AI Reading
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This curriculum from Tutorial Rays provides a complete, step-by-step guide to learning artificial intelligence, starting from the basics and progressing through 110 tutorials. It covers everything from AI fundamentals and Python programming to machine learning, deep learning, natural language processing, generative AI, and real-world projects.
- The course is structured into 12 modules, beginning with an introduction to AI and Python, and moving through data science, statistics, and essential libraries like NumPy and Pandas.
- It includes detailed coverage of machine learning algorithms (such as Linear Regression, SVM, and Random Forest) and deep learning topics like Neural Networks and CNNs.
- Natural Language Processing (NLP) and Generative AI, including Large Language Models like ChatGPT and Google Gemini, are covered in dedicated modules.
- The curriculum concludes with 8 real-world projects, such as building an AI chatbot, a house price prediction system, and a sentiment analysis tool.
Artificial Intelligence Complete Curriculum
Learn Artificial Intelligence step by step through 110 published tutorials, from AI fundamentals and Python to machine learning, deep learning, NLP, generative AI, and real-world projects.
1
Module 1: Introduction to Artificial Intelligence
5 tutorials
2
Module 2: Python Programming for AI
14 tutorials
2.1What is Python? Complete Beginner’s Guide›2.2Why Python is the Preferred Language for Artificial Intelligence›2.3Installing Python: Complete Beginner’s Guide›2.4Python IDEs and Development Tools›2.5Introduction to Jupyter Notebook›2.6Python Variables and Data Types›2.7Python Operators›2.8Lists and Tuples›2.9Dictionaries and Sets›2.10Conditional Statements›2.11Loops in Python›2.12Functions in Python›2.13Modules and Packages›2.14File Handling in Python›
3
Module 3: Data Science Fundamentals
8 tutorials
4
Module 4: Essential Python Libraries for AI
8 tutorials
5
Module 5: Statistics for Artificial Intelligence
10 tutorials
5.1Statistics for Artificial Intelligence – Introduction to Statistics›5.2Mean, Median, and Mode›5.3Variance and Standard Deviation›5.4Probability Basics›5.5Normal Distribution›5.6Correlation Analysis›5.7Outlier Detection›5.8Missing Value Treatment›5.9Bias and Variance›5.10Underfitting and Overfitting›
6
Module 6: Machine Learning Fundamentals
9 tutorials
7
Module 7: Machine Learning Algorithms
10 tutorials
8
Module 8: Evaluation Metrics for AI Models
9 tutorials
9
Module 9: Deep Learning
9 tutorials
9.1Deep Learning – Introduction to Deep Learning›9.2Artificial Neural Networks (ANN)›9.3Perceptron›9.4Multi-Layer Perceptron (MLP)›9.5Activation Functions›9.6Forward Propagation and Backpropagation›9.7Convolutional Neural Networks (CNN)›9.8Recurrent Neural Networks (RNN)›9.9Deep Learning Applications›
10
Module 10: Natural Language Processing (NLP)
12 tutorials
10.1Natural Language Processing (NLP) – Introduction to NLP›10.2Text Preprocessing›10.3Tutorial 84: Tokenization›10.4Stop Words Removal›10.5Tutorial 86: Stemming›10.6Lemmatization›10.7Named Entity Recognition (NER)›10.8TF-IDF›10.9Word Embeddings›10.10Text Classification›10.11Text Matching›10.12Sentiment Analysis›
11
Module 11: Generative AI & Large Language Models
7 tutorials
11.1Generative AI & Large Language Models – What is Generative AI?›11.2Generative AI & Large Language Models – Tutorial 95: Large Language Models (LLMs)›11.3Introduction to ChatGPT›11.4Introduction to Google Gemini›11.5Prompt Engineering Fundamentals›11.6Advanced Prompt Engineering Techniques›11.7AI Agents and Autonomous Systems›
12
Module 12: Real-World Artificial Intelligence Projects
8 tutorials
12.1Real-World Artificial Intelligence Projects – AI Chatbot Development Project›12.2House Price Prediction System›12.3Resume Screening AI System›12.4Customer Churn Prediction Project›12.5Sentiment Analysis Project›12.6Image Classification Project›12.7AI Recommendation System›12.8Final Capstone AI Project›
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Additional AI Resources
1 resources