learn Machine Learning
6 months
(3 hours/day)
44 students/ class
₹ 45,000
(Books are included)
course syallabus
Introduction
Machine Learning (ML) is a branch of artificial intelligence (AI) that enables
systems to learn from data, identify patterns, and make decisions with minimal
human intervention. It powers many of the intelligent systems we interact with
daily, including recommendation engines, predictive analytics, and autonomous
vehicles. This course covers the foundational concepts and algorithms of Machine
Learning, offering hands-on experience with popular tools and techniques.
You will learn how to implement supervised and unsupervised learning algorithms,
work with real-world datasets, and evaluate models using various performance metrics.
Topics include linear regression, classification, clustering, neural networks,
decision trees, and more advanced topics like deep learning and reinforcement
learning. By the end of the course, participants will have the skills to apply
ML algorithms to solve real-world problems in various domains, including finance,
healthcare, marketing, and more.
Objectives
After this course student will be able to:
- Understand the fundamentals of Machine Learning and its applications.
- Learn the differences between supervised, unsupervised, and reinforcement learning.
- Gain proficiency in implementing regression and classification algorithms.
- Master techniques like decision trees, random forests, and support vector machines (SVM).
- Understand model evaluation techniques (accuracy, precision, recall, F1-score, etc.).
- Learn how to preprocess data and handle missing values.
- Implement clustering algorithms like k-means and hierarchical clustering.
- Develop deep learning models using neural networks.
- Explore natural language processing (NLP) and its applications.
- Understand the ethical implications of machine learning in decision-making.
Certification
Upon successful completion of the course, participants will receive a Machine Learning Certification. This certification will validate your expertise in Machine Learning techniques and algorithms, making you a qualified candidate for data scientist, machine learning engineer, or AI developer roles.
Who can apply for this course?
- Data scientists and analysts looking to expand their skills in machine learning.
- Software developers and engineers interested in adding AI capabilities to their projects.
- Aspiring machine learning engineers or data scientists who want to build expertise in ML algorithms.
- Professionals in industries like finance, healthcare, or marketing looking to leverage machine learning for data-driven insights.
- Students or graduates in computer science or mathematics with an interest in AI and ML.
- Anyone eager to understand and apply machine learning to solve complex problems.
Training Methodology
- Instructor-Led Sessions: Live, interactive lessons to cover core Machine Learning concepts and techniques.
- Hands-On Practice: Implement ML algorithms using Python and libraries like Scikit-learn, TensorFlow, and Keras.
- Tool Training: Learn how to work with ML tools and frameworks, including TensorFlow, PyTorch, and scikit-learn.
- Case Studies: Analyze case studies from various industries to see how ML is applied to solve real-world problems.
- Assessments: Periodic quizzes and coding assignments to reinforce learning and test knowledge.
- Capstone Project: Build and deploy a machine learning model as part of the final project.
- Peer Collaboration: Work with peers on group projects and share insights to strengthen understanding.
