 Installation and Working with Python

 Introduction, why python?

 Versions of Python

 SET PATH

 PEP 8 standards

 Coding conventions

 Understanding Python variables  Identifier rules

 Literals

 Keywords

 IDLE and information

 Different ways of execution

 Scripting

 Python Operators

 Understanding python blocks

 Indentation, comments, docstring  Type casting, Unicode etc.

 Mutable and Immutable data types

 Declaring and using Numeric data types: int, float, complex

 Using string data type and string operations

 Defining list and list slicing, its methods

 Use of Tuple data type

 Conditional blocks using if, else and elif
 Nested if, elif ladder
 Simple for loops in python
 For loop using range, string, list and dictionaries

 Use of while loops in python

 Loop manipulation using: pass, continue, break
 Programming using Python conditional and loops block

 Different case studies

 Building blocks of python programs
 Understanding string built-in methods
 List manipulation using built-in methods  Tuple operation
 Set: its methods and manipulation
 Dictionary: its methods and manipulation  functions
 Modules and Packages

 What is OOP
 Class
 Reference variable
 Types of variables
 Types of Methods
 Importing Class
 Constructor
 OOP’s Concepts: Inheritance, Encapsulation, Polymorphism, Abstraction

 File handling in detail: txt, bin, csv

 What is Function?
 Advantages of functions
 Syntax and Writing function

 Calling or Invoking function

 Classification of Functions
 No arguments and No return values
 With arguments and No return values
 With arguments and with return values

 No arguments and with return values
 Recursion
 Python argument type functions:
 Default argument functions
 Required (Positional) arguments function

 Keyword arguments function
 Variable arguments functions
 ‘pass’ keyword in functions
 Lambda functions/Anonymous functions

 map ()
 filter ()
 reduce ()
 Nested functions
 Non local variables, global variables
 Closures
 Generators
 Iterators
 Decorators

 Introduction to files
 Opening file
 File modes
 Reading data from file

 Writing data into file
 Appending data into file

 Line count in File

 CSV module
 Creating CSV file
 Reading from CSV file

 Writing into CSV file

 What is Exception?
 Why exception handling?
 Syntax error v/s Run-time error
 Exception codes – AttributeError, ValueError, IndexError, TypeError...

 Handling exception – try except block
 Try with multiple except
 Handling multiple exceptions with single except block  Finally block
 Try-except-finally
 Try with finally
 Case study of finally block
 Raise keyword
 Custom exceptions / User defined exceptions
 Need to Custom exceptions

 Introduction To DBMS

 Overview of SQL

 Data Manipulation Language

 Data Query Language (DQL)

 Built In Functions

 Set Operators

 Joins

 Sub Queries

NumPy: (Numerical Python)

 Introduction to Numpy

 Datatypes of ndarrays

 Dealing with ndarrays, copies and views

 Arithmetic operations,

 Indexing , Slicing, splitting arrays

 Shape manipulation

 Stacking together different data

Pandas: (Data Analysis)

 DataFrame and Series

 DataFrame operations

 Data Slicing, indexing

 DataFrame functions

 Reading the files- csv, excel

 Boolean filtering

 Storing file in various formats

 Useful DataFrame functions

 Stats using pandas

 Dealing with missing data

 Operations over the data

 Filtering Dataframes

 Descriptive Analysis with pandas

 Handling Missing Values

 Finding unique values and deleting duplicates

 Analysing Outliers

 Creating new categorical features from continuous variable

 Grouby operations

 Grouby statistical Analysis

Matplotlib: ( Data Visualization)

 Introduction to Matplotlib

 Formatting the graph: colors, markers, linestyle, etc

 Customization

 Plotting with list, arrays, pandas

 Line plot, Scatter plot, Pie plot, Bar plot, Histogram etc

Seaborn & Plotly: (Data Visualization)

 Different types of plotting

 Scatter

 Distance

 Histogram

 Pie plot

 KDE plot

 Joint Plot

 Pair plot

 Tableau Introduction

 Tableau Components

 Tableau Extensions

 View Sections

 Dash Board Components

 Data Connection in Tableau Interface

 Graphs/Charts/Bars

 Maps

 Understanding the concept of Artificial Intelligence

 Applications of AI

 Who uses AI?

 AI for Banking & Finance, Manufacturing, Healthcare, Retail and Supply Chain

 Understanding types of data to be dealt with

 AI Landscape - AI v/s ML v/s Deep Learning v/s Data Science

 Data Science based use cases

 Computer Vision Based Use cases

 NLP Based use cases

 AI Project Management & Lifecycle

 Size of Data and its impact in AI/ML Project lifecycle

 The process of Machine Learning

 Supervised & Unsupervised Learning

 Understanding -

 Regression & Classification Problems

 Clustering and Anomaly Detection

 Recommendation System and Dimensionality Reduction

 Time Series Forecasting

 What makes a Machine Learning Expert?

 What to learn to become a Machine Learning Developer?

 Types of Variables – Continuous and Categorical

 Interval, Ratio, Ordinal and Nominal

 Terms – Population, Sample, Descriptive Statistics and Inferential Statistics

 Mean, Median and Mode

 Range, Percentile and IQR

 Variance and Standard Deviation

 Correlation and Covariance

 Business logic with correlation Analysis

 Introduction to Hypothesis testing

 Building a business hypothesis

 One sample t test, independent t test, paired t test

 ANOVA – F test

 Chi Square Test

 Introduction to Machine learning,

 Types of Machine learning

 Concepts of Data Preprocessing,

 Data munging

 Importing the data, functioning over the data

 Arithmetic operations

 Categorical and Continuous data

 Feature Scaling, Selection, Engineering

 Binarization, Normalization, Label encoding

Linear Regression

 Importing data

 Data Selection, operations

 Splitting the data into training and testing

 Call the model, build the model and train

 Find the coefficients and intercept

 Accuracy measurements

 Regression Problem Analysis

 Mathematical modelling of Regression Model  Gradient Descent Algorithm

 Use cases

 L1 & L2 Regularization

Linear Regression – Case Study & Project

 Programming Using python

 Building simple Univariate Linear Regression Model  Multivariate Regression Model

 Apply Data Transformations

 Identify Multicollinearity in Data Treatment on Data  Identify Heteroscedasticity

 Modelling of Data

 Variable Significance Identification

 Model Significance Test

 Bifurcate Data into Training / Testing Data set  Build Model on Training Data Set

 Predict using Testing Data Set

 Validate the Model Performance  MAPE, RMSE and R2 Value

Multi Linear Regression

Importing Datasets

Data preprocessing

Feature scaling

Training and Testing split

Characterizing regression

Find coefficients and intercepts

Logistic Regression

 Assumptions

Reason for the Logit Transform

Logit Transformation

 Hypothesis

Variable and Model Significance

Maximum Likelihood Concept

ROC Curve

Model Specification

Confusion Matrix

Accuracy, Recall, Precision and F1 Score

How to handle overfitting and underfitting

Decision Tree Classification

Forming a Decision Tree

Components & Mathematics of Decision Tree

Regression and Classification Tree

Entropy and Information Gain

Gini Impurity

Decision Tree Evaluation

Feature Importance

Tree Visualization

Hyperparameter Tuning using Grid Search Decision Boundary Analysis

Support Vector Machine

 SVM model

 Importing data, data Selection, operations

 Preprocessing

 Splitting the data into training and testing

 Call the model, build the model and train

 Predictions, Evaluating the algorithm

 Naïve Bayes Classification

 Naïve Bayes working flow

 Probability learning, Applications

 Splitting the data into training and testing

 Stepwise model building

 Predictions & Evaluating the algorithm

KNN Classifier

 Importing data, data Selection, operations

 Pros and cons

 Preprocessing

 Distance metrics

 Splitting the data into training and testing

 Feature scaling

 Call the model, build the model and train

 Predictions, Comparing error rate-k value

KMeans clustering

 Introduction to Unsupervised learning model

 K-means clustering in detail

 Importing the dataset

 Finding the clusters

 Visualize the clusters

 Applying the transformation

 Building the model

 Visualize complete clustered model.

Random Forest Regression

 Concept of Random Forest

 Random Forest Mathematics

 Ensemble learning

 How Random Forest works?

 Advantages and Disadvantages

 Preparing data

 Training the regressor

 Evaluating regressor

 Practical Examples & Case Study

 Examples & use cases using Random Forests

Gradient Boosting Methods

 Understanding Adaboost

 Conceptual understanding and mathematical idea behind adaboost

 Implementing Adaboost with python

 Gradient Boosting trees – understanding logical idea

 Gradient boosting – how gradient descent is used

 Implementing GBM

 Challenges with Gradient boosting

 XGBoost

 Implementing XGBoost with pyhon

 LightGBM

 CatBoost

 Applications of Dimensionality Reduction

 Covariance and Eigen Vectors

 Matrix Decomopsition

 Dimensionality Reduction, Data Compression

 Curse of dimensionality

 Multicollinearity & Factor Analysis

 Concept and Mathematical modelling

 Programming with python

 Streamlit Framework (Python framework for data visualization)

 10+ Projects throughout the course

 Introduction to Agile Methodology

 Support in Technical portfolio building

 Technical round preparation

 Mock Interview

 Resume building assistance

 Daily 1 hr (min)