ML-FINANCE.AW1
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.
- 20 Interactive Lessons and 152 topics mapped to the official exam objectives
Intermediate Self-paced · 1 year access
20Interactive Lessons
152Topics
01 / Skills you'll get
What you will be able to do
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No credit card required
Decision-making, risk management & mitigating financial threats just got easier with our Machine Learning for Finance Beginners course.
- 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
Course Highlights
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20 Structured Lessons Comprehensive coverage of core course objectives
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
20 Interactive Lessons · 152 topics01 Preface +
02 Introduction 15 topics +
- 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
03 Naive Bayes, Normal Distribution, and Automatic Clustering 6 topics +
- Introduction
- Naive Bayes
- Normal distribution
- Automatic cluster detection in data mining
- Application of machine learning in cybersecurity
- Conclusion
04 Machine Learning for Data Structuring 5 topics +
- Introduction
- Data structuring
- The future of big data
- Structured and unstructured data
- Conclusion
05 Parsing Data Using NLP 6 topics +
- Introduction
- Uses of NLP
- Key advantages of NLP
- Data handling in NLP
- NLP applications
- Conclusion
06 Computer Vision 8 topics +
- Introduction
- Computer vision application
- Neural networks in computer vision
- Overview of computer vision
- Image recognition
- Biometric recognition
- Software vulnerabilities
- Conclusion
07 Neural Network, GBM, and Gradient Descent 6 topics +
- Introduction
- Working of neural networks
- Types of neural networks in AI
- Benefits of using artificial neural networks
- Gradient boosting algorithms
- Conclusion
08 Sequence Modeling 8 topics +
- 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
09 Reinforcement Learning for Financial Markets 8 topics +
- 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 15 topics +
- 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 5 topics +
- Introduction
- Overview of FinTech companies
- Impact of technology
- Challenges
- Conclusion
12 Machine Learning in Finance 10 topics +
- 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 7 topics +
- 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 12 topics +
- 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 4 topics +
- Introduction
- Advantages
- Disadvantages
- Conclusion
16 Applications of Machine Learning in Other Industries 3 topics +
- Introduction
- General applications of machine learning
- Conclusion
17 Ethical Considerations in Artificial Intelligence 9 topics +
- Introduction
- Loss of jobs
- Inequality
- Humanity
- Disinformation
- Artificial intelligence and crime
- Racist robots
- Artificial intelligence vs. humans
- Conclusion
18 Artificial Intelligence in Banking 7 topics +
- Introduction
- Fraud detection
- Cost cutting
- Customer service
- Risk management
- Internet banking
- Conclusion
19 Common Machine Learning Algorithms 11 topics +
- 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 7 topics +
- Conclusion
- Approaching a machine learning problem
- Humans in the loop
- Testing production systems
- Next step
- Machine learning packages
- Where do we go from here?
03 / FAQs
Questions before you start
What type of AI is used in finance?+
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
Can AI replace finance?+
Artificial intelligence cannot replace professionals from a finance background however it can be utilized by them to make things quicker & easier.
How to learn AI in finance?+
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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