9 Sections
47 Lessons
10 Weeks
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Introduction to Artificial Intelligence
4
1.1
Introduction to Artificial Intelligence Part-1
1.2
Introduction to Artificial Intelligence Part-2
1.3
Application of Artificial Intelligence
1.4
Ethical consideration in AI
Foundations of Machine Learning
12
2.1
Machine Learning Fundamentals
2.2
Machine Learning Fundamentals Part-2
2.3
Machine Learning Fundamentals Lab-Sesson
2.4
Evaluation Matrics
2.5
Types of Machine Learning
2.6
Supervised Learning Algorithms
2.7
House price prediction Model
2.8
Decision Tree
2.9
Classification Model
2.10
Naive Bayes Classifier
2.11
Linear Support Vector Machine Model
2.12
Unsupervised Learning
Deep Learning and Neural Networks
7
3.1
Deep Learning and Neural Networks
3.2
Artificial Neural Network
3.3
Feedforward Neural Network
3.4
Convolutional Neural Network
3.5
CNN Model
3.6
Recurrent Neural Network
3.7
RNN Model
Natural Language Processing (NLP)
5
4.1
Natural Language Processing
4.2
Text Pre-Processing
4.3
Sentiment Analysis and Text Classification
4.4
Sentiment Analysis Model
4.5
Language Modeling and Generation
Computer Vision
7
5.1
Computer Vision
5.2
Image Processing
5.3
Object Detection
5.4
Image Segmentation Types
5.5
Image Classification Using CNN
5.6
Face Detection Model
5.7
Face Detection Model Part-2
Reinforcement Learning
8
6.1
Reinforcement Learning
6.2
Reinforcement Learning Part-2
6.3
Markov Decision Process
6.4
Reward Maximization Framework
6.5
Reinforcement Alogrithms (Q Learning)
6.6
Reinforcement Alogrithms (Value Iteration)
6.7
Reinforcement Algorithms (Deep Q Network)
6.8
Reinforcement learning Model
AI Ethics and Responsible AI
1
7.1
AI Ethics and Responsible AI
Applications of Artificial Intelligence
1
8.1
Applications of AI
Hands on Project
2
9.1
Project – diabetes prediction
9.2
Project – diabetes prediction Part-2
Artificial Intelligence Mastery
Curriculum
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