Artificial Intelligence Course

Artificial Intelligence (AI) is a highly sought-after and exciting career field in today’s world, as technology such as neural networks, cognitive computing, self-driving cars, and speech recognition continue to advance and become a reality.

AI-ML engineers use their programming abilities along with their knowledge in math and computer science to create computer systems that mimic human cognitive processes such as the ability to learn from experiences and identify patterns.

An Artificial Intelligence (AI) course typically provides an overview of the field of AI and its various sub-disciplines, including machine learning, natural language processing, computer vision, and robotics.

The course will typically cover the following topics:

• Introduction to AI and its history, including the different approaches and techniques used in AI research.
• Fundamentals of Machine Learning, including supervised and unsupervised learning, decision trees, and neural networks.
• Natural Language Processing (NLP) including language models, sentiment analysis, and text generation.
• Computer Vision, including image processing, object recognition, and image segmentation.
• Robotics, including motion planning, control, and perception.
• Applications of AI, including self-driving cars, speech recognition, and computer gaming.
• Ethics and safety considerations of AI
• Tools and technologies used for AI development, such as TensorFlow, Keras, and PyTorch.
• The course will also introduce students to the latest research in the field of AI and provide opportunities for hands-on experience through projects and assignments.
• The course will be beneficial for students who are interested in pursuing a career in AI, machine learning, computer science, or related fields. It will also be beneficial for professionals looking to expand their knowledge and skills in the field of AI.

Artificial Intelligence Course Curriculum:

• Semester1
• Introduction to AI, Applications
• Machine Learning, Types
• Deep Learning, ANN, Batch Learning
• Web Scrapping, Searching the Tree
• Python basics, Anaconda Setup
• NumPy, Scipy, Pandas, sklearn
• Additional Topic: Operating System
• Semester-2
• Digital Image Processing Fundamentals
• Computer Vision concepts
• Introduction to OpenCv
• Image Classification
• Object Detection
• Pose Estimation
• Object Tracking
• Visual Saliency Estimation
• Additional Topic: Virtualization
• Semester-3
• Natural Language Processing introduction
• Sentiment Analysis
• Machine Translation
• Document Search
• Parts of Speech Tagging and HMM
• Word Embeddings with NN
• Additional Topic: Networking
• Semester-4
• Deep Neural Networks
• CNNs, RNNs, GANs
• Autoencoders
• Keras, Tensorflow, Pytorch
• YOLO, ResNets, GoogleNet
• Additional Topic: Cloud