AI Course in Chandigarh Mohali

Data Structures

• Lists, Tuples, and Dictionaries
• Creating, accessing, and modifying lists, tuples, and dictionaries
• List comprehensions, dictionary comprehensions
• Exercise: Manipulate data structures to solve problems
• Sets and Strings
• Set operations, string manipulation techniques
• Exercise: Perform data cleaning and transformation tasks

Algorithms

• Sorting and Searching Algorithms
• Bubble sort, selection sort, insertion sort
• Binary search and linear search
• Exercise: Implement and compare different sorting algorithms
• Basic AI Concepts
• Introduction to AI, Machine Learning, and Deep Learning
• Overview of AI applications and use cases
• Exercise: Explore real-world AI examples and their impact

Linear Algebra

• Vectors and Matrices
• Basic operations with vectors and matrices
• Matrix multiplication and transformations
• Exercise: Implement linear algebra operations using NumPy
• Eigenvalues and Eigenvectors
• Understanding eigenvalues and eigenvectors
• Applications in data analysis
• Exercise: Compute eigenvalues and eigenvectors for given matrices

Statistics

• Descriptive Statistics
• Measures of central tendency and variability
• Probability distributions
• Exercise: Analyze datasets using descriptive statistics
• Inferential Statistics
• Hypothesis testing, confidence intervals
• Exercise: Perform hypothesis tests on real-world data

Data Preprocessing

• Data Cleaning
• Handling missing values, outliers, and duplicates
• Exercise: Clean and preprocess raw datasets
• Feature Engineering
• Feature scaling, encoding categorical variables
• Exercise: Engineer features for machine learning models

Supervised Learning

• Regression
• Linear regression, polynomial regression
• Exercise: Build and evaluate regression models
• Classification
• Logistic regression, k-nearest neighbors, support vector machines
• Exercise: Implement and compare classification algorithms

Unsupervised Learning

• Clustering
• K-means clustering, hierarchical clustering
• Exercise: Cluster datasets and interpret results
• Dimensionality Reduction
• Principal Component Analysis (PCA)
• Exercise: Reduce dimensionality of datasets using PCA

Model Evaluation and Tuning

• Model Evaluation Techniques
• Cross-validation, confusion matrix, ROC curve
• Exercise: Evaluate machine learning models using various metrics
• Hyperparameter Tuning
• Grid search, random search
• Exercise: Optimize model performance through hyperparameter tuning

Introduction to Deep Learning

• Neural Networks
• Structure and function of neural networks
• Activation functions, loss functions
• Exercise: Build a simple neural network from scratch
• Deep Learning Frameworks
• Introduction to TensorFlow and Keras
• Exercise: Set up and configure TensorFlow/Keras environment

Convolutional Neural Networks (CNNs)

• Basics of CNNs
• Convolutional layers, pooling layers
• Exercise: Build and train a CNN for image classification
• Advanced CNN Techniques
• Transfer learning, fine-tuning pre-trained models
• Exercise: Apply transfer learning to improve model accuracy

Recurrent Neural Networks (RNNs)

• Basics of RNNs
• Recurrent layers, LSTM, GRU
• Exercise: Build and train an RNN for sequence prediction
• Advanced RNN Techniques
• Attention mechanisms, sequence-to-sequence models
• Exercise: Implement attention in RNNs for better performance

Natural Language Processing (NLP)

• Basics of NLP
• Text preprocessing, tokenization, stemming, and lemmatization
• Exercise: Preprocess and analyze text data
• NLP Techniques
• Bag of words, TF-IDF, word embeddings
• Exercise: Implement text classification and sentiment analysis

Reinforcement Learning

• Introduction to Reinforcement Learning
• Basic concepts and terminology
• Exercise: Implement a simple reinforcement learning agent
• Deep Reinforcement Learning
• Q-learning, deep Q-networks (DQNs)
• Exercise: Train a DQN to solve a control task

AI Ethics and Best Practices

• Ethical Considerations in AI
• Bias and fairness, privacy concerns
• Exercise: Analyze and mitigate ethical issues in AI projects
• Best Practices for AI Development
• Model interpretability, deployment, and maintenance
• Exercise: Develop a deployment plan for an AI model

Capstone Project: End-to-End AI Solution

• Project Planning and Implementation
• Define project scope and objectives
• Develop and execute project plan
• Final Project Presentation
• Present project findings and results
• Receive feedback and iterate on the project

• Workshops and Guest Lectures: Industry professionals share experiences and insights.
• Practical Assignments: Hands-on projects to apply learned concepts.
• Field Trips: Visits to tech companies and AI development teams.
• Final Project: A comprehensive AI project to showcase skills and knowledge.