Here are some of the core courses that students can expect to take during the program:
- Introduction to Artificial Intelligence: This course provides an overview of AI concepts, including problem-solving, reasoning, and knowledge representation. Students will also learn about AI applications in various fields, including robotics and natural language processing.
- Machine Learning Fundamentals: This course covers the basic principles of machine learning, including supervised and unsupervised learning, decision trees, and neural networks. Students will also learn how to develop and evaluate machine learning models.
- Data Structures and Algorithms: This course covers fundamental concepts in data structures and algorithms. It includes topics such as linked lists, trees, graphs, sorting, and searching.
- Data Analytics and Visualization: This course covers the principles of data analysis and visualization. Students will learn how to collect, clean, and process data, as well as how to visualize and communicate insights effectively.
- Deep Learning: This course provides an in-depth understanding of deep learning concepts, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Students will learn how to design and implement deep learning models for image recognition and natural language processing.
- Computer Vision: This course covers computer vision techniques, including image processing, feature extraction, and object recognition. Students will learn how to apply these techniques in real-world applications, such as self-driving cars and surveillance systems.
- Natural Language Processing: This course focuses on the application of machine learning and deep learning techniques to analyze and process human language. Students will learn how to develop systems that can perform tasks such as sentiment analysis and machine translation.
- Big Data Analytics: This course covers the principles and tools for processing and analyzing large datasets. Students will learn how to work with distributed computing systems such as Hadoop and Spark, and how to apply machine learning techniques to big data.
- Reinforcement Learning: This course covers the principles of reinforcement learning, a subfield of machine learning that involves training agents to take actions in an environment to maximize rewards. Students will learn how to design and implement reinforcement learning models for tasks such as game playing and robotics.
In addition to the above courses, students will also be required to take general education courses in areas such as mathematics, physics, and humanities. They will also have opportunities for hands-on learning through internships and research projects.
The tools, libraries, and technologies:
The tools, libraries, and technologies that are typically covered in a B.Tech in Artificial Intelligence and Machine Learning course:
- Programming languages: Python, Java, C++, R
- Machine learning frameworks: TensorFlow, Keras, PyTorch, Scikit-learn
- Deep learning libraries: NumPy, Pandas, Matplotlib, Seaborn
- Data processing and visualization tools: Tableau, Power BI, Excel, SQL
- Cloud platforms: AWS, Google Cloud Platform, Microsoft Azure
- Natural language processing (NLP) libraries: NLTK, SpaCy, Gensim
- Robotics platforms: ROS (Robot Operating System), Gazebo, V-REP
- Data mining and analysis tools: RapidMiner, Weka, KNIME
- Neural network libraries: Theano, Caffe, Torch
- Image processing libraries: OpenCV, PIL, scikit-image
- Reinforcement learning libraries: OpenAI Gym, RLlib, Stable Baselines
- Bayesian inference libraries: PyMC3, Stan
- Distributed computing frameworks: Apache Hadoop, Apache Spark
- Graph analysis libraries: NetworkX, igraph, graph-tool
- Time series analysis libraries: Prophet, ARIMA, LSTM
- AutoML libraries: H2O, TPOT, Auto-sklearn
- Decision-making and optimization libraries: PuLP, CVXPY, Pyomo
- Virtual assistants and chatbots: Dialogflow, Rasa, Wit.ai
- Edge computing libraries: TensorFlow Lite, OpenVINO, ONNX
- Blockchain and smart contracts: Hyperledger Fabric, Corda
projects.
Intake Capacity: 60
Eligibility: Candidates must have completed 10+2 or an equivalent examination from
a recognized board with a minimum aggregate of 50% in PCM (Physics, Chemistry, and Mathematics)
subjects
Duration: 4 Year
B.Tech in Artificial Intelligence and Machine Learning opens up a world of career opportunities for graduates. Here are some career prospects that graduates can pursue after completing this course:
- Machine Learning Engineer: Machine learning engineers design and implement machine learning systems that can learn and make predictions on their own. They work on developing models that can analyze large amounts of data and make predictions based on that data.
- Data Scientist: Data scientists work with large data sets to identify trends, patterns, and insights. They use statistical and machine learning techniques to analyze data and make predictions based on that data.
- AI Researcher: AI researchers work on developing new algorithms and techniques for machine learning and artificial intelligence. They work on cutting-edge research that helps to advance the field of AI.
- Robotics Engineer: Robotics engineers design and build robots that can perform various tasks. They work on developing algorithms that can control robots and make them perform complex actions.
- Business Intelligence Analyst: Business intelligence analysts work with large data sets to help organizations make informed decisions. They use machine learning and data analytics techniques to analyze data and identify trends and insights.
- Natural Language Processing (NLP) Engineer: NLP engineers work on developing algorithms and techniques that can understand and process human language. They work on developing systems that can perform tasks such as speech recognition and language translation.
- Computer Vision Engineer: Computer vision engineers work on developing algorithms and techniques that can analyze and interpret visual information. They work on developing systems that can perform tasks such as object recognition and image segmentation.
