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ARTIFICIAL INTELLIGENCE

The Artificial Intelligence (AI) course offers a comprehensive introduction to AI concepts, techniques, and applications. You will learn key topics such as machine learning, neural networks, natural language processing, computer vision, and reinforcement learning. The course covers the fundamentals of AI algorithms and their real-world applications across industries like healthcare, finance, and robotics. Through hands-on projects and case studies, you’ll gain practical experience in implementing AI models using Python and popular libraries like TensorFlow and PyTorch. Ideal for aspiring AI engineers, this course prepares you to design and develop intelligent systems and solve complex problems using AI technologies.

Course Instructor: SKILLUMNI

₹4130.00 ₹6999.00 41% OFF

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Course Overview

Artificial Intelligence Course (2-3 Months)

This intensive, 2-3 month Artificial Intelligence (AI) course combines structured learning with hands-on experience to equip you with the essential skills and industry knowledge needed to succeed in the AI field.

Month 1-2: In-Depth Training with Industry Mentors

Objective: Build a strong foundation in AI and gain valuable industry insights under the guidance of experienced professionals.
Format: Pre-recorded content for flexible, self-paced learning.

Key Components:

  • Recorded Content: Access high-quality pre-recorded sessions for self-paced learning.
  • Topics Covered: Core AI concepts including machine learning, neural networks, natural language processing, computer vision, reinforcement learning, and AI applications in various industries.

Month 2: Practical Hands-On Project Experience

Objective: Apply your learned knowledge to real-world AI challenges through live projects and collaborative learning.

Key Components:

  • Live Research and Development Projects: Work on both minor and major AI projects to solve real-world challenges and solidify your skills.
  • Collaboration: Collaborate with peers in team-based projects to enhance problem-solving and innovation.
  • Mentorship: Receive ongoing guidance from industry experts, ensuring the application of best practices and advanced techniques throughout the project phase.

Certification

Upon successful completion of the program, you will receive:

  • Course Completion Certificate: Recognizing your mastery of AI concepts and successful completion of the training.
  • Collaborated Internship Completion Certificate: Validating the hands-on experience gained through real-world AI projects and collaboration.

This course provides the perfect blend of flexibility, professional mentorship, and practical experience to launch your career in Artificial Intelligence.

Schedule of Classes

Start Date & End Date

Mar 20 2025 - Jun 20 2025

Course Curriculum

1 Subject

ARTIFICIAL INTELLIGENCE

1 Exercises35 Learning Materials

MODULE 1: INTRODUCTION TO AI

CHAPETER 1: AI vs ML vs DL

Video
00:44:44

CHAPTER 2:Intoduction_ to AI & Real world AI Applications

Video
00:48:31

MODULE 2: NEURAL NETWORKS

CHAPTER 3: ANN Architecture

Video
01:45:31

CHAPTER 4: Backpropagation & Optimization

Video
00:48:21

MODULE 3: Deep Learning Frameworks

CHAPTER 5: Deep learning frameworks Tensorflow Overview

Video
00:46:55

CHAPTER 6: Deep learning frameworks PyTorch Overview

Video
00:45:22

MODULE 4: Convolutional Neural Networks (CNN)

CHAPTER 7: CNN Basics

Video
00:45:19

CHAPTER 8: Transfer Learning ResNet, VGG

Video
00:43:38

MODULE 5: Object Detection

CHAPTER 09: YOLO and Faster R CNN

Video
00:43:58

CHAPTER 10: Image Segmentation U-Net

Video
00:45:40

MODULE 6: NLP Basics

CHAPTER 11: Tokenization and Text Preprocessing

Video
00:49:53

CHAPTER 12: Word Embeddings Word2Vec GloVe

Video
00:41:22

MODULE 7: Advanced NLP

CHAPTER 13: RNNs, LSTMs, GRUs

Video
00:49:17

CHAPTER 14: Transformers BERT and GPT

Video
00:42:18

MODULE 8: Reinforcement Learning

CHAPTER 15: Markov Decision Process

Video
00:45:35

CHAPTER 16: Q Learning and DQN

Video
00:45:29

MODULE 9: Generative AI

CHAPTER 17: GANs Generative Adversarial Networks

Video
00:45:50

CHAPTER 18: Variational Autoencoders VAEs

Video
00:40:38

MODULE 10: Large Language Models (LLMs)

CHAPTER 19: LLMs Overview GPT and Gemini

Video
00:42:10

CHAPTER 20: Chapter 20 Fine tuning LLMs

Video
00:43:16

MODULE 11: AI in Production

CHAPTER 21: AI Model Deployment TF Serving ONNX

Video
00:41:45

CHAPTER 22: AI APIs OpenAI Hugging Face

Video
00:49:48

MODULE 12: AI Ethics & Bias

CHAPTER 23: AI Bias and Fairness

Video
00:46:19

CHAPTER 24: Explainable AI SHAP and LIME

Video
00:55:54

MODULE 13: Edge AI

CHAPTER 25: Chapter 25 AI on Edge Devices NVIDIA Jetson and Coral

Video
00:42:37

CHAPTER 26: TinyML Microcontrollers

Video
00:44:11

MODULE 14: AI for Business

CHAPTER 27: AI for Finance

Video
00:41:06

CHAPTER 28: AI For Healthcare

Video
00:44:10

MODULE 15: AI for Robotics

CHAPTER 29: Autonomous Robots and Control

Video
00:43:30

CHAPTER 30: AI in Industrial Automation

Video
00:52:27

MODULE 16: AI in Gaming

CHAPTER 31: AI In Game Developement

Video
00:58:42

MODULE 17: Ethical AI & Governance

Chapter 32: AI Regulations and Policies

Video
00:52:58

CHAPTER 33: Responsible AI Development

Video
00:57:51

Final Projects

CHAPTER 34: End to End AI Development

Video
01:04:04

CHAPTER 35: Final AI Deployment and Testing

Video
00:47:37

ASSIGNMENTS

ASSIGNMENT FOR AI

Assignment

Course Instructor

tutor image

SKILLUMNI

11 Courses   •   884 Students