Mastering Fintech And Machine Learning!

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