Last updated on May 4, 2021
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 2020, 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: Beginner’s 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.
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