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Python Programming Training


Python is a powerful open-source and flexible language. It is easy to learn and use. It has powerful libraries for data analysis and manipulation. Python Programming has been used in highly quantitative domains including oil & gas, physics, finance and signal processing etc. Python Programming Training provides complete knowledge to the delegates about the fundamentals of Python, Statistics and Machine Learning. It helps the delegates to gain expertise in applied Data Science at scale using Python.

  • Perform Text Mining and Sentimental Analysis

  • Work with real-time data

  • We provide 24 x 7 help and support to our delegates in case of any query


Find out what's included in the training programme.


Tutor Support

A dedicated tutor will be at your disposal throughout the training to guide you through any issues.



Delegates will get certification of completion at the end of the course.


For attending the Python Programming Training, the delegates should have a basic understanding of Computer Programming Languages


The course is designed for below professionals

  • Analytics Managers who are managing team of analysts
  • Programmers, Developers, Technical Leads and Architects
  • Developers who desire to be a Machine Learning Engineer
  • Business Analysts who wish to understand about the Machine Learning (ML) Techniques
  • Information Architects who desire to gain knowledge in Predictive Analytics
  • Python professionals who want to design automatic predictive models


During this course, the delegates will be able to:

  • Understand about the tools and techniques for predictive modelling
  • Learn core Python scripting elements such as variables and flow control structure
  • Learn about the data
  • Explain Time Series and its related concepts

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Python Programming Training provides complete knowledge to the delegates about the different types of sequence structures, related operations and their usage. The delegates will also learn about the different methods of opening, reading, and writing to files. With the help of training, the delegates will able to learn how to create generic Python scripts as well as understand how to address exceptions in code. Throughout the training, the delegates will understand about the Supervised Learning Techniques and implementation of these techniques.


Introduction to Python

  • Overview of Python
  • The Companies using Python
  • Different Applications where Python is used
  • Discuss Python Scripts on UNIX/Windows
  • Values, Types, Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Command Line Arguments
  • Writing to the Screen

Sequences and File Operations

  • Python Files I/O Functions
  • Numbers
  • Strings and Related Operations
  • Tuples and Related Operations
  • Lists and Related Operations
  • Dictionaries and Related Operations
  • Sets and Related Operations

Deep Dive-Functions, OOPs, Modules, Errors and Exceptions

  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values
  • Lambda Functions
  • Object-Oriented Concepts
  • Standard Libraries
  • Modules Used in Python
  • The Import Statements
  • Module Search Path
  • Package Installation Ways
  • Errors and Exception Handling
  • Handling Multiple Exceptions

Introduction to Numpy, Pandas and Matplotlib

  • Numpy - Arrays
  • Operations on Arrays
  • Indexing Slicing and Iterating
  • Reading and Writing Arrays on Files
  • Pandas - Data Structures & Index Operations
  • Reading and Writing Data from Excel/CSV Formats into Pandas
  • Matplotlib Library
  • Grids, Axes, Plots
  • Markers, Colours, Fonts and Styling
  • Types of Plots - Bar Graphs, Pie Charts, Histograms
  • Contour Plots

Data Manipulation

  • Basic Functionalities of a data object
  • Merging of Data objects
  • Concatenation of data objects
  • Types of Joins on data objects
  • Exploring a Dataset
  • Analyzing a dataset

Introduction to Machine learning with Python

  • Python Revision (numpy, Pandas, scikit learn, matplotlib)
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Linear regression
  • Gradient descent

Supervised Learning -1

  • What are Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?

Dimensionality Reduction

  • Introduction to Dimensionality
  • Why Dimensionality Reduction
  • PCA
  • Factor Analysis
  • Scaling dimensional model
  • LDA

Supervised Learning-2

  • What is Naïve Bayes?
  • How Naïve Bayes works?
  • Implementing a Naïve Bayes Classifier
  • What is Support Vector Machine?
  • Illustrate how Support Vector Machine works?
  • Hyperparameter Optimization
  • Grid Search vs Random Search
  • Implementation of Support Vector Machine for Classification

Unsupervised Learning

  • What is Clustering & its Use Cases?
  • What is K-Means Clustering?
  • How does K-Means Algorithm Work?
  • How to do Optimal Clustering
  • What is the C-Means Clustering?
  • What is Hierarchical Clustering?
  • How Hierarchical Clustering Works?

Association Rules Mining and Recommendation Systems

  • What are the Association Rules?
  • Association Rule Parameters
  • Calculating Association Rule Parameters
  • Recommendation Engines
  • How does Recommendation Engines work?
  • Collaborative Filtering
  • Content-Based Filtering

Reinforcement Learning

  • What is Reinforcement Learning
  • Why Reinforcement Learning
  • Elements of Reinforcement Learning
  • Exploration vs Exploitation dilemma
  • Epsilon Greedy Algorithm
  • Markov Decision Process (MDP)
  • Q values and V values
  • Q – Learning
  • α values

Time Series Analysis

  • What is Time Series Analysis?
  • Importance of TSA
  • Components of TSA
  • White Noise
  • Introduction to AR model
  • About the MA model
  • Understand the ARMA model
  • ARIMA model
  • Stationarity
  • ACF & PACF

Model Selection and Boosting

  • What is the Model Selection?
  • The need for Model Selection
  • Cross-Validation
  • What is Boosting?
  • How Boosting Algorithms work?
  • Types of Boosting Algorithms
  • Adaptive Boosting

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