Machine Learning with Python 

Module 1 – Introduction To Data Science And Data Science Libraries
Data science is the field of applying advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning and other uses. Through this module, you will learn the basics, how to analyze data, and then create some beautiful visualizations using Python.
 

• Numpy
It’s a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. NumPy also addresses the slowness problem partly by providing these multidimensional arrays as well as providing functions and operators that operate efficiently on these arrays.
 

• Pandas
Pandas is an important library in Python for Data Science. It is used for data manipulation and analysis. It is well suited for different data such as tabular, ordered and unordered time series, matrix data, etc.
 

• Matplotlib
Matplotlib is a powerful library for visualization in Python. It can be used in Python scripts, shell, web application servers, and other GUI toolkits. Because of the graphs and plots that it produces, it’s extensively used for data visualization.
 

• Seaborn
Seaborn is a statistical plotting library in Python. So, whenever you’re using Python for Data Science, you will be using matplotlib (for 2D visualizations) and Seaborn, which has its beautiful default styles and a high-level interface to draw statistical graphics.
 
• Scikit-learn
Next in the list of the top python libraries for data science comes Scikit-learn, a machine learning library that provides almost all the machine learning algorithms you might need. Scikit-learn is designed to be interpolated into NumPy and SciPy.
 

• Exploratory Data Analysis
Exploratory data analysis (EDA) is an especially important activity in the routine of a data analyst or scientist. It enables an in depth understanding of the dataset, define or discard hypotheses and create predictive models on a solid basis. It uses data manipulation techniques and several statistical tools to describe and understand the relationship between variables and how these can impact business.
 

Exploratory Data Analysis (EDA) – Credit Card Fraud Detection Case Study
Overview : Lots of financial losses are caused every year due to credit card fraud transactions, the financial industry has switched from a posterior investigation approach to an a priori predictive approach with the design of fraud detection algorithms to warn and help fraud investigators.
This case study is focused to give you an idea of applying Exploratory Data Analysis (EDA) in a real business scenario. In this case study, apart from applying the various Exploratory Data Analysis (EDA) techniques, you will also develop a basic understanding of risk analytics and understand how data can be utilized in order to minimize the risk of losing money while lending to customers.
 
Module 2 – Introduction To Machine Learning
This machine learning module gives you an introduction to machine learning along with the wide range of machine learning techniques such as Supervised, Unsupervised, and Reinforcement learning. You will learn about regression and classification models, clustering methods, train- validation-test splitting, and various sequential models. This module is a stepping stone to your Machine Learning journey.
 

• Introduction To Machine Learning and Categories
Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed. At a broad level, machine learning can be classified into three types - Supervised learning, Unsupervised learning and Reinforcement learning.
 

• Your First Machine Learning Model
You will use the scikit-learn library to create your models. When coding, this library is written as sklearn, as you will see in the sample code. Scikit-learn is easily the most popular library for modeling the types of data typically stored in DataFrames.
 

Case Study : How Experience Affects Salary
Overview : We will learn how to build a simple linear regression model on given dataset, we will be using salary dataset. Our dataset will have 2 columns namely – Years of Experience and Salary. Main objective here is to learn steps to building and using a model. We will be predicting salaries based on experience. We will learn Splitting the dataset and fitting linear regression model into the training set.
 
Module 3 – Credit Card Fraud Detection with Python & Machine Learning
For any bank or financial organization, credit card fraud detection is of utmost importance. We have to spot potential fraud so that consumers cannot bill for goods that they haven’t purchased. The aim is, therefore, to create a classifier that indicates whether a requested transaction is a fraud. In this machine learning project, we solve the problem of detecting credit card fraud transactions using machine numpy, scikit learn, and few other python libraries. We overcome the problem by creating a binary classifier and experimenting with various machine learning techniques to see which fits better.
 

• Perform Exploratory Data Analysis (EDA) on our dataset
Here we will use data manipulation techniques and several statistical tools to describe and
understand the relationship between variables and how these can impact business.
 

• Apply different Machine Learning algorithms to our dataset
Let's train different models on our dataset and observe which algorithm works better for our problem. This is actually a binary classification problem as we have to predict only 1 of the 2 class labels. We can apply a variety of algorithms for this problem like Random Forest, Decision Tree, Support Vector Machine algorithms, etc.
In this machine learning project, we build Random Forest and Decision Tree classifiers and see which one works best.
 

• Train and Evaluate our models on the dataset and pick the best one
Now, Let’s train and evaluate the newly created models on the dataset and pick the best one. Train the decision tree and random forest models on the dataset using the fit() function. Record the predictions made by the models using the predict() function and evaluate.
 

• Class Imbalance Issue
It is the problem in machine learning where the total number of a class of data (positive) is far less than the total number of another class of data (negative). This problem is extremely common in practice and can be observed in various disciplines including fraud detection, anomaly detection, medical diagnosis, oil spillage detection, facial recognition, etc.
 
Module 4 – Hyperparameters and Model Validation
Cross-validation is a statistical method used to estimate the performance of machine learning models. It is a method for assessing how the results of a statistical analysis will generalize to an independent data set.
 

• Hold Out Cross-validation
• Leave One Out Cross Validation (LOOCV)
• K-Fold Cross-Validation
• Monte Carlo Cross-Validation(Shuffle Split)
• Hyperparameter Tuning Machine Learning
Choosing the correct hyperparameters for machine learning or deep learning models is one of the best ways to extract the last juice out of your models.
 

Module 5 – Credit Risk Modelling in Python
Credit risk is the risk of a borrower not repaying a loan, credit card or any other type of credit facility. Credit risk is an important topic in the field of finance because banks and other financial institutions heavily invest in reducing their credit risk. The main reason behind the global financial crisis in 2008 was that mortgage loans were given to customers with poor credit scores. Poor credit score indicates that a customer has a higher probability of defaulting a loan.
Machine Learning models have helped these companies in improving the accuracy of their credit risk analysis by giving a scientific technique for predicting potential debtors.
The objective of this article is to build a model to predict probability of person defaulting a loan. The following steps will be followed in building the model.
• Data Preprocessing and data preparation
• Features Engineering and Features selection
• Model Development and Model Evaluations 
• Building Model
Logistic Regression Random Forest Classifier XGBoost Classifier
Support vector machine classifier
• Evaluating the model
Hyperparameter tunning