This module introduces students to Python, one of the key programming languages in data science. The journey begins with foundational Python knowledge, installation and setup of the programming environment, and a look into Python's simple syntax. Students learn about Python's variables, data types, and operators, essential for manipulating and storing data. The second part of this module explores control flow tools, and functions in python. Students get hands-on experience with lists, tuples, dictionaries, conditional statements, loops, and functions. Complementing the Python-specific curriculum, the module also intertwines advanced Python libraries like NumPy, effectively for complex calculations and data analytics.

• Students will be able to recall and explain the basic syntax, variables, data types, and operators in Python.
• Demonstrate comprehension of Python's primary data structures (lists, tuples, dictionaries) and control flow tools (conditional statements, loops) by explaining their usage and significance in data manipulation and storage.
• Students will comprehend the functionality and usage of Python libraries such as NumPy, pandas, and matplotlib for data treatment.
• Students will be able to use Python for Data Science & Machine learning.

• Python introduction and setup.
• Python basic syntax.
• Python variables and data types.
• Usage of Python operators.
• Python data structures.
• Python conditional statements.
• Implementation of Python loops.
• Creation of Python functions.

This module delves into advanced mathematical concepts, including linear algebra, calculus, and probability theory, laying the groundwork for understanding the mathematical underpinnings of machine learning algorithms. Students engage in statistical analysis, exploring methods for data interpretation and hypothesis testing crucial for making informed decisions in AI projects. The module emphasizes the development of a robust quantitative skill set, enabling students to apply mathematical and statistical reasoning to the design, evaluation, and optimization of AI models. Through a combination of theoretical instruction and hands-on exercises, this module empowers learners to navigate the mathematical landscape that shapes the intelligence of AI systems, fostering a deeper comprehension of the algorithms driving contemporary technological advancements.

• Students will comprehend the functionality and usage of Python libraries such as NumPy, pandas, and matplotlib for data treatment.
• Apply descriptive and inferential statistical methodologies to analyze and interpret data.
• Formulate and test hypotheses, using these as a foundation for making data-driven decisions.
• Implement probability theory and Bayes' theorem to evaluate the uncertainty and update probabilities based on evidence in data science scenarios.

• NumPy for numerical computations.
• Data manipulation with pandas.
• Data visualization using matplotlib.
• Descriptive and inferential statistics.
• Hypothesis formulation and testing.
• Probability theory application.
• Understanding Bayes' theorem.
• Correlation and Regression Analysis

This module provides a comprehensive exploration of Python, emphasizing its role as a versatile and powerful language in the AI landscape. Students delve into Python's libraries, including NumPy, Pandas, and Scikit-Learn, gaining proficiency in data manipulation, analysis, and the implementation of various machine learning algorithms. Through hands-on coding exercises and practical projects, learners acquire the ability to leverage Python to preprocess and transform data, build predictive models, and assess their performance. The module fosters a practical understanding of how Python serves as a fundamental tool for AI development, preparing students to navigate the complexities of machine learning workflows and empowering them to contribute effectively to AI projects in both academic and professional settings. Following the preprocessing, the course advances into Data cleaning and preprocessing are essential steps in the data mining process. Data cleaning involves identifying and correcting errors, inconsistencies, and inaccuracies in the data to improve its quality. It includes tasks such as removing duplicates, filling in missing values, and correcting invalid data. Preprocessing, on the other hand, involves transforming raw data into a format that is suitable for analysis.

• Students will understand the importance of EDA and the process of data cleaning and preprocessing.
• Apply various data preprocessing techniques and manage data quality issues effectively.
• Develop Competency in Various Types of Data Cleaning and Preprocessing
• Acquire Practical Experience in Managing Numerical and Text Data

• Hands on with NumPy library
• Hands on with Pandas Library
• Hands on with Matplotlib Library
• Introduction to scikit-learn Library
• Data Collection
• Numerical Data cleaning and Preprocessing
• Hands on learning in dataset
• Text Data Cleaning and preprocessing
• Hands on learning in dataset
• Pattern Discovery
• Clustering and Classification

This module provides an in-depth understanding of established methods of artificial intelligence and machine learning techniques that enable computers to learn without being explicitly programmed. The module discusses various parts of artificial intelligence, which include ML (Machine Learning) and aims to explain real-world application. The module provides foundational understanding of AI concepts and terms, be able to describe several issues and ethical concerns surrounding AI, and articulate advice from experts about learning and starting a career in AI. Following the preprocessing, the course advances into algorithms such as linear regression, k-NN, decision trees, random forest, etc. for machine learning by supervised and unsupervised learning.

• Understand Artificial Intelligence and Machine Learning fundamentals.
• Understand the nature of the dataset and the methods for pre-processing it for machine learning.
• Introduction to Supervised and Unsupervised Machine Learning Algorithms
• Demonstrate a deep critical understanding of algorithms and mathematics behind established ML approaches

• Introduction to Machine Learning
• Supervised Machine Learning
• Unsupervised Machine Learning
• Regression
• Linear
• Univariate
• Multivariate
• Selection of an Algorithm
• Random Forest
• Decision Tree
• Logistic Regression
• Training and Testing models
• Checking F1-score, precision, and Accuracy for models

This module explores advanced mathematics and discrete optimization to create resilient and high-performance machine learning systems. Learners get to employ Python to construct multivariate calculus for machine learning to investigate the role of mathematical intuitions in creating Natural Language processes and algorithms. Furthermore, observe a demonstration using calculus and mathematical operations using Python; and grasp the use of limits and series expansions in Python. Key aspects presented here include extracting synonyms, atonyms, process, and text analysis for machine learning utilizing the Natural Language Toolkit package for Python to generate extremely fast tokenization, parsing, entity identification, and lemmatization of text. Throughout the module, we understand the relationship between ML and Natural Language processing by utilizing python for algorithm implementation. This module inculcates the traditional neural network learning methods, such as feed-forward neural networks, recurrent neural networks, and convolutional neural networks, with applications to natural language processing problems such as utterance classification and sequence tagging.

• Acquire the prerequisite Python skills to move into Natural Language Processing
• Understand NLP python packages to enable them to write scripts for text pre-processing
• Understanding text processing and vectorization for ML Use cases
• Develop and build fully automated NLP algorithms automated speech recognition, speech-to-text conversion, text-to-speech conversion.

• Introduction to machine learning
• Regression
• KNN
• SVM
• Keras
• TensorFlow
• Supervised learning
• Unsupervised learning
• ML deployment
• Automated speech recognition
• Text-to-speech conversion
• Decision theory
• Regression
• Classification
• Text Analysis applications
• Feed-forward neural networks
• Recurrent neural network
• Convolutional neural network
• Utterance classification
• Sequence tagging

This module begins by learning about numerical processing using the NumPy library, reading and changing photographs using the OpenCV library to open and deal with picture essentials, and gaining insight into using current deep learning network models like CNN & RNN. Comprehend image processing and apply various effects, including color mappings, mixing, thresholds, gradients, etc. Learners master video basics using OpenCV, including dealing with streaming video from a webcam. The module will overview Image Processing & Computer Vision using Python. It will cover how TensorFlow, and deep learning can be used for computer vision applications. Learners will learn to develop techniques to help computers see and understand the content of digital images, such as photographs and videos, using CNN (Convolution Neural Network). Learners will discover machine learning algorithms by utilizing neural networks, k-means clustering, and support vector machines in computer vision to analyze data based on supervised, unsupervised, and partially supervised. Additionally covered in this module are, Tensor flow, Faster- RCNN-Inception-V2 model, and Anaconda software development environment utilized to recognize autos and individuals in pictures that provides insight into the usage of current deep learning network models like CNN & RNN.

• Concepts of deep learning to build artificial neural networks and traverse layers of data abstraction and get a solid understanding of deep learning.
• Develop and build fully automated CV algorithms USING YOLO
• Develop the usage of Deep learning models like CNN and RNN
• Gain insights about advancements in CV, AI, and Machine Learning technique

• Introduction to machine learning
• Regression
• KNN
• SVM
• Keras
• TensorFlow
• Introduction to Computer Vision (CV)
• Deep Learning
• Network Models
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Introduction to Keras
• Model Life-Cycle
• Image Data Manipulation using Pillow Python library
• Convert Images to NumPy Arrays and Back

The Industry-based Capstone Project in Master of Artificial Intelligence and Machine Learning involves a close collaboration between students and industry mentors. This collaboration brings a real-world perspective and insights into the industry specific challenges that the AI solution aims to solve. Students will learn how to identify business challenges, collect data, apply AI algorithms, and evaluate the performance of the designed solution. The course equips students with established best practices and methodological frameworks to manage project development processes and prioritize the challenges to tackle. It forms an explicit foundation for developing critical project management, time-management, problem-solving, and presentation skills. Upon completion of the Industry-Based Capstone Project, students will have acquired essential skills such as project management, communication, practical problem-solving, and data analysis. Through this course, students will attain significant industry network exposure and thus gain a competitive edge for desired professional experiences.

• Apply AI & ML methodologies and techniques to address complex Industry specific problems.
• Develop and implement end-to-end AI solutions.
• Collaborate effectively with business stakeholders.
• Analyze the effectiveness of AI solutions.

• Developing and implementing entire AI & ML systems for their chosen industry
• Communicate and collaborate in a professional manner with business stakeholders.
• Understand business needs and requirements.
• Propose solutions.
• Evaluate the performance of the AI and ML solutions that they develop and assess their impact on the Industry.
Measure the success of their solutions and identify areas for improvement in future.

• Develop an algorithm to predict the risk of developing a certain disease, such as cancer or heart disease.
• Develop an algorithm to identify patients who are likely to respond well to a certain treatment
.
• Develop a machine learning model to improve the accuracy of medical diagnosis.
• Develop a data visualization tool to help healthcare providers identify trends and patterns in patient data.

• Develop an algorithm to detect fraudulent transactions
.
• Develop an algorithm to predict stock market movements.
• Develop a machine learning model to assess the risk of a loan applicant.
• Develop a data visualization tool to help financial analysts identify trends and patterns in financial data.

• Develop an algorithm to recommend products to customers based on their past purchase history.
• Develop an algorithm to predict which products are likely to be in high demand.
• Develop a machine learning model to optimize the supply chain.
• Develop a data visualization tool to help retailers identify trends and patterns in customer data.

• Develop an algorithm to identify defects in products.
• Develop an algorithm to predict when machines are likely to fail.
• Develop a machine learning model to optimize the manufacturing process.
• Develop a data visualization tool to help manufacturers identify trends and patterns in production data.

• Develop an algorithm to improve the accuracy of search results.
• Develop an algorithm to recommend videos to users.
• Develop a machine learning model to improve the performance of a machine learning model.
• Develop a data visualization tool to help users understand complex data sets.