What You'll Learn

1. Implement machine learning algorithms like regression, classification, and clustering
2. Work with neural networks and deep learning techniques
3. Understand and apply natural language processing (NLP) methods
4. Use AI libraries and frameworks like TensorFlow, Keras, and Scikit-learn
5. Develop AI-based projects and solutions

Detailed Curriculum

1. Module 1: Introduction to AI and Machine Learning
   • Overview of Artificial Intelligence and its applications
   • Supervised vs unsupervised learning
   • Key machine learning algorithms: linear regression, decision trees, k-nearest neighbors
   • Introduction to Scikit-learn for machine learning
2. Module 2: Data Preprocessing and Feature Engineering
   • Handling missing data, normalization, and standardization
   • Feature selection and dimensionality reduction (PCA)
   • Data splitting: training, validation, and test sets
   • Encoding categorical variables and feature scaling
3. Module 3: Supervised Learning Algorithms
   • Detailed exploration of classification algorithms (logistic regression, SVM, KNN)
   • Regression algorithms: linear, polynomial, and support vector regression
   • Model evaluation: accuracy, precision, recall, F1-score
   • Cross-validation and hyperparameter tuning
4. Module 4: Unsupervised Learning and Clustering
   • Clustering algorithms: K-means, hierarchical clustering, DBSCAN
   • Dimensionality reduction techniques: PCA, t-SNE
   • Evaluating clustering models using silhouette scores
   • Anomaly detection and outlier analysis
5. Module 5: Neural Networks and Deep Learning
   • Understanding the basics of neural networks (perceptrons)
   • Activation functions and backpropagation
   • Introduction to deep learning and multi-layer perceptrons (MLPs)
   • Using Keras and TensorFlow for deep learning models
6. Module 6: Convolutional Neural Networks (CNNs)
   • Introduction to CNNs for image classification
   • Working with convolution layers, pooling, and fully connected layers
   • Building CNN models using Keras and TensorFlow
   • Transfer learning and fine-tuning pre-trained models
7. Module 7: Recurrent Neural Networks (RNNs) and NLP
   • Understanding RNNs for sequential data
   • Applications of RNNs in time-series forecasting
   • Introduction to Natural Language Processing (NLP)
   • Text classification, sentiment analysis, and tokenization
8. Module 8: Reinforcement Learning and Advanced Topics
   • Basics of reinforcement learning and Markov Decision Processes (MDPs)
   • Q-learning and deep Q-networks (DQNs)
   • Applications of reinforcement learning in games and robotics
   • Ethics and challenges in AI development
9. Module 9: AI Project Development
   • Designing and developing an AI-based project
   • Implementing machine learning or deep learning algorithms
   • Evaluating model performance and tuning parameters
   • Presenting AI solutions and preparing for career opportunities