Artificial Intelligence and Machine Learning (Basic Course E-book)
Foundations of AI & ML. Read and Learn How to work 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 & Machine learning.
Welcome to Foundations of AI & ML: Creating a Basic Course on Artificial Intelligence and Machine Learning.
Understanding Artificial Intelligence and Machine Learning
Understanding Artificial Intelligence and Machine Learning
Chapter 1
Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (acquiring information and rules for using that information), reasoning (using rules to reach approximate or definite conclusions), and self-correction. AI is often divided into two broad categories:
Fundamental Mathematics and Statistics for AI/ML
Chapter 2
AI and ML algorithms rely heavily on mathematical concepts. While high-level libraries (e.g., Scikit-learn, TensorFlow) allow you to implement models without deriving equations manually, understanding the underlying math empowers you to
Data Acquisition, Cleaning, and Preprocessing
Chapter 3
We delve into data acquisition, cleaning, and preprocessing, teaching you how to gather raw information, handle missing values, normalize features, and engineer new ones. Real-world datasets are rarely perfect; learning to prepare data effectively is critical for any successful AI/ML project.
Supervised Learning Techniques
Chapter 4
Supervised learning refers to algorithms trained on labeled datasets, where each example includes input features x\mathbf{x} and a corresponding target label yy. The algorithm learns a mapping f:x→yf : \mathbf{x} \rightarrow y that generalizes to unseen data. Supervised learning problems can be categorized as:
Foundations of AI & ML(English)
In this book, our goal is to demystify the rapidly evolving fields of Artificial Intelligence (AI) and Machine Learning (ML) by providing a structured, beginner-friendly guide. Whether you are a student, a professional seeking to upskill, or simply an enthusiast curious about how machines learn and make decisions, this course-like eBook is designed to walk you through the fundamentals, equip you with essential tools, and inspire you to build your own projects.
3 Language
In this book, our goal is to demystify the rapidly evolving fields of Artificial Intelligence (AI) and Machine Learning (ML) by providing a structured, beginner-friendly guide.
No course is complete without hands-on practice
Healthcare
Medical Imaging Diagnostics: Convolutional Neural Networks (CNNs) analyze X-rays, MRIs, or CT scans to detect anomalies such as tumors, fractures, or lesions with high accuracy.
Finance
Algorithmic Trading: ML models analyze historical market data, news sentiment, and macroeconomic indicators to make buy/sell decisions. Reinforcement learning approaches adapt strategies in real-time.
Retail and E-commerce
Recommendation Systems: Collaborative filtering, content-based filtering, and hybrid models suggest products based on user behavior, purchase history, and item attributes.
Manufacturing and Industry 4.0
Predictive Maintenance: Sensor data from machines analyzed by ML models predicts equipment failures before they occur, minimizing downtime.
Creating a Basic Course on Artificial Intelligence and Machine Learning
- Understanding Artificial Intelligence and Machine Learning
- Fundamental Mathematics and Statistics for AI/ML
- Data Acquisition, Cleaning, and Preprocessing
- Supervised Learning Techniques
Basic Course on Artificial Intelligence and Machine Learning
Basic Course on Artificial Intelligence and Machine Learning
- Unsupervised Learning and Clustering
- Introduction to Neural Networks and Deep Learning
- Tools, Frameworks, and Implementation
- Real-World Applications, Ethics, and Future Trends
AI and ML have moved from niche research topics to driving forces behind many modern technologies
Courses & Chapter
- Chapter 1: Understanding Artificial Intelligence and Machine Learning
- Chapter 2: Fundamental Mathematics and Statistics for AI/ML
- Chapter 3: Data Acquisition, Cleaning, and Preprocessing
- Chapter 4: Supervised Learning Techniques
- Chapter 5: Unsupervised Learning and Clustering
- Chapter 6: Introduction to Neural Networks and Deep Learning
- Chapter 7: Tools, Frameworks, and Implementation
- Chapter 8: Real-World Applications, Ethics, and Future Trends
Basic Course on Artificial Intelligence and Machine Learning
Whether you are a student, a professional seeking to upskill, or simply an enthusiast curious about how machines learn and make decisions, this course-like eBook is designed to walk you through the fundamentals, equip you with essential tools, and inspire you to build your own projects.
Apa yang akan kamu pelajari
- Unsupervised Learning and Clustering
- Introduction to Neural Networks and Deep Learning
- Tools, Frameworks, and Implementation
- Real-World Applications, Ethics, and Future Trends
$10/*
$100
About the E-book Author
Abhijit Mayra is the founder and principal consultant at Digital One Web Solutions, based in Kolkata, India. Since 2018, Abhijit has guided businesses across various sectors—e-commerce, hospitality, jewelry retail, and healthcare—toward building customized digital solutions. Drawing on hands-on experience developing WordPress websites, data-driven decision-support tools, and AI-powered prototypes, Abhijit transitioned into the AI/ML space to help clients leverage data for smarter outcomes.
Beyond consulting, Abhijit manages hotel Mahamaya Palace and a pharmacy network, where he has applied predictive analytics to inventory management and customer outreach. He is passionate about teaching and regularly mentors’ university students in machine learning, speaking at local tech meetups. In his spare time, he experiments with computer vision projects, contributes to open-source libraries, and writes technical blogs on AI ethics and emerging trends.
Basic Course on Artificial Intelligence and Machine Learning
- Chapter 1: Understanding Artificial Intelligence and Machine Learning
- Chapter 2: Fundamental Mathematics and Statistics for AI/ML
- Chapter 3: Data Acquisition, Cleaning, and Preprocessing
- Chapter 4: Supervised Learning Techniques
Chapter 12: Time Series Forecasting
Time series forecasting involves predicting future values based on previously observed values. It is crucial in sectors like finance, weather prediction, supply chain management, and sales forecasting.
Chapter 20: AI in Education
20.1 Personalized Learning
- Adaptive Learning Systems: Platforms like DreamBox and Knewton adjust content based on individual student performance.
- Recommendation Engines: Suggest learning materials suited to the learner’s progress.
- Pacing and Style: AI allows students to learn at their own pace and in preferred formats.
20.2 Intelligent Tutoring Systems (ITS)
- Simulate one-on-one tutoring by providing hints, feedback, and explanations.
- Examples: Carnegie Learning, ALEKS.
- Use NLP to understand student responses and adapt accordingly.
20.3 AI in Assessment and Grading
- Automated Essay Scoring: Tools like Turnitin and Gradescope use NLP and ML.
- Multiple Choice and Objective Tests: Scored instantly and accurately.
- Reduces teacher workload and provides immediate feedback.
20.4 Virtual Classrooms and Assistants
- Chatbots: Answer student queries, provide reminders, and guide through coursework.
- Virtual Teaching Assistants: Help instructors manage routine tasks.
- Examples: Georgia Tech’s Jill Watson TA (based on IBM Watson).
