Machine Learning - swiftlysewa career portal

This 6-week Machine Learning course is designed to take you from the fundamentals of data science to building intelligent models that can learn from data and make predictions. Whether you’re a beginner or someone with basic programming knowledge, this course provides a structured, hands-on approach to mastering machine learning concepts.

You will start with the core principles of machine learning, including data preprocessing, exploratory data analysis, and essential mathematical foundations. As the course progresses, you will dive into supervised and unsupervised learning techniques such as regression, classification, clustering, and dimensionality reduction.

Through practical examples and real-world projects, you will gain experience using popular tools and libraries like Python, NumPy, Pandas, and Scikit-learn. By the end of the course, you will be able to build, evaluate, and optimize machine learning models effectively.

🎯 What You Will Learn:

Fundamentals of Machine Learning and AI
Data preprocessing and feature engineering
Supervised learning (Linear Regression, Logistic Regression, Decision Trees)
Unsupervised learning (K-Means Clustering, PCA)
Model evaluation and performance metrics
Hands-on projects with real-world datasets

🚀 Course Outcome:

By completing this course, you will have the skills to:

Understand and implement machine learning algorithms
Work with datasets and extract meaningful insights
Build predictive models for real-world applications
Prepare for advanced AI and data science topics

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Course Content

Week 1
Week 1 introduces the fundamentals of Machine Learning and builds a strong foundation for the course. In Lesson 1, you learn what machine learning is, its types (supervised, unsupervised), and real-world applications. In Lesson 2, you explore the basics of data, including data types, data collection, and an introduction to data preprocessing. By the end of this week, you will have a clear understanding of how machine learning works and the importance of data in building intelligent models.

Lesson 1

08:24
Lesson 2

08:50
Quiz on week 1

Week 2
Week 2 focuses on the core concepts of Supervised Learning. In Lesson 1, you learn about regression techniques, especially linear regression, and how they are used to predict continuous values. In Lesson 2, you explore classification methods, where models are trained to categorize data into different classes. By the end of this week, you will understand how supervised learning works and be able to apply basic regression and classification models to solve real-world problems.

Week 3
Week 3 introduces the concepts of Unsupervised Learning. In Lesson 1, you learn about clustering techniques such as K-Means, which group similar data points without labeled outputs. In Lesson 2, you explore dimensionality reduction methods like PCA, used to simplify data while retaining important information. By the end of this week, you will understand how to discover hidden patterns in data and work with unlabeled datasets effectively.

Week 4
Week 4 focuses on Model Evaluation and Performance Improvement. In Lesson 1, you learn how to evaluate machine learning models using metrics like accuracy, precision, recall, and error measurement. In Lesson 2, you explore techniques to improve model performance, including overfitting, underfitting, and methods like cross-validation and regularization. By the end of this week, you will be able to assess how well your model performs and apply techniques to make it more accurate and reliable.

Week 5
Week 5 focuses on Advanced Machine Learning Concepts and Real-World Applications. In Lesson 1, you learn about ensemble methods and how combining multiple models can improve accuracy. In Lesson 2, you explore real-world applications and how machine learning is used in industries like healthcare, finance, and e-commerce. By the end of this week, you will understand how to build stronger models and apply machine learning techniques to practical, real-world problems.

Week 6
Week 6 focuses on Model Deployment and Final Projects. In Lesson 1, you learn how to deploy machine learning models into real-world environments using tools and frameworks. In Lesson 2, you work on a mini project that applies all the concepts learned throughout the course. By the end of this week, you will be able to take a machine learning model from development to real-world use and gain practical project experience.

A course by

Kushawaha Shankar

CAD CAM developer.

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Price:
Original price was: ₹900.00.Current price is: ₹400.00.
Instructor:Kushawaha Shankar
Duration: 20 hours
Lessons:12
Students:3
Level:All Levels