Artificial Intelligence

This course provides an in-depth introduction to Artificial Intelligence (AI), covering its core concepts, algorithms, and applications. It is designed for beginners and intermediate learners looking to understand how AI works and how it is transforming various industries.

The course emphasizes practical AI techniques, including machine learning, neural networks, and natural language processing.

Modules:

• Module 1: Introduction to Artificial Intelligence
• What is Artificial Intelligence?
• Definition and scope
• History and evolution of AI
• Key milestones in AI development
• Types of AI
• Narrow AI vs. General AI vs. Superintelligent AI
• Reactive Machines, Limited Memory, Theory of Mind, Self-aware AI
• AI Applications
• Industry use cases (healthcare, finance, automotive, etc.)
• Everyday AI applications (virtual assistants, recommendation systems)
• AI vs. Machine Learning vs. Deep Learning
• Understanding the relationships and distinctions
• Module 2: Foundations of Machine Learning
• Introduction to Machine Learning
• Definition and importance
• Types of machine learning: Supervised, Unsupervised, Reinforcement Learning
• Data Preprocessing
• Data collection and cleaning
• Feature selection and engineering
• Handling missing data and outliers
• Evaluation Metrics
• Accuracy, Precision, Recall, F1-Score
• Confusion Matrix
• ROC-AUC and other performance metrics
• Model Selection and Validation
• Cross-validation techniques
• Bias-Variance tradeoff
• Hyperparameter tuning
• Module 3: Supervised Learning Algorithms
• Linear Models
• Linear Regression
• Logistic Regression
• Decision Trees and Ensemble Methods
• Decision Trees
• Random Forests
• Gradient Boosting Machines (GBM), XGBoost, LightGBM
• Support Vector Machines (SVM)
• Fundamentals of SVM
• Kernel tricks
• k-Nearest Neighbors (k-NN)
• Algorithm mechanics
• Choosing the right k
• Module 4: Unsupervised Learning Algorithms
• Clustering Techniques
• K-Means Clustering
• Hierarchical Clustering
• DBSCAN
• Dimensionality Reduction
• Principal Component Analysis (PCA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Uniform Manifold Approximation and Projection (UMAP)
• Association Rule Learning
• Apriori Algorithm
• Eclat Algorithm
• Module 5: Deep Learning Fundamentals
• Introduction to Neural Networks
• Perceptron and Multi-layer Perceptrons (MLP)
• Activation functions
• Training Neural Networks
• Backpropagation and gradient descent
• Optimization algorithms (SGD, Adam, RMSprop)
• Regularization techniques (Dropout, L2 regularization)
• Convolutional Neural Networks (CNNs)
• Architecture and components
• Applications in computer vision
• Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
• Sequence modeling
• Applications in NLP and time-series data
• and more