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Program Overview

What you'll learn


Python is an interpreted, high-level, general-purpose programming language.


Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.


NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

Jupyter Notebook

Project Jupyter is a nonprofit organization created to "develop open-source software, open-standards, and services for interactive computing across dozens of programming languages"

Scikit learn

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy


Best-in-class content by leading faculty and industry leaders in the form of videos, cases and projects

Introduction to Python
  • What is Python and Brief History
  • Discussion on Python 2 and 3
  • Unique Features of Python
  • Discussion on Various IDE’s
  • Demonstration of Practical Use Cases
  • Python Use cases using Data Analysis
Setting Up and Installations
  • Installing Python
  • Setting up Python Environment for Development
  • Installation of Jupyter Notebook
  • How to Access Our Course Material
  • Write your First Program in Python
Python Object and Data Structures Operations
  • Introduction to Python Objects
  • Number Objects and Operations
  • Variable Assignment and Keywords
  • String Objects and Operations
  • Print Formatting with Strings
Python Statements
  • Introduction to Python Statements
  • IF, IF-Else, IF-ElIF Statements
  • Comparison operators
  • Chained Comparison Operators
  • What are Loops?
  • For Loop
  • While Loop
File and Exception Handling
  • Process Files Using Python
  • Read/Write and Append File Object
  • File Functions
  • File Pointer and operations
  • Introduction to Error Handling
  • Try, Except and Finally
Object Oriented Programming
  • Implement Object Oriented with Python
  • Creating Classes and Objects
  • Creating Class Attributes
  • Creating Methods in a Class
  • Inheritance
  • Polymorphism
Data Analysis with Python
  • Introduction to Data Analysis
  • Why Data Analysis?
  • Data Analysis and Artificial Intelligence Bridge
  • Introduction to Data Analysis libraries
  • Data Analysis Introduction
Data Analysis Using Numpy
  • Introduction to Numpy Arrays
  • Creating and Applying Functions
  • Numpy Indexing and Selection
  • Numpy Operations
  • Exercise and Assignment Challenge
Pandas and Advanced Analysis
  • Panda’s Series
  • Introduction to Data Frames
  • Missing Data
  • Group by
  • Merging, Joining and Concatenating
  • Operations
  • Data Input and Output
Data Visualization with Python
  • Plotting Using Matplot Lib
  • Seaborn Visualization
  • Pandas Built-in Data Visualization
  • Project
Seaborn Visualization
  • Categorial Plot Using Seaborn
  • Distributional Plots Using Seaborn
  • Matrix Plots
  • Grids
  • Seaborn Exercises
Linear Regression with Python
  • Introduction to Regression
  • Exercise on Linear Regression using Scikit
  • Practice Project for Linear regression
Logistic Regression with Python
  • Regression Vs Classification
  • Logistic Regression Using Scikit
  • Learn Library
  • Handling Missing Values
  • Handling Categorial Data
  • Evaluation of Model Using Confusion Matrix
  • Practice Project on Logistic Regression
K- Nearest Neighbors Using Python
  • K- Nearest Neighbors Using Scikit
  • Getting the Correct Number of Clusters
  • Evaluation of Model Using Confusion Matrix and Classification Report
  • Standard Scaling Problem
  • Practice Project on Logistic Regression
Support Vector Machines
  • Linearly Separable Data
  • Non-linearly Separable Data
  • SVM Project with Dataset
Decision Tree and Random Forest with Python
  • Intuition Behind Decision Trees
  • Implementation of Decision Tree
  • Decision Tree and Random Forest for Regression
  • Decision Tree and Random Forest for Classification
  • Evaluation of the Decision Tree and Random Forest using Different Methods
  • Practice Project on Decision Tree and Random Forest
Ensemble Methods
  • XGBoost
  • Cat boost
  • Ada boost
  • Model Evaluation
  • Bias Variance Trade-off
  • Sales Forecasting using Walmart Dataset
  • BigMart Sales Prediction ML Project
  • Music Recommendation System Project
  • Human Activity Recognition using Smartphone Dataset
  • Stock Prices Predictor using TimeSeries
  • Predicting Wine Quality using Wine Quality Dataset
  • MNIST Handwritten Digit Classification
  • Learn to build Recommender Systems with Movielens Dataset
  • Boston Housing Price Prediction ML Project
  • Social Media Sentiment Analysis using Twitter Dataset
  • Iris Flowers Classification ML Project


Executive Program in Python with ML Technology Certified By Microsoft


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Frequently Asked Questions

Python is capable of scripting, but in general sense, it is considered as a general-purpose programming language..
An interpreted language is any programming language which is not in machine level code before runtime. Therefore, Python is an interpreted language.
A namespace is a naming system used to make sure that names are unique to avoid naming conflicts.
Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well.
Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling.
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