久久综合色88_欧美激情国产日韩精品一区18_午夜精品一区二区三区在线观看 _自拍日韩亚洲一区在线

      ☆ 1、如有部分內容理解不透或消化不好，可免費在以后培訓班中重聽；
      ☆ 2、在課程結束之后,授課老師會留給學員手機和E-mail,免費提供半年的課程技術支持，以便保證培訓后的繼續消化；
      ☆3、合格的學員可享受免費推薦就業機會。
      ☆4、合格學員免費頒發相關工程師等資格證書，提升您的職業資質。

Introduction

Difference between statistical learning (statistical analysis) and machine learning
Adoption of machine learning technology and talent by finance companies
Understanding Different Types of Machine Learning

Supervised learning vs unsupervised learning
Iteration and evaluation
Bias-variance trade-off
Combining supervised and unsupervised learning (semi-supervised learning)
Understanding Machine Learning Languages and Toolsets

Open source vs proprietary systems and software
Python vs R vs Matlab
Libraries and frameworks
Understanding Neural Networks

Understanding Basic Concepts in Finance

Understanding Stocks Trading
Understanding Time Series Data
Understanding Financial Analyses
Machine Learning Case Studies in Finance

Signal Generation and Testing
Feature Engineering
Artificial Intelligence Algorithmic Trading
Quantitative Trade Predictions
Robo-Advisors for Portfolio Management
Risk Management and Fraud Detection
Insurance Underwriting
Introduction to R

Installing the RStudio IDE
Loading R Packages
Data Structures
Vectors
Factors
Lists
Data Frames
Matrices and Arrays
Importing Financial Data into R

Databases, Data Warehouses, and Streaming Data
Distributed Storage and Processing with Hadoop and Spark
Importing Data from a Database
Importing Data from Excel and CSV
Implementing Regression Analysis with R

Linear Regression
Generalizations and Nonlinearity
Evaluating the Performance of Machine Learning Algorithms

Cross-Validation and Resampling
Bootstrap Aggregation (Bagging)
Exercise
Developing an Algorithmic Trading Strategy with R

Setting Up Your Working Environment
Collecting and Examining Stock Data
Implementing a Trend Following Strategy
Backtesting Your Machine Learning Trading Strategy

Learning Backtesting Pitfalls
Components of Your Backtester
Implementing Your Simple Backtester
Improving Your Machine Learning Trading Strategy

KMeans
k-Nearest Neighbors (KNN)
Classification or Regression Trees
Genetic Algorithm
Working with Multi-Symbol Portfolios
Using a Risk Management Framework
Using Event-Driven Backtesting
Evaluating Your Machine Learning Trading Strategy's Performance

Using the Sharpe Ratio
Calculating a Maximum Drawdown
Using Compound Annual Growth Rate (CAGR)
Measuring Distribution of Returns
Using Trade-Level Metrics
Extending your Company's Capabilities

Developing Models in the Cloud
Using GPUs to Accelerate Deep Learning
Applying Deep Learning Neural Networks for Computer Vision, Voice Recognition, and Text Analysis
Summary and Conclusion