Applied Machine Learning
Leveraging AI in Industry
This course equips participants with essential knowledge and practical skills to excel in real-world machine learning applications. The course covers the fundamental concepts of supervised and unsupervised learning, ensemble methods, neural networks, and their applications in diverse industries.
₹ 10,000 + GST
AI & ML
A basic understanding of machine learning concepts and programming.
Participants should have access to a computer with internet connectivity to engage in the online lectures, exercises, and practical sessions.
Individuals must have a genuine interest in learning AI and Ml.
What is included ?
Engage with interactive lessons that incorporate multimedia elements like videos, quizzes, and exercises to enhance your understanding and retention of the material.
Participate in online discussion forums where you can connect with fellow learners, share insights, ask questions, and engage in meaningful discussions related to the course content. .
Participate in practical assignments and projects that reinforce learning, allowing you to apply acquired concepts and skills. Gain hands-on experience and build confidence in the subject matter.
Regular assessments and quizzes evaluate your understanding of the course material, track your progress, and provide valuable feedback on your learning journey.
Overview of Machine Learning and its applications
Basics of Linear Algebra (vectors, matrices, operations)
Introduction to NumPy for numerical computing in Python
Linear Regression: Mathematical formulation
Gradient Descent and optimization
Implementation of Linear Regression using NumPy
Polynomial Regression: Theory and implementation
Overfitting and Underfitting issues in regression
Regularization methods (L1, L2 regularization)
Regularized Linear Regression with NumPy
Introduction to Classification problems
Logistic Regression: Mathematical foundation and binary classification
Loss Functions for Classification (Cross-Entropy, Hinge Loss)
Implementing Logistic Regression with NumPy
Decision Trees and
Decision Trees: Theory and implementation
Gini Index and Information Gain for splitting
Random Forests: Bagging and Pruning techniques
Building Random Forests from scratch with NumPy
Boosting and XGBoost
Introduction to Boosting and its advantages
Gradient Boosting: Theory and implementation
Introduction to XGBoost and its features
Implementing Gradient Boosting with XGBoost library
Introduction to Unsupervised Learning and Clustering
K-Means Clustering: Algorithm and mathematical details
Determining the optimal number of clusters using the Elbow Method
Implementing K-Means Clustering with NumPy
Feature Selection techniques (Filter, Wrapper, and Embedded methods)
Principal Component Analysis (PCA): Theory and mathematical foundation
Singular Value Decomposition (SVD) and its applications
Implementing PCA for dimensionality reduction with NumPy
Introduction and Basics
Introduction to Neural Networks and their architecture
Single-layer Perceptrons: Activation functions and learning
Multi-layer Perceptrons (MLPs): Backpropagation algorithm
Implementing MLPs for simple tasks with libraries like Keras
Neural Networks (CNNs)
Introduction to CNNs and their applications in image processing
Convolutional layers, pooling layers, and flattening
Introduction to Deep Learning and its real-world applications
Building and training a CNN for image classification using Keras
Prof. Sashikumaar Ganesan
Prof. Sashi, a dedicated educator with extensive expertise, takes great pleasure in mentoring graduates and working professionals alike. He has delivered enlightening courses on Artificial Intelligence, Machine Systems and Industrializing ML (MLOps) to over 750 working professionals, earning admiration from students and industry experts.