Udacity – Artificial Intelligence For Trading nd880 v1.0.0

Description

Udacity – Artificial Intelligence For Trading nd880 v1.0.0

In this program, you’ll analyze real data and build financial models for trading. Whether you want to pursue a new job in finance, launch yourself on the path to a quant trading career, or master the latest AI applications in quantitative finance, this program offers you the opportunity to master valuable data and AI skills.

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
12 – 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

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