Contact Us

DURATION
6 Months

MODE OF TRAINING
Online/Offline

LEVEL
Advanced

Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are transformative fields of computer science that aim to simulate human intelligence and enable machines to learn from data. AI focuses on creating systems capable of performing tasks that typically require human intelligence, such as decision-making, language understanding, and problem-solving. Machine Learning, a subset of AI, emphasizes developing algorithms that allow systems to improve their performance automatically through experience without being explicitly programmed. Together, these technologies power applications ranging from virtual assistants and autonomous vehicles to advanced healthcare diagnostics and financial analytics.

In this extensive course, discover the fascinating fields of machine learning and artificial intelligence. Discover the principles of artificial intelligence, including supervised, unsupervised, and reinforcement learning methods. Learn how to use Python libraries like scikit-learn, TensorFlow, and Keras to create machine learning models. Learn about computer vision, natural language processing, and neural networks. Engage in practical tasks such as image recognition, recommendation systems, and sentiment analysis. Recognize the limitations and ethical issues around AI. Develop your ability to solve problems by applying data-driven methods. For those looking to develop cutting-edge AI solutions for a variety of industries, this course is ideal.

AI and ML have revolutionized industries by offering unparalleled efficiency and insight into complex problems. Machine Learning models, like neural networks and decision trees, analyze large datasets to identify patterns, enabling predictions and recommendations with remarkable accuracy. These technologies are widely used in natural language processing, image recognition, and personalized marketing. As the field evolves, advancements in deep learning and reinforcement learning continue to push the boundaries of what machines can achieve, making AI and ML integral to shaping the future of technology and society.

AI & ML Course Curriculum

Introduction +

What Is Machine Learning?
Supervised Versus Unsupervised Learning
Regression Versus Classification Problems Assessing Model

Introduction And Linear Algebra +

Supervised Versus Unsupervised Learning
Introduction to Matrices
Vector spaces, including dimensions, Euclidean spaces, closure properties and axioms
Eigenvalues and Eigenvectors, including how to find Eigenvalues and the corresponding Eigenvectors

REGRESSION TECHNIQUES +

Linear Regression
Simple Linear Regression
Estimating the Coefficients
Assessing the Coefficient Estimates
Squared and Adjusted R Squared V
M SE and RMSE
Estimating the Regression Coefficients
OLS Assumptions
Multicollinearity
Feature Selection
Gradient Discent

General AI Questions +

What is Artificial Intelligence (AI)?
How is AI different from Machine Learning (ML)?
What are the types of AI (e.g., Narrow AI, General AI, Super AI)?
What are the ethical concerns related to AI?
How does AI impact industries like healthcare, education, and finance?

Machine Learning Basics +

What is Machine Learning (ML)?
What are the different types of ML (Supervised, Unsupervised, Reinforcement Learning)?
What are some common ML algorithms (e.g., Linear Regression, Decision Trees, Neural Networks)?
What is overfitting and underfitting in ML models?
How do you evaluate the performance of an ML model?

Enquiry Form

Only alphabets are allowed.

Email must start with alphabets followed by numbers.