable and Constants Lecture 256 Learning Goals Lecture 257 Intro to Variables and Constants Lecture 258 Primitive types Lecture 259 Strings Lecture 260 Nil Values Lecture 261 Tuples Lecture 262 Type Conversions Lecture 263 Assignment Operators Lecture 264 Conditional Operators Lecture 265 Variables and Constants Text.playground Section 42: Collection Types Lecture 266 Topics List and Learning Objectives Lecture 267 Intro to Collection Types Lecture 268 Creating Arrays Lecture 269 Common Array Operations Lecture 270 Multidimensional Arrays Lecture 271 Ranges Lecture 272 Collection Types Text.playground Section 43: Control flow Lecture 273 Topics List and Learning Objectives Lecture 274 Intro to If and Else Statements Lecture 275 Else If Statements Lecture 276 Multiple Simultaneous Tests Lecture 277 Intro To Switch Statements Lecture 278 Advanced Switch Statement Techniques Lecture 279 Testing for Nil Values Lecture 280 Intro to While Loops Lecture 281 Intro to for...in Loops Lecture 282 Intro to For...In Loops (Cont'd) Lecture 283 Complex Loops and Loop Control statements Lecture 284 Control Flow Text.playground Section 44: Functions Lecture 285 Intro to Functions Lecture 286 Function Parameters Lecture 287 Return Statements Lecture 288 Parameter Variations - Argument Labels Lecture 289 Parameter Variations - Default Values Lecture 290 Parameters Variations - InOut Parameters Lecture 291 Parameter Variations - Variadic Parameters Lecture 292 Returning Multiple Values Simultaneously Lecture 293 Functions Text.playground Section 45: Classes, Struct and Enums Lecture 294 Topics List and Learning Objectives Lecture 295 Intro to Classes Lecture 296 Properties as fields - Add to Class Implementation Lecture 297 Custom Getters and Setters Lecture 298 Calculated Properties Lecture 299 Variable Scope and Self Lecture 300 Lazy and Static Variables Lecture 301 Behaviour as Instance Methods and Class type Methods Lecture 302 Behaviour and Instance Methods Lecture 303 Class Type Methods Lecture 304 Class Instances as Field Variables Lecture 305 Inheritance, Subclassing and SuperClassing Lecture 306 Overriding Initializers Lecture 307 Overriding Properties Lecture 308 Overriding Methods Lecture 309 Structs Overview Lecture 310 Enumerations Lecture 311 Comparisons between Classes, Structs and Enums Lecture 312 Classes, Structs, Enums Text.playground Section 46: Practical MacOS BootCamps Lecture 313 Introduction and UI Elements Lecture 314 Calculator Setup and Tax Calculator Lecture 315 Calculate Tax And Tip - Mammoth Interactive Source Code Lecture 316 Tip Calculator and View Controller Lecture 317 View Controller - Mammoth Interactive Source Code Lecture 318 Constraints Lecture 319 Constraints - Mammoth Interactive Source Code Lecture 320 Constraints Code Lecture 321 Refactor Lecture 322 Refactor - Mammoth Interactive Source Code Lecture 323 MacOsElements - Mammoth Interactive Source Code Section 47: Data Mining With Python Lecture 324 Data Wrangling and Section 1 Lecture 325 Project Files - Data Mining with Mammoth Interactive Lecture 326 Project Files - Data Wrangling with Mammoth Interactive Lecture 327 Data Mining Fundamentals Lecture 328 Project Files - Data Mining fundamentals with Mammoth Interactive Lecture 329 Framework Explained, Taming Big Bank with Data Lecture 330 Project Files - Frameworks with Mammoth Interactive Lecture 331 Mining and Storing Data Lecture 332 Project Files - Mining and Storing with Mammoth Interactive Lecture 333 NLP (Natural Language Processing) Lecture 334 Project Files - NLP with Mammoth Interactive Lecture 335 Summary Challenge Lecture 336 Conclusion Files - Mammoth Interactive Section 48: Introduction to Video Editing Lecture 337 Introduction to the Course Lecture 338 Installing Camtasia Lecture 339 Exploring the Interface Lecture 340 Camtasia Project Files Section 49: Setting Up a Screen Recording Lecture 341 Introduction and Tips for Recording Lecture 342 Creating a Recording Account Lecture 343 Full Screen vs Window Mode Lecture 344 Setting the Recording Resolution Lecture 345 Different Resolutions and their Uses Lecture 346 Tips to Improve Recording Quality and Summary Section 50: Camtasia Recording Lecture 347 Introduction and Workflow Lecture 348 Tools Options Menu Lecture 349 Your First Recording Lecture 350 Viewing your Test Lecture 351 Challenge - VIDEO GAME NARRATION Lecture 352 Mic Etiqutte Lecture 353 Project - Recording Exercise Lecture 354 Webcam, Telprompter, and Summary Section 51: Camtasia Screen Layout Lecture 355 Introduction and Tools Panel Lecture 356 Canvas Lecture 357 Zoom N Pan Lecture 358 Annotations Lecture 359 Yellow Snap Lines Lecture 360 TimeLine Basics, Summary and Challenge Section 52: Camtasia Editing Lecture 361 Introduction and Importing Media Lecture 362 Markers Lecture 363 Split Lecture 364 Working with Audio Lecture 365 Clip Speed Lecture 366 Locking and Disabling tracks Lecture 367 Transitions Lecture 368 Working with Images Lecture 369 Voice Narration Lecture 370 Noise Removal Lecture 371 Smart Focus Lecture 372 Summary and Challenge Section 53: Advance Editing Introduction Lecture 373 Advance Editing Introduction Lecture 374 Zooming Multiple Tracks Lecture 375 Easing Lecture 376 Animations Lecture 377 Behaviors Lecture 378 Color Adjustment Lecture 379 Clip Speed Lecture 380 Remove a Color Lecture 381 Device Frame Lecture 382 Theme Manager Lecture 383 Libraries Lecture 384 Media and Summary Section 54: Camtasia Resources and Tips Lecture 385 Resources and Tips Introduction Lecture 386 Masking Lecture 387 Extending Frames Lecture 388 Working with Video Section 55: Exporting a Project for Youtube Lecture 389 Exporting a Project for Youtube Section 56: Code with C# Lecture 390 Introduction to Course and Types Lecture 391 Operator, Classes , and Additional Types Lecture 392 Statements & Loops Lecture 393 Arrays, Lists, and Strings Lecture 394 Files, Directories, and Debugs Lecture 395 Source code Section 57: Learn to Code with R Lecture 396 Intro to R Lecture 397 Control Flow and Core Concepts Lecture 398 Matrices, Dataframes, Lists and Data Manipulation Lecture 399 GGplot and Intro to Machine learning Lecture 400 Conclusion Lecture 401 Source Code Section 58: Advanced R Lecture 402 Course Overview and Data Setup Lecture 403 Source Code - Setting Up Data - Mammoth Interactive Lecture 404 Functions in R Lecture 405 Source Code - Functions - Mammoth Interactive Lecture 406 Regression Model Lecture 407 Source Code - Regression Models - Mammoth Interactive Lecture 408 Regression Models Continued and Classification Models Lecture 409 Source Code - Classification Models - Mammoth Interactive Lecture 410 Classification Models Continued, RMark Down and Excel Lecture 411 Source Code - RMarkDown And Excel - Mammoth Interactive Lecture 412 Datasets - Mammoth Interactive Section 59: Learn to Code with Java Lecture 413 Introduction and setting up Android Studio Lecture 414 Introduction - Encryption Source Code Lecture 415 Setting up Continued Lecture 416 Java Programming Fundamentals Lecture 417 Source Code - Java Programming Fundamentals Lecture 418 Additional Java fundamentals Lecture 419 Source Code - Additional fundamentals Lecture 420 Classes Lecture 421 Source Code - Classes Lecture 422 Please rate this course Lecture 423 Bonus Course 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 and use data science to make graphs.,Anyone who wants to learn the technology that is shaping how we interact with the world around us,Anyone who wants to use data for prediction, recognition, and classification,Experienced programmers who want to learn a 2D plotting library for Python. 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