AI & ML Made Easy: From Basic to Advanced (2025)

Übersicht

AI & ML Made Easy:

From Basic to Advanced (2025) is a beginner-friendly yet comprehensive course designed to take you from the fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) to advanced concepts like Deep Learning, Natural Language Processing (NLP), and real-world applications.

Lehrplan

• Introduction to AI & ML
Overview of AI and ML
History and evolution of AI
Key concepts and terminology
• Programming Foundations for AI & ML
Introduction to Python
Essential libraries (NumPy, Pandas)
Data types and structures
• Data Handling and Preprocessing
Data collection and cleaning
Feature selection and engineering
Handling missing values
• Supervised Learning
Linear and logistic regression
Decision trees and ensemble methods
Model evaluation and metrics
• Unsupervised Learning
Clustering techniques (K-means, hierarchical clustering)
Dimensionality reduction (PCA, t-SNE)
Applications of unsupervised learning
• Neural Networks and Deep Learning
Basics of neural networks
Deep Learning architectures (CNN, RNN)
Introduction to training and optimization
• Natural Language Processing (NLP)
Text preprocessing techniques
Sentiment analysis and text classification
Advanced NLP models (transformers)
• AI & ML Tools and Libraries
TensorFlow and PyTorch
Scikit-learn overview
AI platforms and cloud services
• Ethical and Responsible AI
AI bias and fairness
Privacy and security concerns
AI governance and ethics
• Real-world Applications and Case Studies
AI in healthcare
AI in finance
AI in autonomous systems
• Advanced Topics and Trends
Deep Reinforcement Learning
Explainable AI
Future directions in AI research
• Hands-on Projects and Assignments
Implement a supervised learning project
Build an NLP model for sentiment analysis
Develop a deep learning application
• Review and Final Assessment
Course recap and key takeaways
Final project presentation
Feedback and course reflections

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Unterrichtet von

Programming Hub: 40 million+ global students and Laxminarayan Narayan G

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Fachgebiete

Computer Science