Learning Track Machine Learning & Deep Learning in Financial Markets

Learning Track Machine Learning & Deep Learning in Financial Markets – Instant Download!

A highly recommended track for those interested in Machine Learning and its applications in trading.

From simple logistic regression models to complex LSTM models, these courses are perfect for beginners and experts. Learn to tune hyperparameters, gradient boosting, ensemble methods, advanced techniques to make robust predictive models, use unsupervised learning in trading to enhance your algorithms.

LIVE TRADING

Master artificial intelligence techniques and packages essential for financial markets prediction.

Hands-on training on creating predictive models using Regression algorithm, Support Vector Classifier,

Decision Trees, Random Forests, Neural Networks, Activation Layers and more.

Understand why and how predictive models fail and what can be done to improve them; cross-validation techniques, gradient boosting, ensemble methods, hyper-parameter tuning and more.

Use cutting-edge research in artificial intelligence to create improved models: Recurrent Neural Networks, Long Short Term Memory Unit.

Use predictive models in live trading. Save and update your model regularly for live trading. Know how to use the models for live trading.

Describe, implement and list the differences between the workings of k-means clustering algorithm and DBSCAN clustering algorithm.

Code and fine-tune various machine learning algorithms from simple to advance in complexity. If you consider machine learning as an important part of the future in financial markets, you can’t afford to miss this specialization.

SKILLS COVERED

LEARNING TRACK

Machine Learning & Deep Learning in Financial Markets

FOUNDATION

Python For Trading!

Introduction to Machine Learning for Trading

BEGINNER

Trading with Machine Learning: Regression

INTERMEDIATE

Trading with Machine Learning: Classification and SVM

Decision Trees in Trading

Unsupervised Learning in Trading

ADVANCED

Neural Networks in Trading

COURSE FEATURES

Faculty Support on Community

Interactive Coding Practice

Capstone Project using Real Market Data

Trade and Learn Together

Get Certified

PREREQUISITES

Prior experience in programming is required to fully understand the implementation of machine learning algorithm taught in the course.

However, Python programming knowledge is optional. If you want to be able to code and implement the machine learning strategies in Python, you should be able to work with `Dataframes`. These skills are covered in the course `Python for Trading` which is a part of this learning track.

SYLLABUS

Course 1

Python For Trading!

Introduction to Course

Introduction to Python

Functions, Variables and Objects

Data Structures: Lists and Dict

Data Structures: Series and Dataframe

Financial Market Data

Dealing With Financial Data

Data Visualisation

Relative Strength Index

Other Technical Indicators

Backtesting

Performance Metrics

Live Trading on Blueshift

Live Trading Template

Run Codes Locally on Your Machine

Python Codes and Data Files

Course 2

Introduction to Machine Learning for Trading

Introduction

Machine Learning

Types of Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Predict Trend Using Classification

Live Trading on Blueshift

Live Trading Template

Natural Language Processing

Data & Feature Engineering

Run Codes Locally on Your Machine

Summary

Course 3

Trading with Machine Learning: Regression

Problem Statement

Introduction to Data Generation

Data Preprocessing

Regression

Bias and Variance

Applying the Prediction

Creating the Algorithm

Live Trading on Blueshift

Live Trading Template

Run Codes Locally on Your Machine

Downloadable Resources

Course 4

Trading with Machine Learning: Classification and SVM

Introduction

Binary Classification

Multiclass Classification

Support Vector Machine

Prediction and Strategy

Live Trading on Blueshift

Live Trading Template

Run Codes Locally on Your Machine

Downloadable Resources

Course 5

Decision Trees in Trading

Introduction To Decision Trees

Splitting, Stopping and Pruning Methods

Classification Model

Live Trading on Blueshift

Live Trading Template

Regression Trees

Parallel Ensemble Methods

Sequential Ensemble Methods

Cross Validation and Hyperparameter Tuning

Challenges in Live Trading

Run Codes Locally on Your Machine

Downloadable Code

Course 6

Unsupervised Learning in Trading

Introduction to the Course

Introduction to Unsupervised Learning

Clustering

K-Means Clustering

K-Means for Financial Data

Scaling the Data

Feature Selection

Selecting Clusters for K-Means

Analysing Clusters: Hit Ratio

Analysing Clusters: Skewness

Putting It All Together

Curse of Dimensionality

Introduction to Principal Component Analysis

Maths Behind Principal Component Analysis

Principal Component Analysis

Application of Unsupervised Learning for Pairs Trading

DBSCAN

Pairs Trading using Clustering Algorithms

Run Codes Locally on Your Machine

Capstone Project

Automate Trading Strategy Using IBridgePy

Course Summary

Course 7

Neural Networks in Trading

Neural Networks

Live Trading on Blueshift

Live Trading Template

Deep Learning in Trading

Recurrent Neural Networks

Long Short Term Memory Unit (LSTMs)

Cross Validation in Keras

Challenges in Live Trading

Run Codes Locally on Your Machine

Paper and Live Trading

Downloadable Resources

ABOUT AUTHOR

Learning Track Machine Learning & Deep Learning in Financial Markets, What is it included (Content proof: Watch here!)