Udacity – AI for Trading v1 0 0 nd880 v1 0 0

Includes:

02_Get Help from Peers and Mentors

04_Stock Prices

06_Data Processing

08_Momentum Trading

01_Quant Workflow

03_Regression

Includes:
01_Welcome to the Nanodegree Program
02_Get Help from Peers and Mentors
03_Get Help with Your Account
04_Stock Prices
05_Market Mechanics
06_Data Processing
07_Stock Returns
08_Momentum Trading
09_Project 1 Trading with Momentum
01_Quant Workflow
02_Outliers and Filtering
03_Regression
04_Time Series Modeling
05_Volatility
06_Pairs Trading and Mean Reversion
07_Project 2 Breakout Strategy
01_Stocks, Indices, Funds
02_ETFs
03_Portfolio Risk and Return
04_Portfolio Optimization
05_Project 3 Smart Beta and Portfolio Optimization
01_Factors
02_Factor Models and Types of Factors
03_Risk Factor Models
04_Time Series and Cross Sectional Risk Models
05_Risk Factor Models with PCA
06_Alpha Factors
07_Alpha Factor Research Methods
08_Advanced Portfolio Optimization
09_Project 4 Alpha Research and Factor Modeling
_Welcome To Term II
02_Intro to Natural Language Processing
03_Text Processing
04_Feature Extraction
05_Financial Statements
06_Basic NLP Analysis
07_Project 5 NLP on Financial Statements
01_Introduction to Neural Networks
02_Training Neural Networks
03_Deep Learning with PyTorch
04_Recurrent Neural Networks
05_Embeddings Word2Vec
06_Sentiment Prediction RNN
07_Project 6 Sentiment Analysis with Neural Networks
01_Overview
02_Decision Trees
03_Model Testing and Evaluation
04_Random Forests
05_Feature Engineering
06_Overlapping Labels
07_Feature Importance
08_Project 7 Combining Signals for Enhanced Alpha
01_Strengthen Your Online Presence Using LinkedIn
02_Optimize Your GitHub Profile
01_Intro to Backtesting
02_Optimization with Transaction Costs
03_Attribution
04_Project 8 Backtesting
01_Why Python Programming
02_Data Types and Operators
03_Control Flow
04_Functions
05_Scripting
01_Introduction
02_Vectors
03_Linear Combination
04_Linear Transformation and Matrices
01_Jupyter Notebooks
02_NumPy
03_Pandas
01_Descriptive Statistics – Part I
02_Descriptive Statistics – Part II
03_Admissions Case Study
04_Probability
05_Binomial Distribution
06_Conditional Probability
07_Bayes Rule
08_Python Probability Practice
09_Normal Distribution Theory
10_Sampling distributions and the Central Limit Theorem
11_Confidence Intervals
12_Hypothesis Testing
13_Case Study AB tests
01_Linear Regression
02_Naive Bayes
03_Clustering
04_Decision Trees
05_Introduction to Kalman Filters
01_Introduction to Neural Networks
01_Intro to Computer Vision
01_Intro to NLP