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Master Artificial Intelligence
Enroll in this AI essentials course to master AI skills. Learn machine learning, neural networks, computer vision, NLP, and generative AI. Discover how such technologies are changing industries and resolving various problems.
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Course outline
Industry focussed curriculum designed by experts
Overview of Artificial Intelligence
3 videos
50 mins
- Overview of AI
- Machine Learning terminologies
- Deep Learning terminologies
Introduction to Machine Learning
9 videos
- Machine Learning and its types
- Supervised Learning
- Unsupervised Machine Learning - Overview
- Applications of Unsupervised Learning
- Reinforcement Learning
- Clustering and its applications
- Applications of Reinforcement Learning
- How to evaluate a classification model
- Final Thoughts on Machine Learning
Introduction to Neural Networks
15 videos
3 hour and 30 minutes
- Deep Neural Network Overview
- Boolean Gates and Artificial Neuron
- Warren McCulloch and Walter Pitts Neuron
- Rosenblatt Neuron Perceptron?
- Artificial Neural Network
- Building Blocks of Neural Network
- Activation Functions
- Softmax Function
- Forward Propagation and Bias
- Loss function
- BackPropagation
- Gradient Descent
- Code Walkthrough
8 Coding Exercises
- Code Eval TensorFlow Questions- Beginner
- Code Eval Classification Questions - Beginner
- Code Eval Regression Questions - Beginner
- Code Eval Regression Questions - Intermediate
Introduction to Computer Vision
11 videos
3 hours
- Introduction to Computer Vision
- Types of Computer Vision Problems
- Pixel
- How a Computer Sees an Image?
- 3D Images
- Resolution
- Image Transformation
- Convolution
- Pooling
- Significance of Convolution and Pooling
- Convolutional Neural Networks
Introduction to NLP
11 videos
3 hours
- Intro to Natural Language Processing
- Automated Service Desk Tickets Routing
- AWS Installation
- Building a Model
- Different Tasks in NLP
- How Are NLP Problems Solved?
- Listening on Social Media to Analyze Sentiments About Your Brand
- NLP Demonstration on Sentiment Analysis
- Search Engine Optimization on Knowledge Base with NLP
- Sentiment Analysis with a Case Study
- Text Extraction - Web Scraping
6 Coding Exercises
- Coding Exercise questions for Beginner and Intermediate
Introduction to Generative AI
13 videos
3 hours and 30 minutes
- Introduction to Generative AI - Mind Map
- Introduction to Generative AI - Definitions
- A Peek into Generative AI Models
- Brief History of Generative AI
- Supervised and Unsupervised Learning
- Business Problems Solved by Generative AI
- Discriminative AI vs. Generative AI
- How Does a Model Predict the Next Word?
- How Does a Model Understand Text?
- Large Language Models (LLMs)
- Why Do Language Models Hallucinate?
- Introducing ChatGPT - Hands-On
- ChatGPT Demo
4 Coding Exercise Questions
- Coding Exercises - Prompt Engineering (Beginner)
Guided Project 1 : Clothing Classification
Guided Project 2 : Airline Sentiment using RNN
Guided Project 3 : Digit Recognition
AI Engineer - Mock Interview
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Start 7-Day Free TrialGuided Projects
Solve real-life problems in this AI Fundamentals Course by applying deep learning techniques like RNNs, CNNs, and computer vision. Build sentiment models, digit recognition systems, and clothing classifiers to strengthen your portfolio.
- GUIDED PROJECT 1
- Sentiment Classification of Tweets using RNNs
- This project focuses on building a Sentiment Classification model for Tweets using Recurrent Neural Networks (RNNs). The model will analyze Tweets and categorize them into Positive, Negative, Neutral, or Irrelevant, providing valuable insights into Public Opinion, Customer Sentiment, and Social Media Trends. By leveraging the power of Deep Learning, this project aims to enhance our understanding of the Emotional Landscape expressed within the vast realm of Social Media.
- GUIDED PROJECT 2
- Digit Recognition using CNNs
- Handwritten digit recognition is a cornerstone of Machine Learning, with diverse applications such as postal automation, banking systems, and efficient data entry. In this project, you’ll harness the power of Convolutional Neural Networks (CNNs) to accurately classify handwritten digits (0-9) using the renowned MNIST dataset.
- GUIDED PROJECT 3
- Clothing Classification using Computer Vision
- This project focuses on developing a Machine Learning model to classify clothing items using the Fashion MNIST dataset. Participants will preprocess the dataset, train a Deep Learning model, and evaluate its performance on unseen test data. Through this task, participants will learn to handle image data, build classification models, and assess the model's ability to generalize to new data. This project has key applications in e-commerce, inventory management, and retail analytics.
Gain skills & build your resume with complete access to guided projects in your free trial
Start 7-Day Free TrialCourse Instructors
Prof. Mukesh Rao
Senior Faculty, Academics, Great Learning
Dr. Abhinanda Sarkar
Senior Faculty & Director Academics, Great Learning
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30+ Guided Projects
200+ Coding Exercises
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Frequently Asked Questions
Who can enroll in the AI for Everyone course?
The course will suit anyone with a basic understanding of Python and the desire to sharpen AI skills. It is ideal to anyone who wishes to work in the AI field or be a data scientist and also to anyone who would like to know the most significant AI concepts, including machine learning, neural networks, and computer vision.
What will I learn during this AI fundamentals course?
Through this course, you will get acquainted with the fundamentals of AI, including the types of Machine Learning, Neural Networks, Computer Vision, Natural Language Processing (NLP) and Generative AI. You will also learn how to employ the concepts of AI in the real-life projects via Azure OpenAI.
What practical skills can I expect to acquire during this AI Essentials course?
In this course, you will be able to work on sentiment analysis with RNNs, digit recognition with CNNs, and clothing classification with computer vision by the end of this course. You will also get a rich experience in terms of building, assessing, and implementing AI.
Is this AI fundamentals course self-paced or instructor-led?
The AI fundamentals course is self-paced, which means that you can learn as fast as you want. Moreover, you will be able to work on guided projects that will provide you with starter code templates and solutions to develop your learning process.
Does this course have any practical projects?
Yes, the AI for everyone course includes three guided projects:
- Sentiment Classification of Tweets using RNNs
- Digit Recognition using CNNs
- Clothing Classification using Computer Vision
These projects will help you build a robust portfolio, showcasing your skills in NLP, deep learning, and computer vision.