AI For Trading nd880 v1.0.0

[renato]

AI For Trading nd880 v1.0.0
WEBRip | English | MP4 | 1280 x 720 | AVC ~170 Kbps | 30 fps
AAC | 126 Kbps | 44.1 KHz | 2 channels | 39:34:27 | 7.32 GB
Genre: Video Tutorial

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.

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
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

 
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