## Python Machine Learning for Financial Analysis

### What you’ll 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.

### Requirements

No prior experience required.

### 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!

### Overview

Section 1: Course Introduction, Success Tips and Key Learning Outcomes

Lecture 1 Welcome Message

Lecture 2 Introduction, Success Tips & Best Practices and Key Learning Outcomes

Lecture 3 Course Outline and Key Learning Outcomes

Lecture 4 Environment Setup & Course Materials Download

Lecture 5 Google Colab Walkthrough

Lecture 6 Python for Data Science Learning Path

Lecture 7 Study Tips For Success

Section 2: **********PART #1: PYTHON PROGRAMMING FUNDAMENTALS***********

Lecture 8 Introduction to Part #1: Python Programming Fundamentals

Section 3: Python 101: Variables Assignment, Math Operation, Precedence and Print/Get

Lecture 9 Colab Notebooks – Variables Assignment, Math Ops, Precedence, and Print/Get

Lecture 10 Variable assignment

Lecture 11 Math operations

Lecture 12 Precedence

Lecture 13 Print operation

Lecture 14 Get User Input

Section 4: Python 101: Data Types

Lecture 15 Colab Notebooks – Data Types

Lecture 16 Booleans

Lecture 17 List

Lecture 18 Dictionaries

Lecture 19 Strings

Lecture 20 Tuples

Lecture 21 Sets

Section 5: Python 101: Comparison Operators, Logical Operators, and Conditional Statements

Lecture 22 Colab Notebooks – Comparison Operators, Logical Operators and If Statements

Lecture 23 Comparison operators

Lecture 24 Logical operators

Lecture 25 Conditional statements – Part #1

Lecture 26 Conditional statements – Part #2

Section 6: Python 101: Loops

Lecture 27 Colab Notebooks – For/While Loops, Range, List Comprehension

Lecture 28 For loops

Lecture 29 Range

Lecture 30 While Loops

Lecture 31 Break a loop

Lecture 32 Nested loops

Lecture 33 List comprehension

Section 7: Python 101: Functions

Lecture 34 Colab Notebooks – Functions

Lecture 35 Functions: built-in functions

Lecture 36 Custom functions

Lecture 37 Lambda expression

Lecture 38 Map

Lecture 39 Filter

Section 8: Python 101: Files Operations

Lecture 40 Colab Notebooks – Files Operations

Lecture 41 Reading & Writing Text Files

Lecture 42 Reading & Writing CSV Files

Section 9: Python 101: Data Science Python Libraries for Data Analysis (Numpy)

Lecture 43 Colab Notebooks – Numpy

Lecture 44 Numpy basics

Lecture 45 Built-in methods

Lecture 46 Shape Length Type

Lecture 47 Math operations

Lecture 48 Slicing & indexing

Lecture 49 Elements Selection

Section 10: Python 101: Data Science Python Libraries for Data Analysis (Pandas)

Lecture 50 Colab Notebooks – Pandas

Lecture 51 Pandas: Introduction to Pandas and DataFrames

Lecture 52 Reading HTML data, and applying functions, and sorting

Lecture 53 DataFrame operations

Lecture 54 Pandas with functions

Lecture 55 Ordering and Sorting

Lecture 56 Merging/joining/concatenation

Section 11: Python 101: Data Visualization with Matplotlib

Lecture 57 Colab Notebooks – Data Visualization with Matplotlib

Lecture 58 Line Plot

Lecture 59 Scatterplot

Lecture 60 Pie Chart

Lecture 61 Histograms

Lecture 62 Multiple Plots

Lecture 63 Subplots

Lecture 64 3D Plots

Lecture 65 BoxPlot

Section 12: Python 101: Data Visualization with Seaborn

Lecture 66 Colab Notebooks – Data Visualization with Seaborn

Lecture 67 Data Visualization with Seaborn – Part #1

Lecture 68 Data Visualization with Seaborn – Part #2

Section 13: ********* PART #2: PYTHON FOR FINANCIAL ANALYSIS*********

Lecture 69 Introduction to Part #2: Python for Financial Analysis

Section 14: Stocks Data Analysis and Visualization in Python

Lecture 70 Colab Notebooks – Stocks Data Analysis and Visualization in Python

Lecture 71 Task 1

Lecture 72 Task 2

Lecture 73 Task 3

Lecture 74 Task 4

Lecture 75 Task 5

Lecture 76 Task 6

Lecture 77 Task 7

Lecture 78 Task 8

Section 15: Asset Allocation and Statistical Data Analysis

Lecture 79 Colab Notebooks – Asset Allocation and Statistical Data Analysis

Lecture 80 Task 1

Lecture 81 Task 2

Lecture 82 Task 3

Lecture 83 Task 4

Lecture 84 Task 5

Lecture 85 Task 6

Lecture 86 Task 7

Lecture 87 Task 8

Section 16: Capital Asset Pricing Model (CAPM)

Lecture 88 Colab Notebooks – Capital Asset Pricing Model (CAPM)

Lecture 89 Task 1

Lecture 90 Task 2

Lecture 91 Task 3

Lecture 92 Task 4

Lecture 93 Task 5

Lecture 94 Task 6

Lecture 95 Task 7

Section 17: ******* PART #3: MACHINE AND DEEP LEARNING IN FINANCE *********

Lecture 96 Introduction to Part #3: Machine and Deep Learning in Finance

Section 18: Predict Stocks Future Prices Using Machine and Deep Learning

Lecture 97 Colab Notebooks – Predict Future Stock Prices Using Machine/Deep Learning

Lecture 98 Task 1

Lecture 99 Task 2

Lecture 100 Task 3

Lecture 101 Task 4

Lecture 102 Task 5

Lecture 103 Task 6

Lecture 104 Task 7

Lecture 105 Task 8

Lecture 106 Task 9

Lecture 107 Task 10

Lecture 108 Task 11

Lecture 109 Task 12

Section 19: Perform Bank Market Segmentation Using Unsupervised Machine Learning Techniques

Lecture 110 Colab Notebooks – Perform Bank Customers Segmentation

Lecture 111 Problem statement and business case

Lecture 112 Import libraries and datasets

Lecture 113 Visualize data

Lecture 114 Understand K-means algorithm

Lecture 115 Obtain optimal K

Lecture 116 Apply K-means clustering

Lecture 117 Principal component analysis

Lecture 118 Intuition of autoencoders

Lecture 119 Train autoencoder

Lecture 120 Apply autoencoder

Section 20: Perform Sentiment Analysis On Stocks Data Using Natural Language Processing

Lecture 121 Colab Notebooks – Perform Sentiment Analysis on Stocks Data

Lecture 122 Task 1

Lecture 123 Task 2

Lecture 124 Task 3

Lecture 125 Task 4

Lecture 126 Task 5

Lecture 127 Task 6

Lecture 128 Task 7

Lecture 129 Task 8

Lecture 130 Task 9

Lecture 131 Task 10

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.

#### Course Information:

Udemy | English | 23h 2m | 13.68 GB

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

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