Artificial Intelligence and Machine Learning are revolutionizing industries across the globe. Our AI & ML course equips you with the skills to understand and implement intelligent systems. You will dive into concepts such as neural networks, natural language processing, and computer vision. Through practical exercises and projects, you will develop the ability to build and deploy machine learning models. This course is ideal for those looking to harness the power of AI and ML to create innovative solutions and stay ahead in the tech landscape.
1. Introduction to AI & ML History of AI and ML Types of AI: Weak AI, Strong AI, Artificial General Intelligence (AGI) Machine Learning vs. Artificial Intelligence vs. Deep Learning Applications of AI in various industries 2. Mathematical Foundations Linear algebra: Matrices, vectors, eigenvalues, and eigenvectors Calculus: Derivatives, gradients, partial derivatives Probability and statistics: Probability theory, distributions, Bayesian theorem Descriptive and inferential statistics Optimization techniques: Gradient descent, stochastic gradient descent 3. Python for AI & ML Python basics Key libraries: NumPy and Pandas for data handling Scikit-learn for machine learning algorithms Matplotlib and Seaborn for data visualization 4. Supervised Learning Linear regression and logistic regression Evaluation metrics: Mean Squared Error (MSE), R², accuracy, precision, recall, F1-score Classification algorithms: Decision trees, Random Forests, k-Nearest Neighbors (kNN) Support Vector Machines (SVM), Naive Bayes Overfitting and underfitting concepts Cross-validation techniques 5. Unsupervised Learning Clustering algorithms: K-means, hierarchical clustering, DBSCAN Dimensionality reduction: Principal Component Analysis (PCA), t-SNE Association rule learning: Apriori algorithm 6. Feature Engineering & Feature Selection Data preprocessing: Handling missing values, data scaling Feature selection methods: Filter, wrapper, and embedded techniques Handling categorical variables: One-hot encoding, label encoding Feature extraction techniques 7. Neural Networks and Deep Learning Introduction to neural networks: Structure and functioning Activation functions: Sigmoid, tanh, ReLU Training a neural network: Forward and backward propagation Loss functions: Mean squared error, cross-entropy Deep learning frameworks: TensorFlow, Keras, PyTorch 8. Convolutional Neural Networks (CNNs) Introduction to CNNs and their applications Convolution, pooling, and fully connected layers Popular architectures: LeNet, AlexNet, VGG, ResNet Image classification and object detection tasks 9. Recurrent Neural Networks (RNNs) Introduction to RNNs Long Short-Term Memory (LSTM) networks Gated Recurrent Units (GRUs) Sequence modeling applications: Time series forecasting, language modeling 10. Natural Language Processing (NLP) Text preprocessing: Tokenization, stemming, lemmatization Bag-of-Words and TF-IDF Word embeddings: Word2Vec, GloVe Transformers and attention mechanisms NLP tasks: Sentiment analysis, text classification, named entity recognition 11. Generative Models Introduction to generative models Generative Adversarial Networks (GANs) GAN architecture and training Applications of GANs in image and video generation Variational Autoencoders (VAEs) 12. Reinforcement Learning Introduction to reinforcement learning Markov Decision Processes (MDPs) Q-learning and deep Q-networks (DQN) Policy gradients Reinforcement learning applications 13. Advanced Topics in ML Ensemble learning: Bagging, boosting (e.g., XGBoost, LightGBM) Hyperparameter tuning: Grid search, random search, Bayesian optimization Transfer learning and pre-trained models Explainable AI (XAI) 14. Machine Learning Pipeline and Deployment Model evaluation, selection, and testing Saving and loading models Introduction to MLOps Deployment using Flask, FastAPI, or Docker Continuous integration and delivery (CI/CD) for ML models 15. Cloud Platforms for AI & ML Introduction to cloud platforms: AWS, GCP, Azure Using cloud services for ML model training and deployment Introduction to AutoML tools 16. Ethical and Societal Implications of AI Bias and fairness in AI systems AI governance and ethics Data privacy and responsible AI development 17. Capstone Project End-to-end project involving data collection, preprocessing, model development, and deployment Problem statement definition and solution design Presentation and documentation of findings
