Python and Machine Learning Training Course

Be an expert in Python & Machine Learning

python and machine learning course

What you will learn

  • Python and Machine learning training course from beginner to advanced level
  • 50 Modules and 11 popular Python libraries (Machine learning, Python basics, Tkinter, Turtle, Django, Pandas, NumPy, Matplotlib, Seaborn, Scikit learn, PyTorch, TensorFlow, etc.)
  • Chance to work with the product development team
  • Course Materials (PDF, Source code files, etc.)
  • Focus on job seekers and interview preparations
  • Placement training & project discussion
  • Be an expert in Python & Machine Learning

Key highlights

  • 70+ Hours of HD Video Course
  • Lifetime Access
  • Source code files & materials
  • Weekly doubt clear sessions (Live)
  • Dedicated mentorship assistance
  • Internship opportunities
  • Certificate of completion
  • Placement opportunities

Introducing Python and Machine Learning Training Course

This Python and Machine learning training course will teach you, complete development from beginner to advanced levels. After the training, you will become an expert in Python and also start and grow your career in machine learning. The course contains a lot of popular python and machine learning libraries like Tkinter, Turtle, Django, Pandas, NumPy, Matplotlib, Scikit learn, PyTorch, TensorFlow, etc. It’s an online self-paced course that is having 50 modules that you can learn right away. It is full of practical & real-time examples. Join and become a machine learning expert.

What Professionals Say about the training course

Python and Machine Learning Training Course

I just joined the Machine learning with Python Training course and am super excited with the way the instructors explain every concept.


Machine Learning Professional

Machine Learning Training Course

I joined the course when TSInfo offered the course for free, with more than 50 hours of Machine Learning From Basic to advanced course content. It is helping me while working at my office. The team covered almost everything.


Python Developer

machine learning training online

The machine learning with python training course helps me to start my journey in machine learning. Almost all lectures have source code files and pdf documents. Really easy to understand.


Python Developer, USA

Python and machine learning training course modules

The Python and machine learning training course is a 100% online self-paced video course that you can access lifetime. As soon as you register, you will be able to access all the modules, download files and source codes, etc.

The training covers not only python programming, but also 10 popular Python libraries.

Introduction to Machine Learning

Module-1: Introduction to Machine Learning

  • Introduction to Machine Learning
  • Why use Machine Learning
  • Steps of Machine Learning
  • Machine Learning Tools
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Supervised Learning vs Unsupervised Learning
  • Workflow of Supervised Learning
  • Categories of Supervised Learning
  • Types of Regression Algorithms
  • Types of Classification Algorithms
  • Workflow of Unsupervised Learning
  • Why use Unsupervised Learning
  • Categories of Unsupervised Learning
  • How Reinforcement Learning Works
  • Why use Reinforcement Learning
  • Types of Reinforcement Learning
  • Reinforcement Learning vs Supervised Learning
  • Markov Decision Process

Python Programming

Module-2: Introduction to Python Programming

  • Introduction to Python Programming
  • Features and where we can use Python
  • How Python is different from other programming languages
  • Companies using Python
  • Installation of Python (Windows, Linux, Mac)
  • How to access & work with Python online
  • Understanding Python environment (PIP, Conda, Venv)
  • Setting up Python Development Environment
  • Python Developer Tools
  • Your First Python Program
  • Git & GitHub

Module-3: Basics of Python

  • Python Identifiers & Keywords
  • Python Variables
  • Python Data Types
  • Indentation in Python
  • Python Naming Conventions & Coding Standards
  • Document Interlude in Python
  • Understanding of Operators in Python
  • Python Functions
  • The Anonymous Functions
  • Working with Control Statements
  • Working with Python Strings
  • Various String Operations with Examples
  • Working with Python List
  • List operations with Examples
  • Working with Tuples in Python
  • Various operations of Python Tuples
  • String vs List vs Tuple
  • Python Dictionaries
  • Various operations and functions using Dictionary
  • Python Sets
  • Advance examples of Python Sets
  • Dictionary vs Sets
  • Python Regular Expressions
  • Modules in Python
  • Packages in Python

Module-4: Exception handling in Python

  • Python Errors (Compile time, Runtime, Logical, etc. errors)
  • Understanding Various Python Exceptions (Built-in & User-defined Exceptions)
  • Exception Handling

Module-5: Working with Files in Python

  • Create, Read, Write, Append, etc. files
  • Working with binary files
  • Working with Docx in Python
  • Working with PDFs in Python
  • Working with CSVs in Python
  • Convert one file to another file
  • Convert PDF to docx in Python

Module-6: Object-Oriented Programming in Python

  • Overview of OOPs
  • Classes & Objects
  • Private, Public, & Self-variable
  • Functions or Methods
  • Constructor & Destructor
  • Abstraction & Encapsulation
  • Inheritance & Multiple Inheritance
  • Polymorphism

Module-7: Python Multithreaded Programming

  • What is Multithreading?
  • Concurrent Programming & GIL
  • Uses of Thread, Starting a New Thread
  • The Threading Module
  • Multiprocessing vs Multi-Threading
  • Thread Synchronization
  • Locks
  • Semaphore
  • Daemon Threads
  • Deadlock of Threads & Avoiding Deadlocks

Module-8: Using Databases in Python

  • Working with MySQL, SQLite3, MariaDB & PostgreSQL with Python
  • Create Database Connection
  • DDL Operation with Database

Python Tkinter

Module-9: Introduction to Tkinter Library

  • About GUI program and role of Tkinter
  • Installation on Windows, Mac, Linux & Docker
  • Methodologies of Tkinter
  • Write Your First GUI Program

Module-10: Geometry Manager in Tkinter

  • Working with Pack
  • Grid Geometry Manage
  • Place in Tkinter
  • Pack vs Place vs Grid

Module-11: Widgets in Tkinter

  • Display Text – Label & Message
  • User Input – Entry, Text, Scrollbar, Slider or Scale, Spinbox, Canvas
  • Containers – Frame, LabelFrame, TopLevel, Paned Window
  • Action Buttons – Button, Radiobutton, Checkbutton, Menubutton
  • Messagebox – Information, Confirmation, Warning, Errors
  • FileDialog – Single File, Multiple files, chooser folder
  • ColorChooser – Color Picker
  • Combo Box
  • Progressbar
  • Treeview
  • Notebook
  • Separator
  • Sizegrip
  • Working with images in Tkinter
  • Working with Python-Docx in Python Tkinter
  • Working with PyPDF2 in Python Tkinter

Module-12: Tkinter with Object Oriented Programming

  • Tkinter using Object Oriented Programming
  • Example of Tkinter with OOP
  • Notepad application using Tkinter

Module-13: Packaging and Distributing Executables

  • Overview of Pyinstaller
  • Convert Tkinter to exe using the console
  • Convert Tkinter to exe without console
  • Pack the exe to distributable file

Python Turtle

Module-14: Introduction to Python Turtle

  • Introduction to Turtle
  • Understanding Python Turtle Library
  • Get started with Turtle
  • Movement of the Turtle
  • Change Turtle Size & Shape
  • Drawing Shapes & Preset Figures
  • Change the Screen Color & Screen Title
  • Working with Pen in Turtle
  • Fill color in an Image
  • Customizing in One Line
  • Clearing the Screen
  • Resetting the Environment
  • Leaving a Stamp
  • Cloning Your Turtle

Module-15: Using loops & Conditional Statements in Turtle

  • Working with Turtle using for Loops
  • While Loops in Python Turtle
  • Conditional Statements in Python Turtle
  • Game project from scratch


Module-16: Django introduction and installation

  • Introduction to Web Framework & MVC Design Pattern
  • What is Django, features & what it is used for
  • Companies using Django
  • Installation of Django
  • Setting up database
  • Creating a project and application
  • Understanding the development server
  • Various useful Django commands

Module-17: Dynamic Web Pages in Django

  • View in Django: Dynamic Content
  • Mapping URLs to Views
  • How Django Processes a Request
  • URL configurations and Loose Coupling
  • 404 Errors
  • Your Second View: Dynamic URLs
  • Django’s Pretty Error Pages

Module-18: Template System in Django

  • Introduction to Django Template System
  • How to use Django Template System
  • Creating template objects
  • Rendering a template
  • Multiple contexts & Same template
  • Basic Template Tags & Filters Tags
  • render() vs render_to_response()
  • The locals() Trick
  • The include Template Tag
  • Template Inheritance
  • How to write your own Context Processors
  • Creating a Template Library
  • Writing Custom Template Filters & Tags

Module-19: Models in Django

  • Understanding models in Django
  • The MTV development pattern
  • Executing Queries in Views
  • Create your first model
  • Adding model string representations
  • Insert, update, select, delete & filter data objects
  • Ordering, slicing, chaining lookups data objects
  • Add, update, remove fields
  • Making Changes to a Database Schema
  • Removing models

Module-20: Django Administration

  • When and why to use the admin interface
  • Create and use the admin interface
  • Working with users, groups & permissions
  • How to customize the admin interface
  • Customizing the admin index page

Module-21: Forms in Django

  • Creating a feedback form in Django
  • Processing the submission of forms
  • Customizing forms
  • Validation rules for form
  • Creating forms from models

Module-22: Generic, Advanced Views and URL configurations in Django

  • Introduction to generic views and their objects
  • Extending generic views
  • Template contexts
  • Viewing subsets of objects
  • Complex Filtering
  • URL configuration tips
  • Streamlining Function Imports
  • Working with Named Groups
  • Understanding the Matching/Grouping Algorithm
  • Using Default View Arguments
  • Capturing Text in URLs
  • Including Other URL configurations
  • How Captured Parameters Work with include()

Module-23: Users and Registration

  • Understanding How Cookies Work in Django
  • Setting Up Cookies
  • Users & Authentication
  • Enabling Authentication Support
  • Logged-in Users Access Control
  • Managing users and messages
  • Creating profile and password
  • Managing profile

Module-24: Django Projects

  • Blog application
  • Social media clone
  • Company website

Python Pandas

Module-25: Introduction to Pandas

  • Pandas Introduction
  • Features of Pandas
  • Installation of Pandas using the package manager
  • Dataset for Data Analysis

Module-26: Basics of Pandas

  • Series and DataFrame
  • Import and Export CSVs, Excel & URLs
  • Head & Tail in Pandas

Module-27: Operation in Pandas

  • Selecting, Viewing & Describing data
  • Slicing the DataFrame using loc and iloc
  • Create & Delete Operations
  • Merge & Concat Operations
  • Handling missing values
  • Manipulating data using Group by & Crosstab
  • Working with date and time
  • Operations & Visualization of DataFrame

Module-28: Python Project Using Pandas

  • Google Play Store Apps

Python NumPy

Module-29: Introduction to Python NumPy

  • Python NumPy introduction
  • Install & Setup NumPy
  • Different ways to create arrays in NumPy
  • NumPy Data Types & Attributes
  • Working with NumPy Arrays
  • String Arrays in NumPy
  • Basic Slicing Nd Arrays
  • Copy, Arange & Random in NumPy
  • NumPy Arithmetic Operation
  • Logical & Comparison Operators in NumPy
  • Mathematical functions in NumPy

Module-30: Advanced Operations in NumPy

  • Working with Linear Algebra Module
  • NumPy Array Manipulation
  • NumPy Statistical Function
  • NumPy Counting Function
  • Working of universal function
  • NumPy Matrix Library
  • Basic & Advanced indexing on NumPy Arrays
  • Simple Linear Regression Using NumPy
  • NumPy Histogram Using Matplotlib
  • Scaler Objects
  • Character Arrays
  • Sorting Arrays using NumPy
  • Record Arrays
  • Masked Arrays
  • Working with File IO with Python NumPy


Module-31: Overview of Matplotlib

  • Introduction to Matplotlib
  • Matplotlib installation
  • How to start with Matplotlib
  • Start with a simple plot
  • Add title, labels, legend, grids, handling axes
  • Saving plots in different formats
  • Working with backend
  • Working with colors, lines, marker properties & handling ticks

Module-32: Working with different types of plots

  • Multiple Lines Chart
  • Bar Chart (Basic, Grouped, and Stacked)
  • Histogram
  • Scatter Plot
  • Pie Chart
  • Donut Chart
  • Error Bars
  • Polar Chart
  • Radial & Angular Grids
  • Quiver Plot
  • Contour Plot
  • Date Plot
  • Text in figure
  • Text Functions, Fonts & LaTeX Formatting
  • Annotations and Arrows
  • Subplots
  • Multiple figures
  • Twin Axes, Logarithmic Axes & Share Axes

Module-33: Statistical and Three-Dimensional Charts

  • Autocorrelation
  • Boxplots
  • Violin plots
  • Heatmap
  • Image Plotting
  • Color Bar
  • Basic 3D Plots
  • Advance 3D Plots

Module-34: Plotting Data From DataSource

  • Plotting Data from Pandas dataframe
  • Plotting Data from Web URL
  • Plotting Data from CSV
  • Plotting Data from Database MySQL 
  • Plotting Data from Database MariaDB 
  • Plotting Data from Database SQLite 

Module-35: Embedding Matplotlib in GUI

  • Introduction to PyQt5
  • Embedding Matplotlib figure in PyQt5
  • Introduction to Tkinter
  • Embedding Matplotlib figure in Tkinter
  • Introduction to Django
  • Embedding Matplotlib figure in Django
  • Introduction to wxFrame
  • Embedding matplotlib figure in wxFrame

Seaborn Library

Module-36: Working with Seaborn Library

  • Introduction to Seaborn library
  • Installation of Seaborn library
  • Relational Plots
  • Categorical Plots
  • Regression Plots
  • Matrix Plots
  • Multi-plot grids

Scikit learn

Module-37: Introduction to Scikit learn

  • What is Scikit learn
  • Scikit learn Installation (Windows, macOS, Linux)
  • How to implement Scikit learn Workflow
  • Modeling the Data in Scikit learn
  • Convert data into numbers
  • Handle Missing Data
  • Predicting and fitting the model in Scikit learn
  • Evaluation of the model in Scikit learn
  • Improvement of Model in Scikit learn

Module-38: Scikit learn supervised methods

  • Naive Bayes: Bernoulli – Multinomial
  • Support Vector Machines (SVM)
  • Logistic Regression
  • Predicting Example
  • Isotonic Regression
  • Linear regression – Lasso – Ridge
  • Decision Trees
  • Introduction to Ensemble Methods
  • Averaging Ensemble Methods
  • Boosting Ensemble Methods

Module-39: Scikit learn Unsupervised Methods

  • Density Estimation
  • Principal Component Analysis
  • K-Mean
  • DBScan
  • Clustering
  • Outlier Detection
  • Novelty Detection


Module-40: Overview of PyTorch

  • Introduction to PyTorch
  • Installation of PyTorch
  • How to start with PyTorch
  • Tensorflow vs PyTorch
  • 1 Dimensional Tensor
  • Vector Operations
  • 2 Dimensional Tensors
  • Slicing 3D Tensors
  • Matrix Multiplication
  • Gradient with PyTorch

Module-41: Linear Regression in PyTorch

  • Understanding of Linear Regression
  • Making Predictions
  • Linear Class
  • Custom Modules
  • Loss Function
  • Gradient Descent
  • Mean Squared Error

Module-42: Perceptron in PyTorch

  • What is Deep Learning
  • Creating Dataset
  • Model Setup
  • Model Training
  • Model Testing

Module-43: Convolution & Deep Neural Network in PyTorch

  • Convolutions and MNIST
  • Convolutional Layer
  • Pooling
  • Fully Connected Network
  • Non-Linear Boundaries
  • Feedforward Process
  • Backpropagation
  • Testing Mode

Module-44: Image Recognition in PyTorch

  • MNIST Dataset
  • Image Transforms
  • Neural Network Implementation
  • Neural Network Validation

Module-45: CIFAR10 Classification in PyTorch

  • The CIFAR 10 Dataset
  • Hyperparameter Tuning
  • Data Augmentation


Module-46: Overview of TensorFlow

  • Introduction To TensorFlow
  • Installation of TensorFlow
  • How to start with TensorFlow
  • Various Operations of TensorFlow
  • Slicing and Indexing of Tensor
  • TensorFlow vs NumPy
  • Linear Regression Model with TensorFlow

Module-47: Convolutional and Recurrent Neural Networks

  • Implementation of Convolutional neural network
  • Recurrent neural network implementation with TensorFlow
  • Difference Between CNN & RNN
  • A predicted model for time series data by using the RNN

Module-48: Perception in TensorFlow

  • Artificial Neural Network
  • Single layer perception
  • Multi-layer perception
  • Optimizers in TensorFlow
  • Recommendations for Neural Network Training
  • TensorFlow XOR Implementation

Module-49: TensorBoard Visualization

  • TensorFlow Graph Visualization using TensorBoard
  • How to visualize models, data, and Training with Tensor Board
  • Hyperparameter Tuning with the HParams Dashboard
  • Graph and loss visualization using TensorBoard

Machine Learning Examples

Meet Your Instructors

Shivam Dogra

Python with machine learning training course

Shivam is currently working with TSInfo Technologies as a Senior Python Developer. His expertise is in multiple domains like Python, Django, and Databases like SQL Server, MySQL, and MariaDB.

Vineet Singh

Machine learning with Python Training course

Vineet is working as a Senior Python developer with TSInfo Technologies. He is expert in using various Python libraries like Tkinter, Pandas, and databases like Oracle and PostgreSQL.

Arvind Vaishnavi

Machine Learning From Basic to Advanced

Arvind is currently working with TSInfo Technologies as a Senior Python Developer. He is proficient in Python libraries like NumPy, and Tensorflow.

Tanya Puri

machine learning training course

Tanya is working as a Senior Python developer with TSInfo Technologies. She is skilled in using various Python libraries like Matplotlib, Seaborn, and Django framework.

Vaishali Ganotra

Machine learning with Python Training course

Vaishali is currently working with TSInfo Technologies as a Senior Python Developer. She has experience in diverse Python libraries like Scikit learn, Turtle, and PyTorch.

Start Now

Enroll now to access the complete Python and Machine Learning Training Course

Frequently Asked Questions

Can I join the course as a fresher?

Yes, you can join the course as a fresher. We have prepared from the basic to advanced level so that even if you do not have any programming knowledge, you can learn and work in Python and Machine Learning.

Is it really lifetime access?

Yes, you can access it for a lifetime. One time you pay and access it forever.

Do you provide any help to get a job in Python?

Yes, we do provide that. Even we will help you to prepare for interviews. We at our company also requirement for Python developers and if you are good enough we will hire you as a python developer with us.

What will happen if I have doubts?

Do not worry, we have multiple options so you can clear your doubts. You can ask in the dedicated Facebook group. Also, you can email us at and if required we can have a zoom call also. You can also join our live doubt clear sessions on every Thursday at 7 PM IST.

Do I get a course completion certificate?

Yes, you will automatically get a course completion certificate at the end of the course.

Do I get an invoice so that I can get it reimbursed from my employer?

Yes. When you make the payment, you immediately get the invoice via email. If you do not receive the invoice, send us an email at

Still, if you have any other questions to ask, send an email to