The AI Certification Course at INSD Designing Institute is a 6-month intensive program designed to equip students with the fundamental and advanced concepts of Artificial Intelligence (AI) and its applications. This course covers machine learning, deep learning, natural language processing (NLP), computer vision, AI ethics, and AI-driven design innovations, making it ideal for students, professionals, and entrepreneurs looking to build expertise in AI-driven technologies.
Introduction to AI & Python for AI
| 1. Introduction to AI | - Definition & History of AI - Types of AI: Narrow AI, General AI, Super AI - AI Applications in Healthcare, Finance, E-commerce, etc. - Ethics & Challenges in AI |
| 2. Mathematics for AI | - Linear Algebra: Vectors, Matrices, Eigenvalues - Probability & Statistics: Bayes’ Theorem, Gaussian Distribution - Calculus: Derivatives, Partial Derivatives, Chain Rule |
| 3. Python for AI | - Basics: Variables, Loops, Functions, OOP - Libraries: NumPy (Arrays, Broadcasting), Pandas (DataFrames), Matplotlib (Data Visualization) - Data Preprocessing: Handling Missing Values, Encoding Categorical Data |
| 4. Basic AI & Search Algorithms | - Search Strategies: BFS, DFS, A* Algorithm - Optimization: Genetic Algorithm, Gradient Descent |
| 5. Hands-on Project: | - Build a Simple Chatbot - Data Visualization with Pandas & Matplotlib |
Machine Learning Basics
| 1. Introduction to ML | - ML vs AI vs Deep Learning - Types of ML: Supervised, Unsupervised, Reinforcement Learning |
| 2. Supervised Learning | - Regression: Linear Regression, Polynomial Regression - Classification: Logistic Regression, Decision Trees, k-NN |
| 3. Unsupervised Learning | - Clustering: K-Means, DBSCAN, Hierarchical Clustering - Dimensionality Reduction: PCA, LDA |
| 4. Model Evaluation & Performance Metrics | - Confusion Matrix, Accuracy, Precision, Recall, F1-score - Overfitting vs Underfitting, Bias-Variance Tradeoff |
| 5. Hands-on Project | - Predict House Prices using Regression - Customer Segmentation using Clustering |
Advanced Machine Learning
| 1. Ensemble Learning | - Bagging (Random Forest) - Boosting (Gradient Boosting, XGBoost, AdaBoost) |
| 2. Feature Engineering & Selection | - Handling Missing Data (Mean, Median Imputation) - Feature Scaling (Normalization, Standardization) - Feature Selection Methods (Recursive Feature Elimination, Mutual Information) |
| 3. Hyperparameter Tuning | - Grid Search, Random Search, Bayesian Optimization |
| 4. Hands-on Project | - Spam Email Classifier using Naïve Bayes - Predict Loan Approval using Decision Trees & Feature Engineering |
Introduction to Deep Learning
| 1. Basics of Neural Networks | - Perceptron, Activation Functions (Sigmoid, ReLU, Softmax) - Backpropagation, Loss Functions (MSE, Cross-Entropy) - Optimization Techniques (SGD, Adam, RMSProp) |
| 2. Deep Learning Frameworks | - Introduction to TensorFlow & PyTorch - Building Neural Networks with Keras |
| 3. Hands-on Project: | - Handwritten Digit Recognition using ANN (MNIST Dataset) |
Computer Vision & NLP
| 1. Convolutional Neural Networks (CNN) | - Convolution & Pooling Layers - Transfer Learning with Pre-trained Models (VGG16, ResNet) - Object Detection (YOLO, Faster R-CNN) |
| 2. Natural Language Processing (NLP) | - Text Preprocessing: Tokenization, Stopword Removal, Lemmatization - Word Embeddings: Word2Vec, GloVe, BERT - Sentiment Analysis & Text Classification |
| 3. Recurrent Neural Networks (RNN, LSTM, GRU) | - Sequence Modeling for Text & Time Series Data - Transformer Networks & Attention Mechanisms |
| 4. Hands-on Project | - Image Classification using CNN - Sentiment Analysis using LSTMs |
AI Applications & Deployment
| 1. Reinforcement Learning Basics | - Markov Decision Process (MDP) - Q-Learning & Deep Q Networks (DQN) |
| 2. AI in Real-world Applications | - AI in Healthcare (Disease Prediction) - AI in Finance (Stock Price Prediction) - AI in Automation (Self-driving Cars) |
| 3. Model Deployment & Cloud Integration | - Saving & Loading Models (Pickle, Joblib) - Deploying Models with Flask, FastAPI - Deploying AI Models on Cloud (AWS, Google Cloud, Azure) |
| 4. Capstone Project | - End-to-end AI Application (e.g., Chatbot, Fraud Detection, Image Recognition) |
1.1 Mathematics for AI
Reading Material:
Coding Exercises:
1.2 Python for AI
Reading Material:
Coding Exercises:
Data Visualization Dashboard using Matplotlib & Seaborn
1.3 AI & Search Algorithms
📚 Reading Material:
Coding Exercises:
Project Idea:
Pathfinding Algorithm for Maze Solver
2.1 Introduction to ML
2.2 Supervised Learning
2.3 Unsupervised Learning
2.4 Model Evaluation & Performance Metrics
2.5 Hands-on Project:
3.1 Ensemble Learning
3.2 Feature Engineering & Selection
3.3 Hyperparameter Tuning
3.4 Hands-on Project:
4.1 Basics of Neural Networks
4.2 Deep Learning Frameworks
4.3 Hands-on Project:
5.1 Convolutional Neural Networks (CNN)
5.2 Natural Language Processing (NLP)
5.3 Recurrent Neural Networks (RNN, LSTM, GRU)
5.4 Hands-on Project:
6.1 Reinforcement Learning Basics
6.2 AI in Real-world Applications
6.3 Model Deployment & Cloud Integration
6.4 Capstone Project:
