## MACHINE LEARNING WITH PYTHON

Machine learning is about extracting knowledge from data. It is a research field at the intersection of statistics, artificial intelligence, and computer science and is also known as predictive analytics or statistical learning. Python has become the lingua franca for many data science applications. It combines the power of general purpose programming languages with the ease of use of domain-specific scripting languages.

Python has libraries for data loading, visualization, statistics, natural language processing, image processing, and more. This vast toolbox provides data scientists with a large array of general- and special-purpose functionality. Machine learning and data analysis are fundamentally iterative processes, in which the data drives the analysis. It is essential for these processes to have tools that allow quick iteration and easy interaction. As a general-purpose programming language, Python allows for exactly that including the creation of complex graphical user interfaces (GUIs) and web services, and for integration into existing systems.

## What we will learn ?

• Fundamental concepts of machine learning

• Introduction to popular ML Algorithms

• Data used for training a machine & how we represent data that is processed by machine learning

## Content of our program

- Basic Python operations & functions Revisiting Python Datatypes
- Setting up machine learning python
- Installing essential machine learning libraries
- Concept of Numerical Computations using Python
- Introducing numpy
- Pandas library
- Scipy Library
- Classification & Regression
- Sentiment Analysis & Prediction using ML

## Machine Learning using Python

**Prerequisite:**

• Logical thinking concepts

• Basic concepts of Mathematics and Statistics

## Module 1

### INTRODUCTION TO PYTHON

• Introduction to Python language

• Difference between compiled and interpreted language

• Advantages of python over other languages

• Environmental setup for Python, Anaconda, Jupyter, Spider etc

• Data types of Python, numbers, string

• Run a python program in jupyter/spider/ipython

• Conditional statements If, elif, in python

## Module 2

### Loops, functions and modules in Python

• Loops in python, for and while loop

• Nested for loop

• Modules and functions in python

• Positional and keyword argument

• Lambda function

• Build-in and User defined modules

• Importing modules

## Module 3

### Different Data Structures in Python

• How to create strings in python

• Different build –in functions in strings

• Indexing and slicing in string

• Lists and its functions

• Index and slice a list

• List comprehension

• Dictionary, Tuple, Set and their function

• Difference between all the data structures

## Module 4

### Object oriented python

• What is an Object oriented programming language

• Advantages of OOP over Procedural language

• Features of OOP

• Create class, object in python

• Concept of inheritance, method overloading

• Method overriding in python

• Exceptions in python

## DATA SCIENCE AND MACHINE LEARNING

## Module 1

### Introduction to AI and Data Science

• Artificial intelligence and its effect in our life

• Different fields of AI

• Advantages and disadvantages of AI

• Use case for AI

• What is Data Science

• Importance of Data in our life

## Module 2

### Introduction to Machine Learning

• What is Machine learning

• The three different types of machine learning

• Supervised, unsupervised, reinforcement learning

• An introduction to the basic terminology and notations

• A roadmap for building machine learning systems

• Uses of Machine learning in real life example

## Module 3

### Introduction Numeric and Data Analysis Modules of Python

• Introduction to Numpy

• Creating 1D and 2D Numpy array

• Slicing and indexing of Numpy Array

• Operations on Numpy array

• Introduction to Pandas

• Series and DataFrame

• Uses of Series and DataFrame

• Operations on series and dataframe

• Read external dataset using Pandas

• Analysis on the dataset using Panads

## Module 4

### Visualization library of Python

• Introduction to Matplotlib and Seaborn

• Difference between the two

• Scatter plot using matplotlib

• Draw histogram, barchart, pie chart to any data

• Create pairplot using seaborn

• Concept of Box plot in seaborn

• Drawing of 3D graphs

## Module 5

### Handling Data – Data Wrangling

• What is Data Wrangling

• Prepare data for use

• Introduction to sklearn module of python

• Find for missing value and impute

• Fillna() and Imputer() of sklearn

• Concepts of categorical variable and its problem

• Solution for categorical problem using dummy variable, LableEncoding

• Feature Scaling and its solution using StandardScalar

## Module 6

### Supervised Machine learning - Regression

• Regression and its uses

• Single and Multiple Linear Regression

• Difference between Simple and Multiple Linear Regression

• Simple linear regression with example

• Multiple linear regression with Boston dataset

• Regression using Decision Tree and Random Forest Algorithm

• Compare between all the algorithm of regression

## Module 7

### Supervised Machine learning - Classification

• Classification and its uses

• Different types of classifiers

• K Nearest Neighbour classifier (KNN)

• Apply KNN on IRIS dataset

• Logistic regression to classify some data points

• Apply Logistic regression on Titanic dataset

• Digit recognition using Logistic regression

## Module 8

### Pattern Recognition and PCA

• How to recognize pattern in machine learning

• Use of MNIST dataset to recognize some hand written digits

• Feature extraction and Feature elimination

• PCA to use Dimensionality reduction

• Problem of time complexity and visualization

• Solution using PCA

• Use PCA to recognize MNIST dataset

## Module 9

### Advance of classification

• Classify dataset using decision tree classifier

• Difference between decision tree and Random forest

• Advantage of random forest over decision tree

• Text based classification using Naïve Bayes algorithm

• Classify email using Naïve Bayes classifier

• CountVectorizer and TFID algorithm to solve text classification problem

## Module 10

### SVM and Kernel trick

• What is support vector machine

• Linear SVM

• Solving non linear problem using SVM

• Kernel and kernel trick to solve non linear problem

• Polynomial and RBF kernel

## Module 11

### Unsupervised learning

• What is unsupervised learning

• Types of unsupervised learning

• Concepts of clustering

• K means clustering to cluster group of datas

• Difference between K means and Hierarchical clustering

## Module 12

### Introduction to Data Science

• Data Science overview

• Data Analytics overview

• Statistical Analysis and Business Application

• Introduction to Natural language processing using Scikit learn

• Uses of Tensor Flow and Open CV in data science problem