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In this course, you will become familiar with a variety of up-to-date financial analysis content, as well as algorithms techniques of machine learning in the Python environment, where you can perform highly specialized financial analysis. You will get acquainted with technical and fundamental analysis and you will use different tools for your analysis. You will get acquainted with technical and fundamental analysis and you will use different tools for your analysis. You will learn the Python environment completely. You will also learn deep learning algorithms and artificial neural networks that can greatly enhance your financial analysis skills and expertise.This tutorial begins by exploring various ways of downloading financial data and preparing it for modeling. We check the basic statistical properties of asset prices and returns, and investigate the existence of so-called stylized facts. We then calculate popular indicators used in technical analysis (such as Bollinger Bands, Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI)) and backtest automatic trading strategies built on their basis.The next section introduces time series analysis and explores popular models such as exponential smoothing, AutoRegressive Integrated Moving Average (ARIMA), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (including multivariate specifications). We also introduce you to factor models, including the famous Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model. We end this section by demonstrating different ways to optimize asset allocation, and we use Monte Carlo simulations for tasks such as calculating the price of American options or estimating the Value at Risk (VaR).In the last part of the course, we carry out an entire data science project in the financial domain. We approach credit card fraud/default problems using advanced classifiers such as random forest, XGBoost, LightGBM, stacked models, and many more. We also tune the hyperparameters of the models (including Bayesian optimization) and handle class imbalance. We conclude the book by demonstrating how deep learning (using PyTorch) can solve numerous financial problems.
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This comprehensive free online course on Udemy covers everything you need to become proficient in Python-based financial analysis and machine learning. Whether you're a beginner or looking to advance your skills, this free Udemy course with certificate provides hands-on training and practical knowledge you can apply immediately.
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Learn from S. Emadedin Hashemi, an experienced industry veteran with a proven track record in financial technology and data science education. With thousands of satisfied students already trained, this instructor brings real-world expertise and practical insights to every lesson. Their teaching style combines technical depth with clear explanations, making complex financial concepts accessible to learners at all levels.
With an impressive 4.47 rating and 13,785 students already enrolled, this Udemy free course has proven its value. The course includes 20 comprehensive lessons and X hours of video tutorials, all taught in English. What sets this free online course apart is its complete end-to-end approach to financial analysis, combining traditional methods with cutting-edge machine learning techniques. Upon completion, you'll receive a certificate to showcase on LinkedIn and your resume. Plus, with mobile access, you can learn anytime, anywhere—perfect for busy professionals. This Development course in the Data Science niche is regularly updated and includes lifetime access, meaning you can revisit materials whenever you need a refresher.
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Absolutely! Upon completing all course requirements, you'll receive an official Udemy certificate of completion. This certificate can be downloaded, shared on LinkedIn, and added to your resume to showcase your new skills to employers.
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Upon completion of all video lectures, Udemy will issue a certificate of completion.
Once you enroll with the coupon, you get full lifetime access to the materials.


