Investing & AI Fundamentals
Published 9/2026
Created by Flavio Alfano
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 33 Lectures ( 6h 37m ) | Size: 2.6 GB
Learn Investing, Machine Learning, AI Signals, Backtesting and AI Agents Foundations
What you’ll learn
⚡ Understand the fundamentals of investing
⚡ Analyze investments using fundamental and technical concepts
⚡ Understand how Artificial Intelligence and Machine Learning can be applied to financial markets
⚡ Understand and build a basic AI Agent workflow for investment analysis
Requirements
❗ No previous investing or financial market experience is required. No previous Artificial Intelligence or Machine Learning knowledge is required. Basic computer skills are sufficient; programming experience is helpful but not required.
Description
This course contains the use of artificial intelligence.
Investing & AI Fundamentals is a practical introduction to the intersection of investing, financial analysis, and Artificial Intelligence.
The course is designed for learners who want to understand not only the foundations of investing, but also how modern AI technologies can support financial analysis, research, and more structured decision-making.
This course contains the use of artificial intelligence for image generation and voice generation to allow a clear lesson understanding and flow. The Audience can increase or decrease video velocity in order to adapt at their own needs.
We begin with the fundamentals: how investing works, how financial markets operate, and how instruments such asstocks, bonds, and ETFs differ. You will then learn how to approach investment analysis using fundamental and technical perspectives, while developing a solid understanding ofrisk, diversification, correlation, volatility, drawdowns, and portfolio decisions.
From there, the course moves into Artificial Intelligence.
You will learn the differences betweenAI, Machine Learning, and Generative AI, and understand how Machine Learning learns from financial data. We explore features and targets, classification and regression, predictions and probabilities, training and testing, overfitting, and the limitations that can cause AI models to fail.
The course then connects these concepts to practical investment applications. You will learn how technical indicators and Machine Learning outputs can becomeinvestment signals, how those signals can be evaluated throughbacktesting, and why issues such as data leakage, overfitting, execution timing, transaction costs, and false confidence must be considered when evaluating historical results.
Finally, we move beyond individual models and introduceAI agents for investment analysis.
You will learn how agents combine goals, context, tools, memory, external data, rules, Machine Learning, and Generative AI into a coordinated analytical system. In the final lessons, we design an investment research agent and useClaude as the reasoning layer in a practical agent architecture-while maintaining validation, traceability, controls, and human review.
By the end of this course, you will understand how to
✨ Explain the foundations of investing and financial markets.
✨ Differentiate between stocks, bonds, ETFs, and their roles in a portfolio.
✨ Apply fundamental and technical concepts to investment analysis.
✨ Understand volatility, drawdowns, diversification, correlation, and position sizing.
✨ Distinguish AI, Machine Learning, and Generative AI.
✨ Understand how financial data becomes features, targets, predictions, and probabilities.
✨ Recognize overfitting, data leakage, and other common AI and backtesting pitfalls.
✨ Understand how AI-based investment signals can be created and evaluated.
✨ Build the conceptual pipeline behind a Machine Learning investment signal using Python.
✨ Understand the architecture of an AI agent.
✨ Combine rules, Machine Learning, and Generative AI in a hybrid analytical workflow.
✨ Understand how Claude can operate as the reasoning layer within an investment research agent.
✨ Recognize whyhuman judgment, validation, and risk controls remain essential when using AI for investment analysis.
No advanced background in finance, Machine Learning, or AI is required. The course progressively develops each concept and emphasizesunderstanding the logic behind the technology rather than treating AI as a black box.
This course is especially suitable for professionals, investors, technology enthusiasts, students, and anyone interested in understanding howfinancial knowledge and modern AI capabilities can work together.
Financial knowledge x AI capabilities x better-informed decisions.This course is for educational purposes only and does not provide financial or investment advice.
Who this course is for
⭐ Beginners who want to understand investing from the ground up, including stocks, bonds, ETFs, risk, return, diversification, and the basic principles of financial markets. Investors and finance enthusiasts who want to understand how Artificial Intelligence and Machine Learning can be applied to market analysis, financial data, investment signals, and decision support. Technology, business, and finance professionals interested in connecting traditional investment concepts with modern AI tools, without requiring advanced knowledge of finance, programming, or data science. Learners interested in AI Agents and practical applications of Generative AI in finance, including how agents can combine market data, technical indicators, fundamental information, risk analysis, and Machine Learning signals to support investment research.
Homepage
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