AI Course Syllabus

Great Learning offers a wide range of world-class AI courses to suit various needs and skill levels. The AI syllabus serves as a roadmap for students and working professionals, enabling them to navigate through multiple aspects of AI and ML, which typically include programming, artificial intelligence, machine learning, deep learning, neural networks, NLP, computer vision (CV), and generative AI (ChatGPT).
 

Artificial Intelligence Course Syllabus
 

Here is detailed information on the syllabus for each course, thoughtfully curated to aid aspiring AI enthusiasts in propelling their career paths:

 

1. Artificial Intelligence Course Online With Certificate - The University of Texas at Austin (UT Austin)
   
   &

2. AI Certificate Training (Classroom) - Great Lakes Executive Learning

 

• Module-1: Foundations of AI & ML

This introductory module equips learners with the fundamental concepts and techniques that form the backbone of AI and ML, laying a solid foundation for deeper understanding. By exploring diverse topics, students comprehensively understand applications and the crucial skills to excel in this domain.

 

1. Python for AI & ML - Basics, Jupyter Notebook, functions, packages, libraries, data structures, arrays, vectors, and data frames

2. Applied Statistics - Descriptive statistics, inferential statistics, probability, and hypothesis testing

 

• Module-2: Machine Learning

This module dives deeper into Machine Learning, providing a comprehensive understanding of various learning methods and techniques. Students will learn to implement these techniques and develop practical skills to navigate the ML landscape.

 

1. Supervised Learning - Regression, classification, and support vector machines

2. Unsupervised Learning - Clustering and Dimensionality Reduction

3. Ensemble Techniques - Decision trees, random forests, bagging, and boosting

4. Featurization, Model Selection & Tuning - Feature engineering, model selection and tuning, model performance measures, and ways of regularization

5. Demystifying ChatGPT and Applications - ChatGPT, OpenAI, NLP, and Generative AI

6. ChatGPT: The Development Stack - Mathematical fundamentals, VAEs, and GANs

7. Introduction to SQL - DBMS, normalization, joins, sorting, set operations, grouping, and filtering

 

• Module-3: Artificial Intelligence

This module offers a deep dive into artificial intelligence, exploring neural networks, deep learning, computer vision, and natural language processing. Learners will thoroughly understand these complex AI concepts and learn how to apply them in real-world scenarios.

 

1. Introduction to Neural Networks and Deep Learning - Gradient Descent, Perceptron, Batch Normalization, Activation and Loss Functions, hyperparameter tuning, Tensor Flow, and Keras

2. Computer Vision - Convolutional Neural Networks (CNN), transfer learning, object detection, and segmentation

3. Natural Language Processing - Preprocessing text data, Bag of Words Model, TF-IDF, Word2Vec, GLOVE, POS Tagging, Named Entity Recognition, Sequential Models, and Recurrent Neural networks (RNNs)

 

• Module-4: Additional Modules

This module introduces learners to more advanced concepts in AI and ML, further expanding their understanding and skills. This module covers an extensive range of topics, from data analysis to model deployment and visualization to advanced learning techniques.

 

1. Exploratory Data Analysis (EDA)

2. Time Series Forecasting

3. Model Deployment

4. Visualization using Tensor boar

5. GANs (Generative Adversarial Networks)

6. Reinforcement Learning

 

3. Data Science and Machine Learning Online Course - MIT Institute for Data, Systems, and Society

 

• Weeks 1-2: Foundations of Data Science

Students are introduced to the foundational concepts and techniques in data science during the first two weeks of the program. This phase focuses on developing a solid foundation in programming and statistical methods, laying the groundwork for more advanced topics later in the program.

 

1. Python for Data Science - NumPy, Pandas, and data visualization

2. Statistics for Data Science - Descriptive statistics and inferential statistics

 

• Week 3: Learning Break

 

• Week 4: Making Sense of Unstructured Data

Students discover the challenges and techniques associated with analyzing unstructured data during the fourth week of the program. This module delves into methods for revealing hidden patterns, relationships, and information in complex, unstructured datasets, which are common in today's data-rich world.

 

1. Introduction to Unsupervised Learning

2. Clustering

3. Spectral Clustering, Components, and Embeddings

 

• Week 5: Learning Break with Hands-on Masterclass 1

 

• Week 6: Regression and Prediction

In the sixth week of the program, students delve into the world of regression and prediction, discovering various techniques for modelling variable relationships and forecasting future outcomes. This module examines both classical and modern regression methods, addressing the challenges posed by high-dimensional data and emphasizing the significance of causal inference.

 

1. Classical Linear and Nonlinear Regression and Extensions

2. Modern Regression with High-Dimensional Data

3. The Use of Modern Regression for Causal Inference

 

• Week 7: Learning Break with Hands-on Masterclass 2

 

• Week 8: Classification and Hypothesis Testing

In the eighth week of the program, students learn about classification and hypothesis testing. This module focuses on techniques for validating statistical claims and methods for separating different groups or classes in a dataset, which are essential elements of data-driven decision-making. 

 

• Week 9: Learning Break with Hands-on Masterclass 2

 

• Week 10: Deep Learning

During the tenth week of the program, students explore the fascinating field of Deep Learning, a branch of Machine Learning that applies artificial neural networks to solve challenging issues. This module introduces deep learning architectures and techniques, enabling students to build intelligent systems that process massive amounts of data and change over time.

 

• Week 11: Recommendation Systems

In the program's eleventh week, students discover the world of recommendation systems frequently employed in e-commerce, entertainment, and other sectors to offer users individualized suggestions. This module explains the fundamental methods and algorithms that underlie these systems, enabling learners to develop personalized recommendations that improve user interactions and increase engagement.

 

1. Recommendations and Ranking

2. Collaborative Filtering

3. Personalized Recommendations

 

• Week 12: Networking and Graphical Models (Non-Graded)

In the twelfth week of the program, students learn the concepts of networking and graphical models, which provide powerful ways to represent complex relationships and dependencies among variables. Even though this module is not graded, it gives valuable insights into how these models can be used to analyze and extract information from complex systems.

 

• Week 13: Self-Paced: Predictive Analytics

Students begin a self-guided exploration of predictive analytics in the thirteenth week of the course, concentrating on methods and best practices for handling temporal data and feature engineering. This module equips learners to build more precise and efficient predictive models by learning to manage time-dependent data and extract valuable features.

 

1. Predictive Modeling

2. Feature Engineering

 

• Self-Paced Modules

Additionally, the course provides self-paced study modules centred on ChatGPT, a potent AI-driven language model. The knowledge gained from these modules will enable students to fully utilize ChatGPT's potential across a range of use cases and industries. These modules offer insights into the inner workings, development, and applications of ChatGPT.

 

4. AI For Leaders - The University of Texas at Austin (UT Austin)

 

• Module-1: Understanding AI through data

This module provides managers and leaders with an understanding of AI from a business perspective. By exploring data visualization and the business aspects of AI, learners can leverage this knowledge to make informed, data-driven decisions.

 

1. Business of AI

2. Data Visualization using KNIME

 

• Module-2: Supervised Learning

This module delves into supervised learning, a cornerstone of AI and Machine Learning. Students will grasp fundamental techniques like regression and classification and learn how to build effective proof-of-concept projects.

 

1. Regression - Linear regression, multivariate linear regression, and evaluation metrics

2. Classification - Logistic regression, Naive Bayes Algorithm, performance measures, and evaluation of models

3. Building POC for AI projects - Conceptualizing an AI product, market potential, and product development roadmap

 

• Module-3: Neural Networks & Ensemble Techniques

This module introduces learners to the world of Neural Networks and Ensemble Techniques. These fundamental AI and machine learning concepts equip students with advanced predictive analysis and decision-making skills.

 

• Module-4: Unsupervised Learning

In this module, learners explore unsupervised learning, a type of machine learning that uncovers hidden patterns in data. From clustering to recommendation systems and setting up AI teams, this module covers a diverse range of relevant topics.

 

1. Clustering - K-Means clustering, scaling, and applications of clustering

2. Recommendation systems - Content filtering, collaborative, and hybrid systems

3. How to set up AI teams & drive AI culture - service vs. product companies, AI team composition, handling resistance from senior management, and scaling AI teams

 

• Module-5: Deep Learning (CV & NLP)

This module delves into deep learning, primarily focusing on Computer Vision (CV) and Natural Language Processing (NLP). These are two of the most exciting and rapidly evolving areas in AI, transforming industries across the globe.

 

• Module-6: AI in Practice

This module teaches about the significant aspects of employing AI in a business. Students will be working on a project that helps to put their learning into practice.

 

1. Transfer Learning, Data Augmentation, and Model Deployment

 

5. Applications of AI Program - Great Learning

 

• Module-1: Introduction to AI

This module provides a comprehensive introduction to AI, laying the groundwork for understanding its history, significance, applications, and ethical implications. It's the perfect starting point for anyone keen to delve into the world of AI.

 

• Module-2: Machine Learning and Working with Data

This module delves deeper into machine learning, focusing on how machines learn, interpret results, and apply this learning in various contexts. This module provides a broad and practical understanding of ML, from recommendation systems to self-driving cars.

 

• Module-3: Frontiers of Artificial Intelligence

This module explores the cutting-edge advancements in AI, focusing on neural networks, deep learning, and their applications. From computer vision to natural language processing, this module provides a glimpse into the transformative power of AI.

 

• Module-4: Natural Language Processing (NLP)

This module offers a deep dive into Natural Language Processing, an exciting AI domain focusing on computer and human language interaction. This module covers a wide range of NLP applications, from building chatbots to sentiment analysis.

 

• Module-5: Computer Vision (CV)

This module focuses on Computer Vision, a field of AI that enables machines to understand and interpret visual data. From understanding digital image storage to image classification and recognition, learners will gain a comprehensive understanding of CV applications.

 

6. Post Graduate Diploma in Artificial Intelligence - Indraprastha Institute of Information Technology, Delhi (IIIT Delhi)

 

• Pre-Work: Math for AI and Statistics Refresher

Before delving into the main course content, this introductory module revisits essential mathematical concepts and statistical principles. These foundational skills are critical for understanding and applying AI algorithms effectively.

 

• Course 1: Programming with Python

This module dives into Python, a powerful language widely used in AI and data science. Students will acquire essential Python programming skills, from data structures to data visualization and preprocessing.

 

• Course 2: Data Structures and Algorithms

This module delves into the heart of computer science, exploring critical data structures and algorithms. Understanding these concepts is crucial for efficient problem-solving and building effective AI applications.

 

1. Arrays, Search, and Sorting Techniques - Linear Search, Binary Search, Bubble Sort, Insertion Sort, Quick Sort, and Merge Sort

2. Linked Lists - Understand the structure and types of linked lists, and learn operations like adding, removing, searching, and sorting elements

3. Stacks - Discover the principles of stacks, and learn to declare, initialize, push, pop, and peek elements

4. Queues - Get introduced to queues and understand their operations, such as enqueuing and dequeuing elements and accessing front and rear elements

5. Trees and Graphs - Explore linear and non-linear data structures, understand trees, B-Trees, and graph theory, including nodes, edges, cycles, and subgraphs

6. Binary Trees - Learn about Binary Tree and Binary Search Tree, their properties, implementation, and operations for efficient data storage and retrieval

 

• Course 3: Design and Analysis of Algorithms

This module delves into algorithm design and analysis principles, providing learners with a deep understanding of various algorithmic strategies and their applications in problem-solving.

 

1. Time & space complexity

2. Divide & conquer algorithm

3. Greedy search algorithm

4. Dynamic programming strategy

5. Breadth-first search & Depth-first search

6. Shortest path algorithm & Minimum Spanning Trees

7. Travelling Salesman Problem

 

• Course 4: Databases - SQL and NoSQL

This module provides a comprehensive introduction to databases, from traditional SQL to modern NoSQL systems. Understanding these methods is essential for data storage, retrieval, and manipulation in AI applications.

 

• Course 5: Machine Learning

This module introduces Machine Learning (ML), a key AI technology. It covers a range of concepts and techniques, from regression models and probability theory to unsupervised learning and dimensionality reduction.

 

• Course 6: Advanced Machine Learning

This module takes a deep dive into advanced machine learning concepts, covering a range of techniques from decision trees and ensemble methods to feature engineering and model performance measures.
 

• Course 7: Deep Learning for AI

This module explores the intricacies of Deep Learning, a subset of ML that's foundational for AI. It covers a broad spectrum, from the basics of neural networks to convolutional neural networks (CNNs) and their applications.

 

1. Math Basics for Deep Learning

2. Neural Networks 

3. Data pre-processing

4. CNNs

5. CV & NLP

 

7. No Code AI and Machine Learning - MIT Professional Education

 

• Module-1: Introduction to the AI Landscape

This foundational module provides a broad overview of the AI landscape, focusing on understanding data, prediction, decision-making, and causal inference.

 

1. Understanding the data: What is it telling us?

2. Prediction: What is going to happen?

3. Decision Making: What should we do?

4. Causal Inference: Did it work?

 

• Module-2: Data Exploration - Structured Data

This module delves into structured data exploration, focusing on understanding data, data visualization, exploratory data analysis, and data clustering techniques.

 

1. Asking the right questions to understand the data

2. Understanding how data visualization makes data clearer

3. Performing Exploratory Data Analysis using PCA

4. Clustering the data through K-means & DBSCAN clustering

5. Evaluating the quality of clusters obtained

 

• Module-3: Prediction Methods - Regression

This module focuses on regression techniques, model fitting, uncertainty quantification, and data scarcity issues, extending beyond linear regression concepts.

 

1. Regression Fundamentals

2. Model Fitting

3. Uncertainty Quantification

4. Dealing with Data Scarcity

5. Beyond Linear Regression

 

• Module-4: Decision Systems

This module delves into decision-making models, performance evaluation, ensemble learning, and the power of Random Forests in prediction aggregation.

 

1. Decision Tree Model

2. Performance Evaluation

3. Ensemble Learning and Bagging

4. Random Forests

 

• Module-5: Data Exploration - Unstructured Data

This module navigates the realm of unstructured data, focusing on natural language as an example and exploring applications of Natural Language Processing (NLP).

 

1. Unstructured Data and Natural Language

2. Business Applications of NLP

3. Analyzing Text Data

4. Sentiment Analysis

 

• Module-6: Recommendation Systems

This module explores recommendation systems, their concept, and their potential business applications.

 

1. The Concept of Recommendation Systems

2. Sparse Data Problem

3. Solutions to Recommendation Problem

4. Collaborative Filtering Recommendation Systems

 

• Module-7: Data Exploration - Temporal Data

This module unravels the intricacies of temporal data, a unique data modality, and explores the concept of Time Series forecasting.

 

1. Understanding Temporal Data

2. Time Series Forecasting

 

• Module-8: Prediction Methods - Neural Networks

This module demystifies the critical concepts of Neural Networks, including the encoding process, forward propagation, and optimization techniques.

 

1. Key Concepts of Neural Networks

2. Encoding and Non-linearities

3. Forward Propagation and First Prediction

4. Cost Function and Backpropagation

5. Optimization Techniques

 

• Module-9: Computer Vision Methods

This module delves into the realm of Computer Vision, exploring the spatial concepts of images, the workings of filters and convolutions, and the structure and learning mechanisms of Convolutional Neural Networks (CNNs).

 

1. Spatial Concepts of Images

2. Filters, Convolutions, and Feature Extraction

3. Convolutional Neural Networks

 

• Module-10: Workflows and Deployment

This module provides a holistic perspective on how the concepts discussed in previous modules are applied in real-world business scenarios, highlighting successful practical applications of Data Science and AI.

 

8. Master's in Machine Learning - The University of Arizona

 

• Module-1: Foundations of Information

This module provides a comprehensive understanding of the fundamental aspects of data in the realm of information science. It begins with learning how to sense data and covers critical aspects of data collection, usability, and storage.

 

1. Sensing the Data

2. Data Collection

3. Data Usability

4. Data Storage

 

• Module-2: Data Mining and Discovery

This module dives into the field of data mining, introducing the key concepts and techniques used to extract useful information from large datasets.

 

1. Introduction to Data Mining

2. Business Problem Identification and Scoping

3. Graphical Data Analysis

4. Unsupervised Learning Techniques

 

• Module-3: Data Analysis and Visualization

This module focuses on analyzing data and presenting findings using various visualization techniques.

 

1. Theory of Visualization

2. Single and Multiple Dimension Visualizations

3. Visualization for Audience

4. Interactive Visualizations

 

• Module-4: Introduction to Machine Learning

This module introduces machine learning, covering key concepts, theories, and techniques.

 

1. Linear Modelling

2. Learning Theory and Model Evaluation

3. Probabilistic Methods

4. Optimization and Approximation Methods

5. Classification Techniques

6. Domain Specific Techniques

 

• Module-5: Data Warehousing and Analytics in the Cloud

This module introduces students to cloud computing and data warehousing, focusing on setting up cloud environments and designing effective data warehouses.

 

1. Introduction to Cloud

2. Setting up Cloud and Parallel Processing

3. Data Warehousing

4. Data Warehouse Design

 

• Module-6: Text Retrieval and Web Search

This module delves into text retrieval and web search techniques, providing students with a comprehensive understanding of these essential areas of information science.

 

1. Introduction to Text Retrieval

2. Vector Space Modelling

3. Information Retrieval

4. Text Classification and Clustering

5. Text Handling Techniques

6. Web Search Basics

 

• Module-7: Neural Networks

This module provides a comprehensive overview of neural networks, covering key concepts and techniques.

 

1. Introduction to Neural Networks

2. Feed Forward Neural Networks

3. Regularization

4. CNN and RNN

5. Practical Considerations and Interpretability

 

• Module-8: Applied Natural Language Processing

This module delves into advanced Natural Language Processing (NLP) topics, providing students with practical, hands-on experience using NLP techniques.

 

1. Distribution Similarity

2. Sequence Models

3. Structured Learning

4. Alignment Models

5. Advanced Techniques

 

• Module-9: Artificial Intelligence and Machine Learning

This module covers the applications of Machine Learning in the field of Artificial Intelligence, with a particular focus on Computer Vision.

 

1. Computer Vision Application of Machine Learning

2. CNN Architectures and Transfer Learning

3. Object Detection

4. Generative Modeling

 

9. Master of Data Science - 24 Months - Deakin University

 

• First 12 Months

 

• Pathway: Artificial Intelligence and Machine Learning Pathway

 

• Module-1: Foundations

This introductory module equips learners with the fundamental concepts and techniques that form the backbone of AI and ML, laying a solid foundation for deeper understanding. By exploring diverse topics, students comprehensively understand applications and the crucial skills to excel in this domain.

 

1. Python for AI & ML - Basics, Jupyter Notebook, functions, packages, libraries, data structures, arrays, vectors, and data frames

2. SELF-PACED MODULE: EDA and Data Processing - Exploratory data analysis (EDA) and data processing techniques

3. Applied Statistics - Descriptive statistics, inferential statistics, probability, and hypothesis testing

 

• Module-2: Machine Learning

This module dives deeper into Machine Learning, providing a comprehensive understanding of various learning methods and techniques. Students will learn to implement these techniques and develop practical skills to navigate the ML landscape.

 

1. Supervised Learning - Regression, classification, and support vector machines

2. Ensemble Techniques - Decision trees, random forests, bagging, and boosting

3. Unsupervised Learning - Clustering and Dimensionality Reduction

4. Featurization, Model Selection & Tuning - Feature engineering, model selection and tuning, model performance measures, and ways of regularization

5. Recommendation Systems - Collaborative filtering, content-based filtering, and hybrid approaches

 

• Module-3: Artificial Intelligence

This module offers a deep dive into artificial intelligence, exploring neural networks, deep learning, computer vision, and natural language processing. Learners will thoroughly understand these complex AI concepts and learn how to apply them in real-world scenarios.

 

1. Demystifying ChatGPT and Applications - ChatGPT, OpenAI, NLP, and Generative AI

2. ChatGPT: The Development Stack - Mathematical fundamentals, VAEs, and GANs

3. Introduction to Neural Networks and Deep Learning - Gradient Descent, Perceptron, Batch Normalization, Activation and Loss Functions, hyperparameter tuning, Tensor Flow, and Keras

4. Computer Vision - Convolutional Neural Networks (CNNs), transfer learning, object detection, and segmentation

5. Natural Language Processing - Preprocessing text data, Bag of Words Model, TF-IDF, Word2Vec, GLOVE, POS Tagging, Named Entity Recognition, Sequential Models, and Recurrent Neural networks (RNNs)

 

• Module-4: SELF-PACED MODULE: Introduction to Reinforcement

Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) are two concepts that will be introduced to the students in this module. Since the module is self-paced, learners can proceed at their own pace.

 

1. Reinforcement Learning (RL)

2. Introduction to GANs (Generative Adversarial Networks)

 

• PROGRAM CURRICULUM FOR MASTER OF DATA SCIENCE (GLOBAL)

 

• Next 12 Months

 

10. Master of Data Science - 12 Months - Deakin University

 

• Module-1: Engineering AI Solutions

The module discusses the essential elements of creating an AI solution, emphasizing the distinctions between creating an AI solution and conventional software.

 

• Module-2: Mathematics for Artificial Intelligence

The module discusses the role of mathematics in AI and assists students in evaluating and communicating their findings to various audiences. Additionally, students will learn how to communicate their problem-solving strategies and read and interpret mathematical notation.

 

• Module-3: Machine Learning

The module covers techniques for evaluating and creating models, including logistic and linear regression/classification and model appraisal. Students will gain knowledge of the KNN and SVM concepts for assessing and creating classification models to address real-world issues. The module also covers multi-class classification models such as decision trees and random forests.

 

• Module-4: Modern Data Science

The module covers advanced concepts and the theoretical foundations of data science. Students will assess modern data analytics and their implications for real-world applications. The module also discusses collecting and processing relatively large datasets using appropriate platforms.

 

• Module-5: Real-World Analytics

The module discusses how to summarize data sets using multivariate functions, data transformations, and data distributions. The module also covers linear programming skills, game theory, and models for making optimal decisions. Students will learn how to create computer programs that can solve computational issues for real-world analytics.

 

• Module-6: Data Wrangling

The module covers researching data discovery and extraction methods and tools, as well as applying the knowledge gained to extract data based on project requirements. It emphasizes performing exploratory analysis on extracted data using statistical and machine learning techniques and communicating results to technical and non-technical audiences.

 

11. Machine Learning Online Course - Great Lakes Executive Learning

 

• Module-1: Foundations

This introductory module equips learners with the fundamental concepts and techniques that form the backbone of AI and ML, laying a solid foundation for deeper understanding. By exploring diverse topics, students comprehensively understand applications and the crucial skills to excel in this domain.

 

1. Python for AI & ML - Basics, Jupyter Notebook, functions, packages, libraries, data structures, arrays, vectors, and data frames

2. Applied Statistics - Descriptive statistics, inferential statistics, probability, and hypothesis testing

 

• Module-2: Machine Learning

This module dives deeper into Machine Learning, providing a comprehensive understanding of various learning methods and techniques. Students will learn to implement these techniques and develop practical skills to navigate the ML landscape.

 

1. Supervised Learning - Regression, classification, and support vector machines

2. Unsupervised Learning - Clustering and Dimensionality Reduction

3. Ensemble Techniques - Decision trees, random forests, bagging, and boosting

4. Featurization, Model Selection & Tuning - Feature engineering, model selection and tuning, model performance measures, and ways of regularization

5. Recommendation Systems - Collaborative filtering, content-based filtering, and hybrid approaches

 

• Module-3: Additional Modules

This module introduces learners to more advanced concepts in ML, further expanding their understanding and skills. This module covers an extensive range of topics, from data analysis to model deployment and visualization to advanced learning techniques.

 

1. Exploratory Data Analysis (EDA)

2. Time Series Forecasting

3. Model Deployment