Python & Machine Learning for Financial Analysis

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

4.54 (4341 reviews)
Udemy
platform
English
language
Data Science
category
99,457
students
23 hours
content
Mar 2024
last update
$94.99
regular price

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)

Understand how to use Jupyter Notebooks for developing, presenting and sharing Data Science projects.

key Python Libraries such as NumPy for scientific computing, Pandas for Data Analysis, Matplotlib/Seaborn for data plotting/visualization

Master SciKit-Learn library to build, train and tune machine learning models using real-world datasets.

Apply machine and deep learning models to solve real-world problems in the banking and finance sectors

Understand the theory and intuition behind several machine learning algorithms for regression, classification and clustering

Assess the performance of trained machine learning regression models using various KPI (Key Performance indicators)

Assess the performance of trained machine learning classifiers using various KPIs such as accuracy, precision, recall, and F1-score.

Understand the underlying theory, intuition behind Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs) & Long Short Term Memory Networks (LSTM).

Train ANNs using back propagation and gradient descent algorithms.

Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance.

Master feature engineering and data cleaning strategies for machine learning and data science applications.

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 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.


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, don’t 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!

Content

Course Introduction, Success Tips and Key Learning Outcomes

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

**********PART #1: PYTHON PROGRAMMING FUNDAMENTALS***********

Introduction to Part #1: Python Programming Fundamentals

Python 101: Variables Assignment, Math Operation, Precedence and Print/Get

Colab Notebooks - Variables Assignment, Math Ops, Precedence, and Print/Get
Variable assignment
Math operations
Precedence
Print operation
Get User Input

Python 101: Data Types

Colab Notebooks - Data Types
Booleans
List
Dictionaries
Strings
Tuples
Sets

Python 101: Comparison Operators, Logical Operators, and Conditional Statements

Colab Notebooks - Comparison Operators, Logical Operators and If Statements
Comparison operators
Logical operators
Conditional statements - Part #1
Conditional statements - Part #2

Python 101: Loops

Colab Notebooks - For/While Loops, Range, List Comprehension
For loops
Range
While Loops
Break a loop
Nested loops
List comprehension

Python 101: Functions

Colab Notebooks - Functions
Functions: built-in functions
Custom functions
Lambda expression
Map
Filter

Python 101: Files Operations

Colab Notebooks - Files Operations
Reading & Writing Text Files
Reading & Writing CSV Files

Python 101: Data Science Python Libraries for Data Analysis (Numpy)

Colab Notebooks - Numpy
Numpy basics
Built-in methods
Shape Length Type
Math operations
Slicing & indexing
Elements Selection

Python 101: Data Science Python Libraries for Data Analysis (Pandas)

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

Python 101: Data Visualization with Matplotlib

Colab Notebooks - Data Visualization with Matplotlib
Line Plot
Scatterplot
Pie Chart
Histograms
Multiple Plots
Subplots
3D Plots
BoxPlot

Python 101: Data Visualization with Seaborn

Colab Notebooks - Data Visualization with Seaborn
Data Visualization with Seaborn - Part #1
Data Visualization with Seaborn - Part #2

********* PART #2: PYTHON FOR FINANCIAL ANALYSIS*********

Introduction to Part #2: Python for Financial Analysis

Stocks Data Analysis and Visualization in Python

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

Asset Allocation and Statistical Data Analysis

Colab Notebooks - Asset Allocation and Statistical Data Analysis
Task 1
Task 2
Task 3
Task 4
Task 5
Task 6
Task 7
Task 8

Capital Asset Pricing Model (CAPM)

Colab Notebooks - Capital Asset Pricing Model (CAPM)
Task 1
Task 2
Task 3
Task 4
Task 5
Task 6
Task 7

Monte Carlo Simulation, Portfolio Optimization, and Trading with Momentum

Monte Carlo Simulation, Portfolio Optimization, and Trading with Momentum

******* PART #3: MACHINE AND DEEP LEARNING IN FINANCE *********

Introduction to Part #3: Machine and Deep Learning in Finance

Predict Stocks Future Prices Using Machine and Deep Learning

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

Perform Bank Market Segmentation Using Unsupervised Machine Learning Techniques

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

Perform Sentiment Analysis On Stocks Data Using Natural Language Processing

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

Screenshots

Python & Machine Learning for Financial Analysis - Screenshot_01Python & Machine Learning for Financial Analysis - Screenshot_02Python & Machine Learning for Financial Analysis - Screenshot_03Python & Machine Learning for Financial Analysis - Screenshot_04

Reviews

Eyad
October 21, 2023
Until now everything is perfect, but my only concern is the repetition of the instructions, program outline and what we are going to learn
Robert
October 18, 2023
I had very high hopes for this course. But I cannot finish section 18. Many times you switch stocks in the middle of an example, making it almost impossible to follow along with you as you go back to decipher what random values you are using. I quit.
Dave
September 26, 2023
Course material is well explained and the examples and code exercises used even in the early stages of learning python programming are related to financial scenarios. I am really enjoying the course.
Christian
August 14, 2023
Disappointing with the chapter "Predict Future Stock Prices Using Machine/Deep Learning". Predicts the stock price with a lag of one day and the variable volume as an explanatory variable is not included in the model as far as I can see. The goodness and usefulness of the model is not discussed in depth, for example to make predictions. The statement that the model explains well is not substantiated in any way. I would not buy stocks based on the model.
Mojtaba
May 12, 2023
Course was really easy to follow and keep me engaged. The Instructor is easily one of the best on udemy. recommended with 5 stars.
Andriy
May 4, 2023
Lector is telling his story for himself - playing with the code wihtout explaining an actual final road - "let's do this or that" wihtout knowing - WHY are we doing it. Just do it. It's not creating a value. Yes, it's giving some knowledge - but not something I would use starting tomorrow. Need a lot of other training courses for a good and deep understanding. Anyway - thanks.
G
May 4, 2023
A good to go course to do 6 machine learning projects. All the projects are explained in a step by step way. Great job done.
Pawan
January 11, 2023
Not as per my expectations and also the course is missing Monte Carlo simulations, trading with momentum & moving average, and a video in sections 18, 19
Stefano
January 1, 2023
Most of the course is just a normal python course like many others. It discuss only 2 very simple models of machine learning that DO NOT make any prediction of the future. Also, such models are based just on historical prices and volumes, nothing else.
Isacm
December 24, 2022
Fantastic!! One of the very few courses I have managed to complete in Udemy!! Highly Recommend for anyone keen to learn Python and do stock analysis. Ryan Ahmed, this is my first course of yours, the content coverage is great esp theory on the ML/AI intuition sections and your explanation is excellent, thank you!
Vignesh
November 20, 2022
This course is way too basic and does even cover the concept rather just explains the code which is already made.
Cletus
November 18, 2022
After wasting countless hours and several attempts to get help to no avail, I was never able to even get started. The setup does not respond. Now I find that the refund time has expired. My research shows that course originated at least 2 yrs ago, and something has changed in the initial setup that has not been included in the course as offered today.
Manikanta
October 28, 2022
Great Content... In section 19 - 116 - Applying K-Means clustering, It would've been great if you explained it and then coded.. Apart from that Awesome Content.
Joe
October 22, 2022
This course is awesome and super thorough. My only wish is that tasks from previous lessons weren't repeated in each subsequent section. Its get super verbose.
Talha
August 4, 2022
the things are very repetitive in this source. though its a good course for some one who is v beginner in python

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3428726
udemy ID
8/18/2020
course created date
9/25/2020
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