The ‘Python and Machine Learning for Financial Analysis Course’ is taught by Dr. Ryan Ahmed. The course focuses on the application of Python programming and Machine Learning techniques in order to solve real-world problems related to finance. The course follows a hands-on approach where students will apply theoretical knowledge to solve real-world financial problems and case studies.
The program covers topics including fundamentals of Python programming, Data Science, Data Analysis, and Python libraries like Pandas, Numpy, Matplotlib, etc. The course is usually available for INR 2,799 on Udemy but students can click on the link and get the ‘Python and Machine Learning for Financial Analysis Course’ for INR 449.
Who all can opt for this course?
- Financial analysts aiming to use data science and artificial intelligence to improve corporate operations, increase revenue, and cut costs.
- Beginner Python programmers and data scientists who are interested in learning the basics of Python and Data Science and their applications in the Finance/Banking industries.
- Investment bankers and financial analysts wishing to develop their careers, build their data science portfolio, and receive real-world practical experience.
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 23 hours |
| Rating | 4.6/5 |
| Student Enrollment | 97,114 students |
| Instructor | Dr. Ryan Ahmed – MBA + PhD https://www.linkedin.com/in/dr.ryanahmed,ph.d.,mba |
| Topics Covered | Fundamentals of Python programming, Data Science, Data Analysis, Pandas, Numpy, and Matplotlib |
| Course Level | Beginner |
| Total Student Reviews | 4,093 |
Learning Outcomes
- Study the fundamentals of Python 3 programming with a focus on finance for data science and machine learning.
- Learn how to use Python’s power to apply important financial concepts like computing daily portfolio returns, risk, and Sharpe ratio.
- Recognize the underlying logic and theory of the capital asset pricing model (CAPM).
- Learn how to use important Python libraries, such as NumPy for scientific computing, Pandas for data analysis, and Matplotlib/Seaborn for data plotting/visualization, in Jupyter Notebooks for creating, presenting, and sharing data science projects.
- Learn how to use real-world datasets to develop, train, and fine-tune machine learning models using the SciKit-Learn library.
- Use machine and deep learning models to find solutions to current issues in the banking and finance industries.
- Recognize the concepts and principles underlying a number of machine learning methods for grouping, classification, and regression.
- Use a variety of KPIs (key performance indicators) to evaluate the performance of trained machine learning regression models.
- Use different KPIs, such as accuracy, precision, recall, and F1-score, to evaluate the performance of taught machine learning classifiers.
- Recognize the underlying theory of long-short-term memory networks, recurrent neural networks, and artificial neural networks (ANNs).
- Train Artificial Neural Networks (ANNs) using back-propagation and gradient descent algorithms.
- To improve network performance, optimise ANN hyperparameters like the number of hidden layers and neurons.
- Learn feature engineering and data cleansing techniques for applications in machine learning and data science.
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Course Introduction, Success Tips and Key Learning Outcomes (58 minutes) | Welcome Message |
| Introduction, Success Tips & Best Practices and Key Learning Outcomes | ||
| Course Outline and Key Learning Outcomes | ||
| Environment Setup & Course Materials Download | ||
| Google Colab Walkthrough | ||
| Python for Data Science Learning Path | ||
| Study Tips For Success | ||
| 2. | **********PART #1: PYTHON PROGRAMMING FUNDAMENTALS*********** (01 minutes) | Introduction to Part #1: Python Programming Fundamentals |
| 3. | Python 101: Variables Assignment, Math Operation, Precedence and Print/Get (01 hour 11 minutes) | Colab Notebooks – Variables Assignment, Math Ops, Precedence, and Print/Get |
| Variable assignment | ||
| Math operations | ||
| Precedence | ||
| Print operation | ||
| Get User Input | ||
| 4. | Python 101: Data Types (01 hour 08 minutes) | Colab Notebooks – Data Types |
| Booleans | ||
| List | ||
| Dictionaries | ||
| Strings | ||
| Tuples | ||
| Sets | ||
| 5. | Python 101: Comparison Operators, Logical Operators, and Conditional Statements (52 minutes) | Colab Notebooks – Comparison Operators, Logical Operators and If Statements |
| Comparison operators | ||
| Logical operators | ||
| Conditional statements – Part #1 | ||
| Conditional statements – Part #2 | ||
| 6. | Python 101: Loops (01 hour 22 minutes) | Colab Notebooks – For/While Loops, Range, List Comprehension |
| For loops | ||
| Range | ||
| While Loops | ||
| Break a loop | ||
| Nested loops | ||
| List comprehension | ||
| 7. | Python 101: Functions (49 minutes) | Colab Notebooks – Functions |
| Functions: built-in functions | ||
| Custom functions | ||
| Lambda expression | ||
| Map | ||
| Filter | ||
| 8. | Python 101: Files Operations (34 minutes) | Colab Notebooks – Files Operations |
| Reading & Writing Text Files | ||
| Reading & Writing CSV Files | ||
| 9. | Python 101: Data Science Python Libraries for Data Analysis (Numpy) (01 hour 04 minutes) | Colab Notebooks – Numpy |
| Numpy basics | ||
| Built-in methods | ||
| Shape Length Type | ||
| Math operations | ||
| Slicing & indexing | ||
| Elements Selection | ||
| 10. | Python 101: Data Science Python Libraries for Data Analysis (Pandas) (01 hour 17 minutes) | Colab Notebooks – Pandas |
| Pandas: Introduction to Pandas and DataFrames | ||
| Reading HTML data, and applying functions, and sorting | ||
| DataFrame operations | ||
| Pandas with functions | ||
| Ordering and Sorting | ||
| Merging/joining/concatenation | ||
| 11. | Python 101: Data Visualization with Matplotlib (01 hour 10 minutes) | Colab Notebooks – Data Visualization with Matplotlib |
| Line Plot | ||
| Scatterplot | ||
| Pie Chart | ||
| Histograms | ||
| Multiple Plots | ||
| Subplots | ||
| 3D Plots | ||
| BoxPlot | ||
| 12. | Python 101: Data Visualization with Seaborn (36 minutes) | Colab Notebooks – Data Visualization with Seaborn |
| Data Visualization with Seaborn – Part #1 | ||
| Data Visualization with Seaborn – Part #2 | ||
| 13. | ********* PART #2: PYTHON FOR FINANCIAL ANALYSIS********* (55 seconds) | Introduction to Part #2: Python for Financial Analysis |
| 14. | Stocks Data Analysis and Visualization in Python (01 hour 32 minutes) | Colab Notebooks – Stocks Data Analysis and Visualization in Python |
| Task 1 | ||
| Task 2 | ||
| Task 3 | ||
| Task 4 | ||
| Task 5 | ||
| Task 6 | ||
| Task 7 | ||
| Task 8 | ||
| 15. | Asset Allocation and Statistical Data Analysis (01 hour 45 minutes) | Colab Notebooks – Asset Allocation and Statistical Data Analysis |
| Task 1 | ||
| Task 2 | ||
| Task 3 | ||
| Task 4 | ||
| Task 5 | ||
| Task 6 | ||
| Task 7 | ||
| Task 8 | ||
| 16. | Capital Asset Pricing Model (CAPM) (01 hour 17 minutes) | Colab Notebooks – Capital Asset Pricing Model (CAPM) |
| Task 1 | ||
| Task 2 | ||
| Task 3 | ||
| Task 4 | ||
| Task 5 | ||
| Task 6 | ||
| Task 7 | ||
| 17. | ******* PART #3: MACHINE AND DEEP LEARNING IN FINANCE ********* (01 minutes) | Introduction to Part #3: Machine and Deep Learning in Finance |
| 18. | Predict Stocks Future Prices Using Machine and Deep Learning (03 hours 05 minutes) | Colab Notebooks – Predict Future Stock Prices Using Machine/Deep Learning |
| Task 1 | ||
| Task 2 | ||
| Task 3 | ||
| Task 4 | ||
| Task 5 | ||
| Task 6 | ||
| Task 7 | ||
| Task 8 | ||
| Task 9 | ||
| Task 10 | ||
| Task 11 | ||
| Task 12 | ||
| 19. | Perform Bank Market Segmentation Using Unsupervised Machine Learning Techniques (02 hours 02 minutes) | Colab Notebooks – Perform Bank Customers Segmentation |
| Problem statement and business case | ||
| Import libraries and datasets | ||
| Visualize data | ||
| Understand K-means algorithm | ||
| Obtain optimal K | ||
| Apply K-means clustering | ||
| Principal component analysis | ||
| Intuition of autoencoders | ||
| Train autoencoder | ||
| Apply autoencoder | ||
| 20. | Perform Sentiment Analysis On Stocks Data Using Natural Language Processing (02 hours 07 minutes) | Colab Notebooks – Perform Sentiment Analysis on Stocks Data |
| Task 1 | ||
| Task 2 | ||
| Task 3 | ||
| Task 4 | ||
| Task 5 | ||
| Task 6 | ||
| Task 7 | ||
| Task 8 | ||
| Task 9 | ||
| Task 10 |
Resources Required
No prior experience is required.
Featured Review
Dumitru Constantin (5/5): Very good, it takes you through the basics and with growing complexity! Attractive and fulfilling its promises!
Pros
- Kehinde Temitope (5/5): This tutor is the best teacher I have ever seen on Udemy.
- Yashar Soleimani (5/5): The instructor is literally one of the best teachers i’ve seen and he teaches everything with a lot of details
- Abhay V Deshpande (5/5): Very happy to be a part of Udemy and Learning the Machine Learning course.
- Karim Mohamed (5/5): I did not try it still but i feel it is one of the best courses i have ever seen
Cons
- Ola Kenji Forslund (1/5): *Does something complicated* “I know it looks complicated but don’t worry guys, it is not.” – Lecturer *Moves on with the lecture without explaining*
- Lim Shan Zhi (1/5): **To all future students/current students of this course!!!** LSTM section is really awful, teaching how to predict stock prices with leaked data?
- Harish Patil (1/5): He has trouble with building an LSTM and used a sequence length of 1, so the results of his stock prediction are useless.
- Daniel Baptista (1/5): I don’t feel this course is worth taking if you are serious about finance or even if you are a beginner because it’s going to teach you bad habits and concepts that are just wrong.
About the Author
Dr. Ryan Ahmed is the instructor of this course. He holds an MBA and PhD degree. He is a professor and one of the best-selling instructors on Udemy with an average of 4.5 instructor ratings, 32,891 reviews and more than 3.2 lakh students. He offers a total of 46 courses on Udemy.
- Ryan holds a PhD degree in Mechanical Engineering from McMaster University. He also pursued MBA in Finance from the DeGroote School of Business.
- He has years of experience in the field of Finance and Technology. He has previously worked at top companies like Samsung America and FCA Canada.
- He has taught over 46 courses on Science, Technology, Engineering and Mathematics to over 3.2 lakh students globally.
- Ryan has his own YouTube Channel called “Professor Ryan” with over 1M views & 22,000+ subscribers. There he teaches people about Artificial Intelligence, Machine Learning, and Data Science.
Comparison Table
| Parameters | Python & Machine Learning for Financial Analysis | Mathematical Foundations of Machine Learning | Software development in Python: A practical approach |
|---|---|---|---|
| Offers | INR 449 ( | INR 455 ( | INR 455 ( |
| Duration | 23 hours | 16.5 hours | 10.5 hours |
| Rating | 4.6/5 | 4.6 /5 | 4.1 /5 |
| Student Enrollments | 97,106 | 105,063 | 85,293 |
| Instructors | Dr. Ryan Ahmed, Ph.D., MBA | Dr Jon Krohn | Daniel IT |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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