Python & Machine Learning for Financial Analysis (Udemy.com)

Master Python Programming Fundamentals and Harness the Power of ML to Solve Real-World Practical Applications in Finance

( 303 Reviews )

Created by: Dr. Ryan Ahmed, Ph.D., MBA

Produced in 2021

What you will learn

Master Python 3 programming fundamentals for Data Science and Machine Learning with focus on Finance.
Understand how to leverage the power of Python to apply key financial concepts such as calculating daily portfolio returns, risk and Sharpe ratio.
Understand the theory and intuition behind Capital Asset Pricing Model (CAPM), Markowitz portfolio optimization, and efficient frontier.
Apply Python to implement several trading strategies such as momentum-based and moving average trading strategies.
Understand how to use Jupyter Notebooks for developing, presenting and sharing Data Science projects.
Learn how to use key Python Libraries such as NumPy for scientific computing, Pandas for Data Analysis, Matplotlib for data plotting/visualization, and Seaborn for statistical plots.
Master SciKit-Learn library to build, train and tune machine learning models using real-world datasets.
Apply machine and deep learning models to sol

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 92 / 100

Course Description

Are you ready to learn python programming fundamentals and directly apply them to solve real world applications in Finance and Banking?
If the answer is yes, then welcome to the The Complete Python and Machine Learning for Financial Analysis course in which you will learn everything you need to develop practical real-world finance/banking applications in Python!
So why Python?
Python is ranked as the number one programming language to learn in 2021, here are 6 reasons you need to learn Python right now!
1. #1 language for AI & Machine Learning: Python is the #1 programming language for machine learning and artificial intelligence.
2. Easy to learn: Python is one of the easiest programming language to learn especially of you have not done any coding in the past.
3. Jobs: high demand and low supply of python developers make it the ideal programming language to learn now.
4. High salary: Average salary of Python programmers in the US is around $116 thousand dollars a year.
5. Scalability: Python is extremely powerful and scalable and therefore real-world apps such as Google, Instagram, YouTube, and Spotify are all built on Python.
6. Versatility: Python is the most versatile programming language in the world, you can use it for data science, financial analysis, machine learning, computer vision, data analysis and visualization, web development, gaming and robotics applications.
This course is unique in many ways:
1. The course is divided into 3 main parts covering python programming fundamentals, financial analysis in Python and AI/ML application in Finance/Banking Industry. A detailed overview is shown below:
a) Part #1 Python Programming Fundamentals: Beginners Python programming fundamentals covering concepts such as: data types, variables assignments, loops, conditional statements, functions, and Files operations. In addition, this section will cover key Python libraries for data science such as Numpy and Pandas. Furthermore, this section covers data visualization tools such as Matplotlib, Seaborn, Plotly, and Bokeh.
b) Part #2 Financial Analysis in Python: This part covers Python for financial analysis. We will cover key financial concepts such as calculating daily portfolio returns, risk and Sharpe ratio. In addition, we will cover Capital Asset Pricing Model (CAPM), Markowitz portfolio optimization, and efficient frontier. We will also cover trading strategies such as momentum-based and moving average trading.
c) Part #3 AI/Ml in Finance/Banking: This section covers practical projects on AI/ML applications in Finance. We will cover application of Deep Neural Networks such as Long Short Term Memory (LSTM) networks to perform stock price predictions. In addition, we will cover unsupervised machine learning strategies such as K-Means Clustering and Principal Components Analysis to perform Baking Customer Segmentation or Clustering. Furthermore, we will cover the basics of Natural Language Processing (NLP) and apply it to perform stocks sentiment analysis.
2. There are several mini challenges and exercises throughout the course and you will learn by doing. The course contains mini challenges and coding exercises in almost every video so you will learn in a practical and easy way.
3. The Project-based learning approach: you will build more than 6 full practical projects that you can add to your portfolio of projects to showcase your future employer during job interviews.
So who is this course for?
This course is geared towards the following:
Financial analysts who want to harness the power of Data science and AI to optimize business processes, maximize revenue, reduce costs.
Python programmer beginners and data scientists wanting to gain a fundamental understanding of Python and Data Science applications in Finance/Banking sectors.
Investment bankers and financial analysts wanting to advance their careers, build their data science portfolio, and gain real-world practical experience.
There is no prior experience required, Even if you have never used python or any programming language before, dont worry! You will have a clear video explanation for each of the topics we will be covering. We will start from the basics and gradually build up your knowledge.
In this course, (1) you will have a true practical project-based learning experience, we will build more than 6 projects together (2) You will have access to all the codes and slides, (3) You will get a certificate of completion that you can post on your LinkedIn profile to showcase your skills in python programming to employers. (4) All of this comes with a 30 day money back guarantee so you can give a course a try risk free! Check out the preview videos and the outline to get an idea of the projects we will be covering.
Enroll today and I look forward to seeing you inside!
Who this course is for:
Financial analysts who want to harness the power of Data science and AI to optimize business processes, maximize revenue, reduce costs.
Python programmer beginners and data scientists wanting to gain a fundamental understanding of Python and Data Science applications in Finance/Banking sectors.
Investment bankers and financial analysts wanting to advance their careers, build their data science portfolio, and gain real-world practical experience.
There is no prior experience required, Even if you have never used python or any programming language before, dont worry! You will have a clear video explanation for each of the topics we will be covering. We will start from the basics and gradually build up your knowledge.

Time: 23h

Instructor Details

Dr. Ryan Ahmed, Ph.D., MBA

4.6 Rating
303 Reviews

Ryan Ahmed is abest-selling Udemy instructorwhois passionate about education and technology. Ryan's mission is to make quality education accessible and affordable to everyone.Ryanholds a Ph.D.degree in Mechanical Engineeringfrom McMaster*University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Masters of Applied Sciencedegree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and anMBA in Finance from the DeGroote School of Business.
Ryanheld several engineering positions at Fortune 500 companies globally such as SamsungAmericaand Fiat-Chrysler Automobiles (FCA) Canada.Ryan has taught several courses on Science, Technology, Engineering and Mathematics to over 200,000+ students globally.He has over 15 published journal and conference research papers on state estimation, AI, Machine learning, battery modeling and EVcontrols. Heisthe co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA.
Ryan isaStanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is alsothe programCo-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC17) in Chicago, IL, USA.
* McMaster University is one of only four Canadian universities consistently rankedin the to

Reviews

4.6

303 total reviews

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By Radomir Sevillano on 11/21/2020

Some lectures missing. It is mentioned in the Q&A repeatedly but nothing done.
Apart from that I liked it. Quite understandable.

By Ferdi G. on 11/21/2020

Some topics are repeated again again this makes the course longer than it needs to be. Also the content related to intuition should be improved.

By Eric Shen on 11/21/2020

Maybe a little bit more detail on how to adjust the models to apply to real-world data is helpful.

By Rafi Katten on 11/21/2020

The end of the course was not explained well. One lecture is missing and the colab files do not work well as the pictures did not often download correctly. I asked for help but the instructor did not get back to me.

By Federico on 11/21/2020

El ingls no es mi lengua materna y se entiende bastante bien

By Zintle Twala on 11/21/2020

Very clear and easy to follow

By Ninad Kelkar on 11/21/2020

This is excellent course, where concepts are explained in detailed manner. Course is highly recommended. I would like to congratulate Dr Ryan Ahmed Sir and team for designing this course to match up industry requirements.

By Rakesh on 11/21/2020

Nicely paced course for a beginner.

By Archit Jain on 11/21/2020

It is a good course but the only thing that I would like to suggest is that while explaining the theory of the Neural Network and some mathematics concepts the instructor should have used a board and pen. It would led to better understanding.

By Joel Moncho on 11/14/2020