AI | Machine Learning

AI | Machine Learning course is offered by Infoem Solution. Infoem Solutions, your trusted partner in IT education and professional development in Namakkal and beyond. With over a decade of experience, we specialize in comprehensive software training, app and website development, internships, and college projects.

Content:

• 1. Introduction to Artificial Intelligence
• Overview of Artificial Intelligence
• History and Evolution of AI
• Key Concepts in AI: Agents, Environments, and Goals
• AI Applications in Various Industries
• 2. Machine Learning Fundamentals
• Introduction to Machine Learning
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• Mathematical Foundations: Probability, Statistics, and Linear Algebra
• Data Preprocessing and Feature Engineering
• 3. Supervised Learning
• Regression
• Linear Regression
• Polynomial Regression
• Ridge and Lasso Regression
• Classification
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Support Vector Machines (SVM)
• Decision Trees and Random Forests
• Naive Bayes Classifier
• Model Evaluation: Cross-Validation, Confusion Matrix, ROC-AUC
• Hyperparameter Tuning: Grid Search, Random Search
• 4. Unsupervised Learning
• Clustering
• K-Means Clustering
• Hierarchical Clustering
• DBSCAN
• Dimensionality Reduction
• Principal Component Analysis (PCA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Association Rule Learning: Apriori, Eclat
• 5. Neural Networks and Deep Learning
• Introduction to Neural Networks
• Activation Functions and Backpropagation
• Deep Learning Basics: Neural Networks, Layers, and Nodes
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Generative Adversarial Networks (GANs)
• Transfer Learning and Pre-trained Models
• 6. Natural Language Processing (NLP)
• Text Preprocessing and Tokenization
• Bag of Words and TF-IDF
• Word Embeddings: Word2Vec, GloVe
• Sequence Models: RNNs, LSTMs, and GRUs
• Sentiment Analysis
• Text Classification and Summarization
• 7. Reinforcement Learning
• Introduction to Reinforcement Learning
• Markov Decision Processes (MDPs)
• Q-Learning and Deep Q-Networks (DQN)
• Policy Gradients and Actor-Critic Methods
• 8. AI in Practice
• Building AI Models with Python and Libraries (TensorFlow, Keras, PyTorch)
• AI for Computer Vision
• AI for Healthcare, Finance, and Other Industries
• Ethics in AI and Bias Mitigation
• 9. Project Work
• End-to-End Machine Learning Project
• Deep Learning Project with CNNs or RNNs
• NLP Project: Text Classification or Sentiment Analysis
• Reinforcement Learning Project
• 10. Soft Skills and Interview Preparation
• Problem-Solving Techniques
• System Design Concepts
• Coding Practice with Data Structures and Algorithms
• Mock Interviews and Resume Building
• 11. Optional Topics
• Advanced Deep Learning: Autoencoders, GANs, Transformers
• AI in Robotics
• AI for Internet of Things (IoT)
• AI Model Deployment and Monitoring