Mastering Fintech And Machine Learning!
Mastering Fintech And Machine Learning!
Last updated 8/2019
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 58.93 GB | Duration: 110h 6m
Learn how successful people trade and invest! Dominate the world of Finance with Python and Machine Learning!
What you'll learn
How Stocks Are Created
Understand Stock Market Fundamentals
Read Algorithms, Strategies, and Different Kinds of Graphs
Get your hands dirty with real world coding examples and learn to code in Python.
Handle Inputs and Outputs, Imports, Errors, and use Lists, Loops, Sets, and Dictionaries in Python.
And More!
Requirements
These tutorials were recorded on a Mac computer using Python 3.5.
To follow along with these tutorials, you will need to install Python. Python is compatible with Mac and PC.
Description
We at Mammoth Interactive value input from students like you. Feel free to leave us your feedback. Learn complete Python trading and coding from scratch. Become an expert in data analytics and real-world financial analysis. We are proud to present one of the most interesting and complete courses we've created so far. No experience is required.Through Mammoth Interactive's self-paced online learning, finance theory is not overwhelming like it would be in a regular university.Wall Street Coder will guide you through everything you need to know to use Python for Finance and Algorithmic Trading. We'll start off by learning the fundamentals of Python and proceed to learn about machine learning and Quantopian.The lessons are supplemented with handful of helpful source files you can refer back to at any time - forever! PLUS: Offline viewing on the Udemy iOS app. Lifetime access to all content.If you have always wanted to learn to code, this is your place to start. In this course, you will learn how to code in the Python 3.5 programming language. Whether you have or have not coded before, you can learn how to use Python. Python is a popular programming language that is useful to know because of its versatility. Python is easy to understand and can be used for many different environments.Cross-platform apps and 3D environments are often made in Python.This course does not assume any level of experience and is therefore perfect for beginners! We will cover basic programming concepts for people who have never programmed before. This course covers key topics in Python and coding in general, including variables, loops, and classes. Moreover, you will learn how to handle input, output, and errors.To learn how to use Python, we will create our own functioning Blackjack game! In this game, you will receive cards, submit bets, and keep track of your score. By the end of this course, you will be able to use the coding knowledge you gained to make your own apps and environments in Python.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: Python Language Basics
Lecture 1 Intro to Python
Lecture 2 Summary of Python
Section 2: Variables
Lecture 3 Variables
Lecture 4 Variables Demo
Lecture 5 Variable Operators
Lecture 6 Variable Operators Demo
Lecture 7 Source Files - Variables
Section 3: Collections
Lecture 8 Lists
Lecture 9 Tuples
Lecture 10 Dictionaries
Lecture 11 Matrices
Lecture 12 Source Files - Collections
Section 4: Control Flow
Lecture 13 If Statements
Lecture 14 While Loops
Lecture 15 For Loops
Lecture 16 Control Flow Statements
Lecture 17 Source Files - Control Flow
Section 5: Functions
Lecture 18 Function
Lecture 19 Parameters and Return Values
Lecture 20 Source Files - Functions
Section 6: Classes and Objects
Lecture 21 Classes and Objects
Lecture 22 Using Objects
Lecture 23 Static Class Members
Lecture 24 Inheritance
Lecture 25 Source Files - Classes and Objects
Section 7: Numpy Tutorials
Lecture 26 Numpy Course Intro
Lecture 27 Installing Numpy
Lecture 28 Numpy Data Types
Lecture 29 Numpy Arrays
Lecture 30 Numpy Array Functions
Lecture 31 Creating Numpy Matrices
Lecture 32 Numpy Matrix Functions
Lecture 33 Numpy Course Summary
Lecture 34 Source Files - Numpy Tutorials
Section 8: Pandas Tutorials
Lecture 35 Pandas 101 Course
Lecture 36 Installing Pandas
Lecture 37 Pandas Data Types
Lecture 38 Pandas Data Types Demo
Lecture 39 Creating Series Demo
Lecture 40 Creating Series Demo (Cont'd)
Lecture 41 Series Function
Lecture 42 Series Functions Demo Part 1
Lecture 43 Series Functions Demo Part 2
Lecture 44 Creating Dataframes
Lecture 45 Creating Dataframe Demo
Lecture 46 Dataframers Functions
Lecture 47 Dataframes Functions Demo (Part 1)
Lecture 48 Dataframes Functions Demo (Part 2)
Lecture 49 Pandas 101 Course Summary
Lecture 50 Source Files - Pandas Tutorials
Section 9: PyPlot Tutorials
Lecture 51 Pyplot Course Intro
Lecture 52 Installing Pyplot
Lecture 53 Plotting with PyPlot
Lecture 54 Plotting with PyPlot Demo
Lecture 55 Customizing Graphs
Lecture 56 Customizind Graph Demo
Lecture 57 Different Graph Types
Lecture 58 PyPlot Course summary
Lecture 59 Intro to Pyplot Slides
Lecture 60 Source Files - PyPlot Tutorials
Section 10: Basics of Programming
Lecture 61 Introduction to Python
Lecture 62 Variables
Lecture 63 Functions
Lecture 64 if Statements
Section 11: Lists
Lecture 65 Introduction to Lists
Section 12: Loops
Lecture 66 Introduction to and Examples of using Loops
Lecture 67 Getting familiar with while Loops
Lecture 68 Breaking and Continuing in Loops
Lecture 69 Making Shapes with Loops
Lecture 70 Nested Loops and Printing a Tic-Tac-Toe field
Section 13: Sets and Dictionaries
Lecture 71 Understanding Sets and Dictionaries
Lecture 72 An Example for an Invetory List
Section 14: Input and Output
Lecture 73 Introduction and Implementation of Input and Output
Lecture 74 Introduction to and Integrating File Input and Output
Lecture 75 An example for a Tic-Tac-Toe Game
Lecture 76 An example of a Tic-Tac-Toe Game (Cont'd)
Lecture 77 An Example writing Participant data to File
Lecture 78 An Example Reading Participant Data from File
Lecture 79 Doing some simple statistics with Participant data from File
Section 15: Classes
Lecture 80 A First Look at Classes
Lecture 81 Inheritance and Classes
Lecture 82 An Example of Classes using Pets
Lecture 83 An Example of Classes using Pets - Dogs
Lecture 84 An examples of Classes using Pets - Cats
Lecture 85 Taking The Pets Example further and adding humans
Section 16: Importing
Lecture 86 Introduction to Importing and the Random Library
Lecture 87 Another way of importing and using lists with random
Lecture 88 Using the Time Library
Lecture 89 Introduction to The Math Library
Lecture 90 Creating a User guessing Game with Random
Lecture 91 Making the Computer guess a random number
Section 17: Project Blackjack Game
Lecture 92 BlackJack Game Part 1 - Creating and Shuffling a Deck
Lecture 93 Blackjack Game Part 2 - Creating the player class
Lecture 94 Blackjack Game Part 3 - Expanding the Player Class
Lecture 95 Blackjack Game Part 4 - Implementing a bet and win
Lecture 96 Blackjack Game Part 5 - Implementing the player moves
Lecture 97 Blackjack Game Part 6 - Running the Game (Final)
Section 18: Error Handling
Lecture 98 Getting started with error handling
Section 19: Stock Data API
Lecture 99 Stock Data Api Course Intro
Lecture 100 Exploring API
Lecture 101 Constructing a URL
Lecture 102 Fetching Data
Lecture 103 Parsing Data
Lecture 104 Graphing Data
Lecture 105 Stock Data API Course Summary
Lecture 106 Wall Street Trader - Fetching and Parsing Stock Data
Lecture 107 Source Files - Stock Data API Code
Section 20: Technical Stock Analysis
Lecture 108 Technical Analysis Course Intro
Lecture 109 Learn the Lingo
Lecture 110 Buying and Selling
Lecture 111 Reading Stock Graphs
Lecture 112 Common Technical Indicators
Lecture 113 Trading Strategies
Lecture 114 Technical Analysis Course Summary
Lecture 115 Wall Street Trader - Technical Analysis
Section 21: Intro to Tensorflow and Machine Learning
Lecture 116 Tensorflow and Machine Learning Course Intro
Lecture 117 Intro to Machine Learning
Lecture 118 Intro to Tensorflow
Lecture 119 Installing Tensforflow
Lecture 120 Tensorflow Variable Nodes
Lecture 121 Running Graphs with Tensorflow Sessions
Lecture 122 Tensorflow Operations
Lecture 123 Simple Linear Regression Model
Lecture 124 Tensorflow and Machine Learning Course Summary
Lecture 125 Wall Street Trader - Tensorflow and ML
Lecture 126 Source Files - Tensorflow Practice
Section 22: Simple Stock Market Prediciton
Lecture 127 Simple Stock Market Prediction Intro
Lecture 128 Exploring Stock API
Lecture 129 Fetching Stock Data
Lecture 130 Creating Datasets
Lecture 131 Building the Model
Lecture 132 Training and Testing the Model
Lecture 133 Simple Stock Prediction Summary
Lecture 134 Wall Street Trader - Simple Stock Prediction
Lecture 135 Source Files - Simple Stock Prediction Model
Section 23: Stock Price Prediction
Lecture 136 Stock Price Prediction Course Intro
Lecture 137 Intro to Keras
Lecture 138 Intro to LSTM Cells
Lecture 139 Fecthing and Transforming data
Lecture 140 Creating Datasets
Lecture 141 Building the Model
Lecture 142 Training and Testing the Model
Lecture 143 Understanding Model Output
Lecture 144 Stock Price Prediction Course Summary
Lecture 145 Wall Street Trader - Stock Price Prediction
Lecture 146 Source Files - Stock Price Prediction
Section 24: Quantopian
Lecture 147 Quantopian 101 Course Intro
Lecture 148 Intro to Quantopian
Lecture 149 Exploring Quantopian Website
Lecture 150 Quantopian Pipeline Intro
Lecture 151 Fetching Data
Lecture 152 Running a Pipeline
Lecture 153 Fetching Factors
Lecture 154 Applying Filters
Lecture 155 Building a Complete Pipeline
Lecture 156 Quantopian Algorithm IDE Intro
Lecture 157 Algorithm IDE Basics
Lecture 158 Making Trades
Lecture 159 Conditional Trades
Lecture 160 Important Pipelines
Lecture 161 Creating and Testing a Portfolio
Lecture 162 Quantopian 101 Course Summary
Lecture 163 Wall Street Trader - Intro to Quantopian
Section 25: Sketch
Lecture 164 Course Intro and Sketch Tools
Lecture 165 Sketch Files - Sketch Tools
Lecture 166 Sketch Basics and Online Resources
Lecture 167 Plug-ins and Designing your First Mobile app
Lecture 168 Your First Mobile App Continued
Lecture 169 Sketch Files - Your First Mobile App
Lecture 170 Shortcuts and Extra tips
Lecture 171 Sketch Files - Shortcuts by Mammoth Interactive
Section 26: Learn to Code in HTML
Lecture 172 Intro to HTML
Lecture 173 Writing our first HTML
Lecture 174 Intro to Lists and Comments
Lecture 175 Nested Lists
Lecture 176 Loading Images
Lecture 177 Loading Images in Lists
Lecture 178 Links
Lecture 179 Images as Link
Lecture 180 Mailto Link
Lecture 181 Div Element
Section 27: Learn to Code in CSS
Lecture 182 Introduction
Lecture 183 Introducing the Box Model
Lecture 184 Writing our First CSS
Lecture 185 More CSS Examples
Lecture 186 Inheritance
Lecture 187 More on Type Selectors
Lecture 188 Getting Direct Descendents
Lecture 189 Class Intro
Lecture 190 Multiple Classes
Lecture 191 id Intro
Lecture 192 CSS Specificity
Lecture 193 Selecting Multiple Pseudo Classes and Sibling Matching
Lecture 194 Styling Recipe Page
Lecture 195 Loading External Stylesheet
Section 28: D3.js
Lecture 196 Introduction to Course and D3
Lecture 197 Source Code
Lecture 198 Handling Data and Your First Project
Lecture 199 Source code
Lecture 200 Continuing your First Project
Lecture 201 Understanding Scale
Lecture 202 Source Code
Lecture 203 Complex charts, Animations and Interactivity
Lecture 204 Source Code
Section 29: Introduction to PyCharm
Lecture 205 Downloading and Installing Pycharm and Python
Lecture 206 Support for Python Problems or Questions
Lecture 207 Exploring Pycharm
Lecture 208 Learning Python with Mammoth Interactive
Section 30: Python Language Basics
Lecture 209 Intro to Variables
Lecture 210 Variables Operations and Conversions
Lecture 211 Collection Types
Lecture 212 Collections Operations
Lecture 213 Control Flow If Statements
Lecture 214 While and For Loops
Lecture 215 Functions
Lecture 216 Classes and Objects
Section 31: Flask
Lecture 217 Setting Up and Basic Flask
Lecture 218 Setting up Terminals on Windows 7 and Mac
Lecture 219 Terminal basic commands and symbols
Lecture 220 Source Code - Setting up Flask
Lecture 221 Source Code - Basic Flask HTML & CSS
Lecture 222 Basic Flask Database
Lecture 223 Source Code - Basic Flask Database
Lecture 224 Flask Session and Resources
Lecture 225 Source Code - Flask Session
Lecture 226 Flask Digital Ocean
Lecture 227 Flask Digital Ocean Continued
Section 32: Xcode Fundamentals
Lecture 228 Intro and Demo
Lecture 229 General Interface
Lecture 230 Files System
Lecture 231 ViewController
Lecture 232 Storyboard File
Lecture 233 Connecting Outlets and Actions
Lecture 234 Running an Application
Lecture 235 Debugging an Application
Lecture 236 Source Code and Art Assets
Section 33: Swift 4 Language Basics
Lecture 237 Language Basics Topics List
Section 34: Variable and Constants
Lecture 238 Learning Goals
Lecture 239 Intro to Variables and Constants
Lecture 240 Primitive types
Lecture 241 Strings
Lecture 242 Nil Values
Lecture 243 Tuples
Lecture 244 Type Conversions
Lecture 245 Assignment Operators
Lecture 246 Conditional Operators
Lecture 247 Variables and Constants Text.playground
Section 35: Collection Types
Lecture 248 Topics List and Learning Objectives
Lecture 249 Intro to Collection Types
Lecture 250 Creating Arrays
Lecture 251 Common Array Operations
Lecture 252 Multidimensional Arrays
Lecture 253 Ranges
Lecture 254 Collection Types Text.playground
Section 36: Control flow
Lecture 255 Topics List and Learning Objectives
Lecture 256 Intro to If and Else Statements
Lecture 257 Else If Statements
Lecture 258 Multiple Simultaneous Tests
Lecture 259 Intro To Switch Statements
Lecture 260 Advanced Switch Statement Techniques
Lecture 261 Testing for Nil Values
Lecture 262 Intro to While Loops
Lecture 263 Intro to for...in Loops
Lecture 264 Intro to For...In Loops (Cont'd)
Lecture 265 Complex Loops and Loop Control statements
Lecture 266 Control Flow Text.playground
Section 37: Functions
Lecture 267 Intro to Functions
Lecture 268 Function Parameters
Lecture 269 Return Statements
Lecture 270 Parameter Variations - Argument Labels
Lecture 271 Parameter Variations - Default Values
Lecture 272 Parameters Variations - InOut Parameters
Lecture 273 Parameter Variations - Variadic Parameters
Lecture 274 Returning Multiple Values Simultaneously
Lecture 275 Functions Text.playground
Section 38: Classes, Struct and Enums
Lecture 276 Topics List and Learning Objectives
Lecture 277 Intro to Classes
Lecture 278 Properties as fields - Add to Class Implementation
Lecture 279 Custom Getters and Setters
Lecture 280 Calculated Properties
Lecture 281 Variable Scope and Self
Lecture 282 Lazy and Static Variables
Lecture 283 Behaviour as Instance Methods and Class type Methods
Lecture 284 Behaviour and Instance Methods
Lecture 285 Class Type Methods
Lecture 286 Class Instances as Field Variables
Lecture 287 Inheritance, Subclassing and SuperClassing
Lecture 288 Overriding Initializers
Lecture 289 Overriding Properties
Lecture 290 Overriding Methods
Lecture 291 Structs Overview
Lecture 292 Enumerations
Lecture 293 Comparisons between Classes, Structs and Enums
Lecture 294 Classes, Structs, Enums Text.playground
Section 39: Practical MacOS BootCamps
Lecture 295 Introduction and UI Elements
Lecture 296 Calculator Setup and Tax Calculator
Lecture 297 Calculate Tax And Tip - Mammoth Interactive Source Code
Lecture 298 Tip Calculator and View Controller
Lecture 299 View Controller - Mammoth Interactive Source Code
Lecture 300 Constraints
Lecture 301 Constraints - Mammoth Interactive Source Code
Lecture 302 Constraints Code
Lecture 303 Refactor
Lecture 304 Refactor - Mammoth Interactive Source Code
Lecture 305 MacOsElements - Mammoth Interactive Source Code
Section 40: Data Mining With Python
Lecture 306 Data Wrangling and Section 1
Lecture 307 Project Files - Data Mining with Mammoth Interactive
Lecture 308 Project Files - Data Wrangling with Mammoth Interactive
Lecture 309 Data Mining Fundamentals
Lecture 310 Project Files - Data Mining fundamentals with Mammoth Interactive
Lecture 311 Framework Explained, Taming Big Bank with Data
Lecture 312 Project Files - Frameworks with Mammoth Interactive
Lecture 313 Mining and Storing Data
Lecture 314 Project Files - Mining and Storing with Mammoth Interactive
Lecture 315 NLP (Natural Language Processing)
Lecture 316 Project Files - NLP with Mammoth Interactive
Lecture 317 Summary Challenge
Lecture 318 Conclusion Files - Mammoth Interactive
Section 41: Introduction to Video Editing
Lecture 319 Introduction to the Course
Lecture 320 Installing Camtasia
Lecture 321 Exploring the Interface
Lecture 322 Camtasia Project Files
Section 42: Setting Up a Screen Recording
Lecture 323 Introduction and Tips for Recording
Lecture 324 Creating a Recording Account
Lecture 325 Full Screen vs Window Mode
Lecture 326 Setting the Recording Resolution
Lecture 327 Different Resolutions and their Uses
Lecture 328 Tips to Improve Recording Quality and Summary
Section 43: Camtasia Recording
Lecture 329 Introduction and Workflow
Lecture 330 Tools Options Menu
Lecture 331 Your First Recording
Lecture 332 Viewing your Test
Lecture 333 Challenge - VIDEO GAME NARRATION
Lecture 334 Mic Etiqutte
Lecture 335 Project - Recording Exercise
Lecture 336 Webcam, Telprompter, and Summary
Section 44: Camtasia Screen Layout
Lecture 337 Introduction and Tools Panel
Lecture 338 Canvas
Lecture 339 Zoom N Pan
Lecture 340 Annotations
Lecture 341 Yellow Snap Lines
Lecture 342 TimeLine Basics, Summary and Challenge
Section 45: Camtasia Editing
Lecture 343 Introduction and Importing Media
Lecture 344 Markers
Lecture 345 Split
Lecture 346 Working with Audio
Lecture 347 Clip Speed
Lecture 348 Locking and Disabling tracks
Lecture 349 Transitions
Lecture 350 Working with Images
Lecture 351 Voice Narration
Lecture 352 Noise Removal
Lecture 353 Smart Focus
Lecture 354 Summary and Challenge
Section 46: Advance Editing Introduction
Lecture 355 Advance Editing Introduction
Lecture 356 Zooming Multiple Tracks
Lecture 357 Easing
Lecture 358 Animations
Lecture 359 Behaviors
Lecture 360 Color Adjustment
Lecture 361 Clip Speed
Lecture 362 Remove a Color
Lecture 363 Device Frame
Lecture 364 Theme Manager
Lecture 365 Libraries
Lecture 366 Media and Summary
Section 47: Camtasia Resources and Tips
Lecture 367 Resources and Tips Introduction
Lecture 368 Masking
Lecture 369 Extending Frames
Lecture 370 Working with Video
Section 48: Exporting a Project for Youtube
Lecture 371 Exporting a Project for Youtube
Section 49: Code with C#
Lecture 372 Introduction to Course and Types
Lecture 373 Operator, Classes , and Additional Types
Lecture 374 Statements & Loops
Lecture 375 Arrays, Lists, and Strings
Lecture 376 Files, Directories, and Debugs
Lecture 377 Source code
Section 50: Learn to Code with R
Lecture 378 Intro to R
Lecture 379 Control Flow and Core Concepts
Lecture 380 Matrices, Dataframes, Lists and Data Manipulation
Lecture 381 GGplot and Intro to Machine learning
Lecture 382 Conclusion
Lecture 383 Source Code
Section 51: Advanced R
Lecture 384 Course Overview and Data Setup
Lecture 385 Source Code - Setting Up Data - Mammoth Interactive
Lecture 386 Functions in R
Lecture 387 Source Code - Functions - Mammoth Interactive
Lecture 388 Regression Model
Lecture 389 Source Code - Regression Models - Mammoth Interactive
Lecture 390 Regression Models Continued and Classification Models
Lecture 391 Source Code - Classification Models - Mammoth Interactive
Lecture 392 Classification Models Continued, RMark Down and Excel
Lecture 393 Source Code - RMarkDown And Excel - Mammoth Interactive
Lecture 394 Datasets - Mammoth Interactive
Section 52: Learn to Code with Java
Lecture 395 Introduction and setting up Android Studio
Lecture 396 Introduction - Encryption Source Code
Lecture 397 Setting up Continued
Lecture 398 Java Programming Fundamentals
Lecture 399 Source Code - Java Programming Fundamentals
Lecture 400 Additional Java fundamentals
Lecture 401 Source Code - Additional fundamentals
Lecture 402 Classes
Lecture 403 Source Code - Classes
Lecture 404 Please rate this course
Lecture 405 Bonus Lecture - Mammoth Interactive Deals
This course does not assume any prior coding knowledge.,People interested in finance and investing,Coders who want to specialize in finance,Anyone who wants to learn programming for an in-demand field,Finance professionals who want to learn FinTech
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