Program Curriculum

Introduction to Artificial Intelligence

• Overview of AI
•   Definition and history of AI
•   Key concepts and applications of AI
• AI Tools and Technologies
•   Introduction to Python for AI
•   Overview of AI frameworks and libraries (TensorFlow, PyTorch, Keras)

Fundamentals of Machine Learning

• Supervised Learning
•    Linear regression, logistic regression
•    Decision trees, random forests
• Unsupervised Learning
•    K-means clustering, hierarchical clustering
•    Principal Component Analysis (PCA)

Advanced Machine Learning Techniques

• Ensemble Methods
•    Bagging, boosting, and stacking
• Model Evaluation and Selection
•    Cross-validation, hyperparameter tuning
•     Precision, recall, F1-score, ROC curves

Introduction to Deep Learning

• Neural Networks Basics
•  Perceptron, activation functions, and backpropagation
•  Introduction to deep learning frameworks (TensorFlow, Keras)
• Building Neural Networks
• Designing and training feedforward neural networks

Convolutional Neural Networks (CNNs)

• Basics of CNNs
•   Convolutional layers, pooling layers, and fully connected layers
•   CNN architectures (LeNet, AlexNet, VGG, ResNet)
• Applications of CNNs
•   Image classification, object detection, and segmentation

Recurrent Neural Networks (RNNs)

• Basics of RNNs
•   Recurrent layers, LSTM, and GRU
•  Applications of RNNs in sequence modeling
• Natural Language Processing with RNNs
•  Text generation, sentiment analysis, and machine translation

Advanced Deep Learning Techniques

• Generative Adversarial Networks (GANs)
•   GAN architecture and training
•   Applications of GANs in image generation and augmentation
• Transfer Learning and Fine-Tuning
•   Using pre-trained models for new tasks

Natural Language Processing (NLP)

• Introduction to NLP
•   Text preprocessing, tokenization, and embeddings
•   Word2Vec, GloVe, and fastText embeddings
• Advanced NLP Techniques**
•   Transformers and BERT
•    Named Entity Recognition (NER) and text summarization

Computer Vision

• Image Processing Basics
•   Image filtering, edge detection, and feature extraction
• Deep Learning for Computer Vision**
•   Advanced CNN architectures (Inception, MobileNet)
•   Applications in image recognition and video analysis

Reinforcement Learning

• Introduction to Reinforcement Learning
•   Key concepts: agents, environments, rewards
•   Q-learning and Deep Q-Networks (DQN)
• Advanced Topics in Reinforcement Learning
•    Policy gradients, actor-critic methods
•    Applications in game playing and robotics

AI Ethics and Deployment

• Ethical Considerations in AI
•   Bias, fairness, and accountability
•   Privacy and security in AI applications
• Deploying AI Models**
•   Model serving and scaling
•   AI in production: challenges and best practices

Capstone Project

• Project Planning and Execution
•   Scoping and planning the project
•   Data collection and preprocessing
•   Model development and evaluation
•   Integration and deployment
• Final Presentation
•    Demonstrating the AI solution
•   Presenting project findings and insights

Enroll in Offline Artificial Intelligence Course in Noida/Delhi NCR

99Skills training centre in Noida Sector 62 offers Artificial Intelligence Course that is uniquely tailored offline training experience. Benefit from direct interaction with expert instructors, personalized feedback, and hands-on practice with industry tools and datasets, ensuring comprehensive skill development for a successful career in AI Domain.

99Skills is one of the top institute for Artificial Intelligence Course in Delhi NCR/Noida