פרטי הקורס

Module 1: Introduction to Artificial Intelligence

• Understanding the fundamentals and history of Artificial Intelligence
• Exploring different AI applications and domains
• Ethical considerations and implications of AI technologies

Module 2: Machine Learning Fundamentals

• Understanding the basics of Machine Learning
• Supervised, unsupervised, and reinforcement learning algorithms
• Evaluating model performance and dealing with overfitting and underfitting

Module 3: Data Preprocessing and Feature Engineering

• Preparing and cleaning data for Machine Learning models
• Handling missing data and outliers
• Transforming and engineering features for improved model performance

Module 4: Supervised Learning Algorithms

• Linear regression and logistic regression
• Decision trees and ensemble methods (Random Forests, Gradient Boosting)
• Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN)

Module 5: Unsupervised Learning Algorithms

• Clustering algorithms (K-means, Hierarchical clustering)
• Dimensionality reduction techniques (Principal Component Analysis - PCA)
• Association rule learning and anomaly detection

Module 6: Deep Learning Fundamentals

• Introduction to Deep Learning and Neural Networks
• Activation functions and backpropagation algorithm
• Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)

Module 7: Natural Language Processing (NLP)

• Text preprocessing techniques for NLP
• Word embeddings and language modeling
• Sentiment analysis and text classification using NLP

Module 8: Recommender Systems

• Collaborative filtering and content-based recommendation
• Evaluation metrics for recommender systems
• Building personalized recommendation engines

Module 9: Neural Networks and Deep Learning

• Advanced topics in Neural Networks architecture
• Deep learning frameworks (TensorFlow, PyTorch)
• Transfer learning and fine-tuning pre-trained models

Module 10: Applications of AI and Future Trends

• Real-world applications of AI and Machine Learning
• Exploring emerging trends and future directions in the field
• Ethical considerations and responsible AI practices