Scikit-learn Course with Certificate [Hindi]
Welcome to our Scikit-learn course, where we use Python to explore the fascinating field of machine learning. The goal of this course is to provide you a thorough grasp of Scikit-learn, a flexible and approachable Python machine learning package. You will gain an understanding of how to use Scikit-learn to create, train, and implement machine learning models for a variety of applications through practical examples and hands-on exercises.
What will you take home from this Scikit-learn Course?
- 15+ hrs self-paced expert-led course
- 30+ hrs of comprehensive study material
- 45+ hrs of real-world practicals
- 15+ Interactive quizzes & assessments
- 70+ Interview questions for top MNCs
- 15+ Real-time projects with implementation
- 40+ Machine Learning practical code Examples
- 98% Positive reviews from learners
- 15+ Comprehensive assignments
- 30+ Real-time industry case-studies
- 90+ Machine Learning tutorials
- 1:1 Career counselling with expert
- Practical knowledge which industry needs
- Industry-renowned certification
Your Scikit-learn Journey Starts Here — Enroll Now
Master Scikit-learn from Scratch
Join our hands-on Scikit-learn course crafted by industry veterans and build real-world skills. It’s not just a course, it’s a job-ready bootcamp.
Start Anytime (self-paced) |
Duration 15+ Hrs |
Access Duration 2 Years |
Price |
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Online Python for Non Programmers Free Training Course Curriculum
- Overview of Python programming language
- Installation and setup of Python environment
- Understanding Python’s role in various domains
- Variables, data types, and operators
- Control flow: if statements and loops
- Understanding indentation and code blocks
- Lists: creation, indexing, slicing
- Dictionaries: key-value pairs, accessing elements
- Tuples: immutable data structures and their usage
- Introduction to functions and their significance
- Defining functions with parameters and return values
- Calling functions and passing arguments
- Reading user input using input() function
- Writing to files and reading from files
- Understanding file modes and file handling operations
- Handling exceptions using try-except blocks
- Raising exceptions and handling specific error cases
- Writing robust code with error handling mechanisms
- Introduction to modules and their usage
- Importing modules and accessing functions
- Organizing code into packages for better organization
- Basic concepts of OOP: classes and objects
- Defining classes and creating objects
- Encapsulation, inheritance, and polymorphism
- Introduction to graphical user interfaces (GUIs)
- Creating windows, labels, buttons, and other GUI elements
- Handling events and user interactions in Tkinter applications
- Overview of pandas library for data manipulation and analysis
- Loading and exploring datasets with pandas DataFrames
- Performing basic data analysis tasks such as filtering, grouping, and aggregation
- Introduction to web development using Python and Flask framework
- Creating routes, handling requests, and rendering templates
- Building simple web applications with Flask
- Overview of automation tasks and their applications
- Automating repetitive tasks using automation libraries
- Writing scripts to interact with web browsers and desktop applications
- Hands-on projects to reinforce learned concepts and skills
- Guidance and support from instructors throughout project development
- Opportunities to apply Python knowledge to real-world scenarios and problems
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Scikit-learn Course Objectives
This Scikit-learn course aims to provide participants with a thorough understanding of machine learning and hands-on experience with the Scikit-learn package. First and foremost, participants will get a strong grasp of the basic ideas of machine learning, such as regression, classification, clustering, supervised and unsupervised learning, and so forth.
Through an organized curriculum, they will learn the fundamental algorithms involved in Scikit-learn, understand the usefulness of these methods, and understand the theory behind them. With this fundamental understanding, learners will be equipped to choose the right algorithms and techniques for different use cases and tackle ML problems from a more informed perspective. The second goal of the course is to provide students with practical experience applying Scikit-learn machine learning methods to real-world datasets.
Participants will learn to preprocess data, design features, choose and train models, and assess model performance through hands-on activities and projects. Best practices for hyperparameter optimization, model deployment, and model tuning will also be covered. By the end of the Scikit-learn course, participants will have acquired the self-assurance and know-how required to take on various machine learning problems and use Scikit-learn to create efficient solutions.
What is Scikit Learn?
Often shortened to sklearn, scikit-learn is one of the most well-liked and potent open-source machine learning packages for Python. Offering a large selection of machine learning algorithms and tools for creating predictive models, it functions as a powerful toolkit for data mining and analysis. Because of its well-known simplicity, effectiveness, and adaptability, Scikit-learn is a popular option for machine learning professionals of all experience levels.
Fundamentally, Scikit-learn offers an intuitive interface for various machine learning operations, such as model selection, dimensionality reduction, clustering, regression, and classification. It contains an abundance of algorithms, all of which have been painstakingly constructed and performance-optimized, enabling users to experiment with various models and methodologies easily.
Along with its rich collection of algorithms, Scikit-learn also contains feature extraction, data preprocessing, model evaluation, and cross-validation, making it a complete package for many machine learning tasks. Scikit-learn is a staple in the machine learning ecosystem. It provides developers, data scientists, and academics the power of machine learning in their projects with its rich documentation and simple to use architecture.
By enrolling in our Scikit-learn course, you can expect the following benefits:
After completing this Scikit-learn course, participants will have a comprehensive understanding of machine learning using Scikit-learn, along with a lot of practical experience. These elements are all-inclusive and describe the expected benefits for learners-
- To have a solid grasp of the fundamentals of machine learning, including clustering, regression, classification and supervised and unsupervised learning.
- Complete understanding of the Scikit-learn library, all its classes, modules, and functions for implementing, constructing, and evaluating machine learning models.
- To improve model accuracy and utility, the ability to digest raw data and produce valuable features, feature engineering and assembly.
- Proficiency in selecting suitable machine learning algorithms and applying cross-validation, hyperparameter tuning, and a variety of evaluation measures to assess model performance.
- Knowledge of ensemble learning strategies, including boosting, stacking, and bagging, and how to use Scikit-learn to implement them successfully.
- You may need to be well versed in dimensionality reduction techniques such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) to deal with high-dimensional data and improve model performance.
- Methods for decoding and representing machine-learning models to understand the underlying decision-making process and gain insight into a model’s prediction.
- Understanding the factors and best practices—such as scalability, performance optimization, and model monitoring—that should be considered when implementing machine learning models in real-world settings.
- Through projects and case studies, participants gain practical experience in machine learning that enables them to apply acquired ideas to real-world datasets and address real-world challenges encountered in industry.
Our students are working in leading organizations

Why should you enroll in this Scikit-learn Course?
- Get started with Scikit-learn, an extremely commonly used and powerful Python machine-learning library known for its efficiency and ease of use
- Receive each and every piece of advice and assistance from individual, knowledgeable teachers who are determined to assisting you in becoming an expert Scikit-learn user
- Immerse yourself in earning activities and projects that can consolidate your learning and gain expertise in machine learning
- Learn how to apply machine learning algorithms to solve real-world problems in marketing, finance, and healthcare
- Learn in-demand machine learning and data science skills to boost your résumé and prospects for employment
- Since machine learning is still essential in many businesses, develop abilities that are highly appreciated in today’s labor market
- Learn abilities that are in great demand in the current job market because machine learning is still a vital component of many companies
- Self-paced learning allows you to mold the course around your hectic lifestyle, enabling you to study whenever it suits you
- Understand the principles and approaches to machine learning required to create predictive models
- After completing the course, obtain a certificate of completion to demonstrate to prospective employers
Features of Scikit-learn Course


Scikit-learn Online Training FAQs
One well-known Python machine-learning library is called Scikit-learn. It offers straightforward and practical tools for data mining, data analysis, and developing and assessing machine learning models.
Students, software engineers, data scientists, analysts, and anyone interested in machine learning can all benefit from this Scikit-learn course. From new users to experts, it accommodates a range of ability levels.
Preparing data for analysis, feature engineering, choosing a model, assessment metrics, and machine learning methods, including clustering, regression, classification, and dimensionality reduction, are just a few subjects covered in this extensive Scikit-learn course.
Although it can be helpful, previous programming experience—especially with Python—is not necessary. Intended for students with different degrees of programming experience, the Scikit-learn course begins with fundamental ideas and works it up to more complex subjects.
Deep learning tasks are outside the purview of Scikit-learn, which is largely focused on conventional machine learning techniques. It can, however, be combined with deep learning tools like PyTorch or TensorFlow to preprocess data or create more straightforward models.
To enroll in this Scikit-learn course, there are no strict requirements. It would be helpful, nevertheless, to have a working knowledge of machine learning principles and a basic comprehension of Python programming.
This is a self-paced course. Using lectures and exercises that have already been recorded, participants can learn at their own speed with the option to connect with instructors and get help when necessary.
Depending on the advanced syllabus taught and each student’s unique learning pace, the Scikit-learn course duration may change. Since this is a self-paced course, each student can go about the learning journey according to his/her own time, pace and comfort.
Certainly, after fulfilling the criteria of the Scikit-learn course, participants will obtain a certificate of completion. Accreditation in machine learning and Scikit-learn methodologies is provided by this certificate.
Using our TechVidvan website, you can enroll in the course by choosing the Scikit-learn course during enrollment. After you sign up, you may start your learning adventure and get access to the course materials.