Machine Learning for Finance

  Become the official problem solver of your organization with Machine Learning tools. Unlock your decision-making skills with our ML course.

(ML-FINANCE.AW1) / ISBN : 978-1-64459-644-9
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About This Course

SDecision-making, risk management & mitigating financial threats just got easier with our Machine Learning for Finance Beginners course.

Skills You’ll Get

  Utilize Machine learning for statistics & data mining.  Explore Automatic cluster detection in data mining.  Learn to manage Structured and unstructured data Understand Data handling in NLP Explore Image recognition, Biometric recognition & Software vulnerabilities.  Work on AI neural networks Analyze types of problems and solutions in Financial Markets Utilize AI for innovation in FinTechs  Detect and prevent fraud with risk management techniques.  Build secure eKYC networks and Anti-Fraud Policy

1

Preface

2

Introduction

  • Introduction
  • How machines are taught
  • Factors contributing to the success of machine learning
  • Machine learning and artificial intelligence
  • Machine learning and deep learning
  • Machine learning and statistics
  • Machine learning and data mining
  • Machine learning in finance
  • Importance of machine learning in finance
  • Robo-warning
  • How to utilize machine learning in finance
  • Utilize outsider machine learning arrangements
  • Development and combination
  • How is machine learning used today
  • Conclusion
3

Naive Bayes, Normal Distribution, and Automatic Clustering

  • Introduction
  • Naive Bayes
  • Normal distribution
  • Automatic cluster detection in data mining
  • Application of machine learning in cybersecurity
  • Conclusion
4

Machine Learning for Data Structuring

  • Introduction
  • Data structuring
  • The future of big data
  • Structured and unstructured data
  • Conclusion
5

Parsing Data Using NLP

  • Introduction
  • Uses of NLP
  • Key advantages of NLP
  • Data handling in NLP
  • NLP applications
  • Conclusion
6

Computer Vision

  • Introduction
  • Computer vision application
  • Neural networks in computer vision
  • Overview of computer vision
  • Image recognition
  • Biometric recognition
  • Software vulnerabilities
  • Conclusion
7

Neural Network, GBM, and Gradient Descent

  • Introduction
  • Working of neural networks
  • Types of neural networks in AI
  • Benefits of using artificial neural networks
  • Gradient boosting algorithms
  • Conclusion
8

Sequence Modeling

  • Introduction
  • Word embedding
  • Feed-forward neural network algorithm
  • Convolutional neural network algorithm
  • Recurrent neural networks (RNN) algorithm
  • Conditional random field (CRF) algorithm
  • Modeling procedure
  • Conclusion
9

Reinforcement Learning for Financial Markets

  • Introduction
  • Problem types in machine learning
  • Identifying key predictors (data reduction)
  • Learning from experience (reinforcement learning)
  • Reinforcement learning algorithms
  • Types of reinforcement learning
  • Applications of reinforcement learning in real life
  • Conclusion
10

Finance Use Cases

  • Introduction
  • Technology and finance
  • Automation
  • The impact of FinTech
  • Guidelines to live by
  • Innovative technologies
  • Digital bank
  • AI as a strategy at the top level
  • Development status of different AI technologies
  • Risk management
  • Fraud detection and prevention
  • Improving the truth of financial rules and designs
  • Trading
  • AI in banking
  • Conclusion
11

Impact of Machine Learning on FinTech

  • Introduction
  • Overview of FinTech companies
  • Impact of technology
  • Challenges
  • Conclusion
12

Machine Learning in Finance

  • Introduction
  • Machine learning use cases in banking
  • Security
  • Guaranteeing and credit scoring
  • Algorithmic exchanging
  • Robo-advisors
  • Utilize outsider machine learning arrangements
  • Applications of machine learning
  • Current financial applications
  • Machine learning and cryptocurrencies
13

eKYC and Anti-Fraud Policy

  • Introduction
  • Big data analytics: True Buzzword of today
  • How criminals obtain information for online banking
  • Common ways in which information can be stolen
  • ATMs
  • Security measures
  • Conclusion
14

Uses of Data Mining and Data Visualization

  • Introduction
  • Data visualization
  • Data mining
  • Future health care
  • Education
  • Customer relationship management
  • Criminal investigation
  • Fraud detection
  • Customer segmentation
  • Intrusion detection
  • Lie detection
  • Conclusion
15

Advantages and Disadvantages of Machine Learning

  • Introduction
  • Advantages
  • Disadvantages
  • Conclusion
16

Applications of Machine Learning in Other Industries

  • Introduction
  • General applications of machine learning
  • Conclusion
17

Ethical Considerations in Artificial Intelligence

  • Introduction
  • Loss of jobs
  • Inequality
  • Humanity
  • Disinformation
  • Artificial intelligence and crime
  • Racist robots
  • Artificial intelligence vs. humans
  • Conclusion
18

Artificial Intelligence in Banking

  • Introduction
  • Fraud detection
  • Cost cutting
  • Customer service
  • Risk management
  • Internet banking
  • Conclusion
19

Common Machine Learning Algorithms

  • Introduction
  • Regression
  • k-means clustering
  • KNN algorithm
  • Principal component analysis (PCA) algorithm
  • Polynomial fitting and least squares algorithm
  • Forced linear regression algorithm
  • Support vector machine (SVM) algorithm
  • Conditional random fields (CRFs) algorithm
  • Decision tree algorithm
  • Conclusion
20

Frequently Asked Questions

  • Conclusion
  • Approaching a machine learning problem
  • Humans in the loop
  • Testing production systems
  • Next step
  • Machine learning packages
  • Where do we go from here?

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AI in finance can be utilized to improve efficiency & complete tasks within a limited time frame - 

  • Fraud detection
  • Risk management
  • Algorithmic trading
  • Customer service (via chatbots)
  • Regulatory compliance
  • Enhanced decision-making

Artificial intelligence cannot replace professionals from a finance background however it can be utilized by them to make things quicker & easier.

To learn AI in finance, analyze the concepts covered in the Machine Learning for Finance course. Get the course and start learning with practical assessments & interactive lessons. 

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