“Python + Machine Learning = Career Success”


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45 Days

Mode of Training

Online Interactive Sessions


8000 INR


Any Graduates, Freshers, Engineers, Professionals, Tech Enthusiasts, or Entrepreneurs.


Machine learning is a form of artificial intelligence (AI) that allows computers to learn without being explicitly programmed. The goal of machine learning is to develop computer programs that can adapt to new data. In this article, we will cover the basics of machine learning and demonstrate the implementation of a simple machine-learning algorithm using Python.

Machine learning is a technique used to train computers to learn from data, without the need for explicit programming. Python is a highly favoured programming language for machine learning due to its vast collection of powerful libraries and frameworks that make it straight forward to implement machine learning algorithms.

To start with machine learning using Python, a basic understanding of Python programming and mathematical concepts such as probability, statistics, and linear algebra is required.

 Your Path to Success

Wide range of libraries and frameworks

High-performance computing

Easy to learn and use

Placement Assistance

Placement Assistance

Hands-On Projects


Learn at your own pace

Contact Team Experts

 Why Edifypath?

Industry Best Trainers: At Edifypath, our faculty members possess extensive real-time experience, are certified, have a passion for training, and are considered the best in the industry.

Tailored Course Curriculum: Edifypath offers a curriculum tailored to meet current industry needs, designed by experts and considered the best in the field.

Access to E-Learning: Participants will have access to recorded sessions of their instructor-led live classes, which will help them revise and recap the videos. They can also watch missed sessions on Edifypath's state-of-the-art Learning Management System (LMS). The recorded sessions can be accessed and watched even while on the move.

 What do you learn in Python for Machine Learning?

When learning Python for machine learning, you'll need to acquire a combination of foundational Python programming skills and specific knowledge related to machine learning concepts, libraries, and tools.

Here's a breakdown of what you should learn:

Basic building blocks of Python

String Operations and Regular Expressions

Data Types

File Handling

Control Structures

Exception Handling


The Object-Oriented Approach


Data Structures

 Who should take the course?

  • Anyone willing and interested to learn machine learning algorithms with Python.
  • Anyone who has a deep interest in the practical application of machine learning to real-world problems.
  • Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms.
  • Any intermediate to advanced EXCEL users who are unable to work with large datasets.
  • Anyone interested in presenting their findings professionally and convincingly.
  • Anyone who wishes to start or transit into a career as a data scientist.
  • Anyone who wants to apply machine learning to their domain.

Your Path to Success

Comprehensive Curriculum


Six months of placement support

Experienced Instructors

Easy & Convenient learning style

Valued Certification

Internship & Placement opportunities

 Build your subject-matter expertise

This course is available as part of multiple programs
When you enroll in this course, you'll also be asked to select a specific program.

Learn new concepts from Industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects


Chapter 1 : Basic building blocks
  • Introduction to Python
  • History of Python and its Features
  • Need of Python and its application in Different industries
  • Different IDE’s for Python
  • Installation of Anaconda Tool
  • Introduction to Jupyter Notebook
  • Basic Arithmetic and String Operations
  • Variable
  • Identifiers
  • Print and Input Function
Chapter 2 : Data Types
  • Numeric Data types
  • Integer
  • Float
  • Complex Number
  • Boolean
  • String
  • List - All List Operation
  • Tuple - All Tuple Operation
  • Dictionary - All Dictionary Operation
  • Set - All Set Operation
  • Type Casting: Data Conversion from one type to Another
Chapter 3: Operators
  • Logical Operators
  • Bitwise Operators
Chapter 4: Control Structure
  • Sequential
  • Alternative or Branching (If, If…else, If…elif…else)
  • Iterative or Loop (For loop, While loop, Break, Pass and Continue statements, Range functions)
Chapter 5: Functions
  • Pre defined Function
  • User Defined Function
  • Lambda Function
  • Recursive Function
Chapter 6: Data Structures
  • Linked List
  • Stack and Queue
  • Binary Tree
  • Binary Search Tree
  • Heap.
  • Sorting
  • Searching
  • Hashing
  • Asymptotic Analysis
  • Big-0-Notation
  • Time Complexity
Chapter 7: String Operations and Regular Expressions
  • String object basics
  • String methods
  • Splitting and Joining Strings
  • String format functions
Chapter 8: File Handling
  • Reading File
  • Writing Files
Chapter 9: Exception Handling
  • _Try__
  • Except
  • Finally
Chapter 10: The Object-Oriented Approach
  • Classes
  • Methods
  • Objects
  • Inheritance, Multiple Inheritance

About The Mentor

We are delighted to introduce Mr Arpit Yadav, who is an Artificial Intelligence Researcher and Senior Data Scientist. He is an international speaker on AI, Machine Learning, and Data Science. He is a member of several professional organizations, including the Institution of Electrical and Electronics Engineers, the Indian Society for Technical Education, and the Indian Council for Technical Research and Development.

He has filed 4 Patents, 3 Indian Patents, and 1 Australian Patent in Artificial Intelligence. He has published several international journal papers on Machine Learning and Deep Learning. He has completed over 70 certifications in Artificial Intelligence, Machine Learning, Deep Learning, and Data Science. He has served as an AI consultant to numerous start-ups and businesses. He has also served as a mentor for several AI-related projects.

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Python is widely used for machine learning for several compelling reasons:

  • Ease of Learning and Readability.
  • Rich Ecosystem of Libraries and Frameworks.
  • Active Community and Support.
  • Compatibility with Data Science Tools.
  • Cross-Platform Compatibility.
  • Integration with Production Systems.

Python's simplicity, extensive libraries, strong community support, and versatility make it a popular choice for machine learning. Its ease of use and rich ecosystem of tools and resources make Python an excellent language for developing, prototyping and deploying machine learning solutions.

Python is exceptionally popular for machine learning for several compelling reasons, which have contributed to its dominance in the field:

  • Versatility
  • Education and Training
  • Scalability and Deployment

Python's combination of simplicity, a rich ecosystem of tools, a supportive community, cross-platform compatibility, and versatility has propelled it to the forefront of the machine-learning landscape. These factors, among others, have made Python the preferred language for data scientists and machine learning practitioners worldwide.

Python for Machine Learning is a beginner's course, and you can begin the course with good knowledge of Python programming.

After completing this course, you will be well-equipped to advance your career in this leading field.

Python is a crucial skill to have for Machine Learning. The industry is rapidly growing, and Python's powerful libraries like Pandas, NumPy, and Matplotlib make it easier to handle tasks. Mastering Python will equip you with the necessary tools for efficient Machine Learning.

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