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Machine Learning For Absolute Beginners

This course gives you a simple overview of how machines learn to make decisions and predictions. See how they help businesses and services improve for you.

4.5/5
1000+ Students
Beginner
16 Classes (8 week)
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School

₹ 499/class

₹ 699/class

Any Query?

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Course Description

Machine Learning for Absolute Beginners is a simple and easy-to-follow course for anyone who wants to understand how machines “learn” from information and use it to make decisions. You don’t need to be a technical expert or have advanced knowledge—everything is explained in plain words with examples from everyday life.

Over 8 Weeks (16 Sessions), you will learn how machines spot patterns, make predictions, and help improve services that we all use daily. For example, you’ll see how online stores suggest products you may like, how banks catch fraud, and how schools or hospitals use data to improve results.

The course also talks about the challenges—like when machines make mistakes, when data is unfair, or when privacy becomes an issue. This helps you understand not just the good side but also the problems that come with it.

By the end of the course, you’ll work on a small project where you’ll use simple data to make a prediction (like guessing student scores). This will give you hands-on experience and help you see how the concepts work in real life. This course is perfect for students, beginners, or anyone curious about how modern technology is shaping the world around us.

Learning Outcomes

By completing this course, you will be able to:

  1. What is machine learning means – explained in easy language with simple examples.
  2. Know the different types of learning systems – and how they are used in real life.
  3. See why data is important – and how it is collected, cleaned, and organized.
  4. Learn how machines find patterns – and how they use information to make predictions.
  5. Discover real-world uses – in shopping, fraud prevention, healthcare, and education.
  6. Understand the limitations – such as mistakes, unfair data, and privacy concerns.
  7. Try a small project – where you’ll use basic data to make simple predictions.
  8. Build a strong foundation – so you can move on to more advanced topics in the future.

* What does it mean for machines to "learn"?
* Comparing machine learning to human learning
* Basic concepts of "training" machines
* Using examples to teach machines
* How machines become smarter over time

* Learning from examples (Supervised learning)
* Finding patterns without labels (Unsupervised learning)
* Learning through trial and error (Reinforcement learning)
* Mixing labeled and unlabeled data
* How machines adapt from previous experiences

* Identifying patterns in data
* Using past experiences to predict future outcomes
* Why mistakes are part of the learning process
* Measuring success and failure of predictions
* Simple examples: how online shops suggest products

* Personalized recommendations (Netflix, YouTube)
* Detecting fraud in banking systems
* Predicting trends in markets (stock prices)
* How smart photos and videos work on social media
* Helping voice assistants improve with use

* How machine learning is changing the way we shop, work, and travel
* Ethical issues around using personal data
* Why privacy matters in machine learning
* The role of machines in solving bigger global problems
* How machine learning could evolve in the next decade

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Machine Learning

School

₹ 499/class

₹ 699/class

Any Query?

Course Includes
Lectures
16 Classes
Duration
8 week
Level
Beginner
Language
English
Certificate
Yes
Available Coaching Centers:
No coaching centers found in this location. Please try another area.
Course Includes
Lectures
10 Classes
Duration
N/A Hours
Level
Beginner
Language
English
Certificate
Yes

What you need/Requirement

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Internet

You need a working internet connection to watch videos, join online classes, and get help when needed.

requirement image
Laptop/PC

A personal computer is essential for hands-on practice and project work.

requirement image
Coding Basics

Knowing basic coding helps you understand how to give instructions to a computer and build simple programs.

Learning Path

Beginner

Start with fundamental concepts and build a strong foundation.

Intermediate

Expand your knowledge and start building real projects.

Advanced

Dive deep into specialized areas and master complex techniques.

Master

Achieve expert-level proficiency and innovate with your skills.

CERTIFICATION

Earn Valuable Credentials
and Lead with a Competitive Edge.

🛡️

Certificate and Recognition That Validates Your Skills

Our curriculum is meticulously designed in collaboration with industry leaders to ensure every skill you acquire is not just current, but in high demand.

🔗

Get Mentorship From Top 1 % Industry Experts

Our mentors are seasoned professionals and thought leaders who provide unparalleled guidance and personalized feedback.

🧑‍🎓

Network For Lifelong Success

Our vibrant community of professionals offers continuous support, mentorship, and a platform for lifelong career acceleration.

Certificate

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Step Into the World of Machine Learning
A practical introduction to the world of Machine Learning
1
Explore the Foundations
Get a clear introduction to what Machine Learning is, how it works, and where it's used in the world around you. Set up your Python environment and explore your first dataset.
2
Build and Train
Dive into hands-on coding sessions where you'll learn to clean data, select features, and build your very first predictive models using industry-standard tools like Scikit-learn.
3
Evaluate and Apply
Learn how to measure your model's performance, work on a capstone project with a real-world dataset, and confidently present your results to earn your beginner's certificate.
Book a free Demo
Go from Beginner to Confident ML Practitioner
01
Understand the Foundation of Machine Learning
The journey begins with your first live session. In this first stage, you'll explore the big question: "What is Machine Learning?" and learn to differentiate between traditional programming and ML-powered systems.
02
Setting Up Your ML Environment
Guided by step-by-step tutorials, you'll learn to set up Python and install essential libraries like NumPy, Pandas, and Scikit-learn. You'll practice writing your first lines of code and loading your first dataset.
03
Exploring Data & Finding Patterns
Once comfortable with Python basics, the focus shifts to data exploration. You'll be introduced to how datasets are structured, how to clean messy data, and how to identify patterns that ML models can learn from.
04
Building Your First Predictive Models
This is where learning turns into creation. Using the skills from the previous steps, you'll build your first working ML models, like a regression model that predicts house prices or a classifier that identifies customer preferences.
05
Evaluate & Refine Your Models
Learning is better when measured! In this stage, you'll learn to evaluate model accuracy using metrics like precision and recall, and understand the importance of ethical AI practices in real-world applications.
06
Master the Workflow & Become a Data-Driven Problem Solver
After completing all the sessions, you'll have mastered the ML workflow and be comfortable using Python and Scikit-learn to build models that solve real-world problems independently.
What Makes This Course Special?
Clear explanations, practical projects, and expert guidance throughout
Personalised learning
Live Interactive Sessions
Personalised learning
Hands-On Coding Practice
Personalised learning
Beginner-Friendly Python Tools
Personalised learning
Real-World Dataset Projects
Personalised learning
Step-by-Step Learning Path
Personalised learning
1-1 Mentor Support

FAQs on Machine Learning For Absolute Beginners

Some courses offer free short courses or modules to sample content and build fundamentals before committing to longer programs; checking the free courses catalog is a good way to gauge fit.

Certificate programs generally issue a completion certificate upon successfully finishing all required modules and assessments; external “certifications” (industry exams) are distinct and may require separate testing with a third party body.

Yes. Post doubts in the Doubts section or during live classes; mentors reply asynchronously or schedule a live 1:1/Group session if a detailed discussion is required, and doubts are typically resolved within 24–48 hours.

Yes. Use the Break & Resume option to pause anytime and continue exactly where progress was left off, including lessons, quizzes, and assignments as per the policy.

Program pages typically list a clear duration (weeks or months) and learning hours; many certificate programs are flexible and self paced with structured milestones or live components depending on the course.