Machine Learning (35hrs)
Are you ready to dive into the exciting world of Machine Learning (ML) and Artificial Intelligence (AI). AnSu Intelligence is proud to offer a comprehensive Machine Learning course designed under the expert guidance of IITians. This course is tailored for both undergraduate and postgraduate students, providing an immersive semester-long program that equips you with the skills and knowledge needed to excel in the rapidly evolving field of AI.
Our mission
Our mission is to provide a world-class educational experience in Machine Learning and Artificial Intelligence that equips students with the knowledge, skills, and ethical grounding necessary to excel in the rapidly evolving tech landscape. We are committed to fostering a learning environment that emphasizes practical application, innovation, inclusivity, and responsible AI practices.
Our vision
Our vision for the Machine Learning course is to create a transformative educational experience that empowers students to become pioneers in the field of Artificial Intelligence and Machine Learning. We aim to cultivate a generation of innovative thinkers and skilled practitioners who can harness the power of AI to solve complex problems and drive positive change in society.
Course Content
Basic Module: Introduction to Machine Learning (6 hours)
Hour 1-2: Understanding machine learning: definition, types, and applications.
Hour 3-4: The machine learning pipeline: data collection, pre-processing, feature engineering.
Hour 5-6: Mathematical foundations: linear algebra basics and introductory calculus.
Start: Supervised Learning (6 hours)
Hour 7-8: Introduction to supervised learning: features, labels, and training data.
Hour 9-10: Linear regression: theory, cost functions, gradient descent.
Hour 11: Logistic regression: theory, binary and multiclass classification.
Hour 12: Decision trees and ensemble methods: theory, random forests.
Preserve: Unsupervised Learning and Evaluation (8 hours)
Hour 13-14: Introduction to unsupervised learning: clustering, dimensionality reduction.
Hour 15-16: Clustering algorithms: k-means, hierarchical clustering.
Hour 17: Dimensionality reduction techniques: principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE).
Hour 18-19: Model evaluation metrics: accuracy, precision, recall.
Advanced Topics and Practical Projects (15 hours)
Hour 20-21: Transfer learning: concepts, pre-trained models, fine-tuning.
Hour 22: Ethical considerations in machine learning: bias, fairness, transparency.
Hour 23-28: Hands-on projects: participants work on supervised and unsupervised learning tasks using real-world datasets.
Hour 29-31: Capstone project presentations and discussion of new ideas to be implemented in the future: participants showcase their machine learning projects and share insights.
Hour 32-35: Miscellaneous and Doubt session