Duration: 3 Months
Overview of AI: Definition, history, and evolution.
Key concepts: Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Computer Vision (CV).
Applications of AI across industries: healthcare, finance, robotics, etc.
Ethical considerations in AI: Bias, fairness, privacy, and accountability.
Introduction to AI tools, libraries, and development environments (e.g., Python, TensorFlow, PyTorch).
Types of Machine Learning:
Supervised Learning (Classification, Regression)
Unsupervised Learning (Clustering, Dimensionality Reduction)
Reinforcement Learning (Q-learning, Markov Decision Process)
Core ML Algorithms:
Regression: Linear and Polynomial Regression
Classification: Logistic Regression, Decision Trees, Random Forests
Clustering: K-means, Hierarchical Clustering
Key Concepts:
Model training, testing, validation.
Overfitting and underfitting.
Metrics: Accuracy, Precision, Recall, F1 Score, MSE.
Hands-On Projects:
Build a regression model to predict housing prices.
Develop a classification model (e.g., spam email detection).
Customer segmentation using clustering.
Introduction to Neural Networks:
Basic architecture: Perceptron, hidden layers, activation functions (ReLU, Sigmoid).
Concepts: Backpropagation, forward propagation, loss functions, optimization (SGD, Adam).
Convolutional Neural Networks (CNNs):
Architecture of CNNs and layers: Convolution, pooling, fully connected.
Applications: Image recognition, object detection.
Project: Build a CNN for image classification (using datasets like MNIST or CIFAR-10).
Recurrent Neural Networks (RNNs):
Understanding sequential data.
Long Short-Term Memory (LSTMs), Gated Recurrent Units (GRUs).
Project: Develop a text generation model using RNNs.
Core Concepts:
Text preprocessing: Tokenization, stemming, lemmatization.
Bag of Words, TF-IDF, Word Embeddings (Word2Vec, GloVe).
Language models: Sequence-to-sequence models, transformers, BERT.
Applications:
Sentiment analysis.
Text classification.
Machine translation, chatbots.
Hands-On Project:
Build a chatbot or a text classification model (e.g., sentiment analysis).
Choose a real-world problem to apply AI techniques.
Example projects:
Predictive model (e.g., time-series forecasting or classification).
Image classification using CNNs.
NLP task such as building a chatbot or sentiment analysis.
Deliverables:
Project report including data collection, preprocessing, model building, and evaluation.
Code and documentation.
Ethical challenges in AI: Bias in algorithms, transparency, privacy.
Responsible AI: Fairness, accountability, and regulations.
The future of AI: Trends in automation, AI-driven technologies, and career opportunities.