Step into the world of Artificial Intelligence and Machine Learning with a practical, beginner-friendly course designed to take you from core concepts to hands-on implementation.
Instructor Ayan Hasnain
Overview
Artificial Intelligence and Machine Learning are transforming the way businesses, technology, and everyday applications work. AI & Machine Learning Mastery is a practical, structured course designed to help learners understand AI/ML concepts and apply them through hands-on exercises and projects.
The course starts with the fundamentals of Artificial Intelligence, Python, mathematics, and data handling before progressing to Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Generative AI, and real-world model development.
Rather than focusing only on theory, learners will work with datasets, train models, evaluate their performance, and build practical AI/ML projects.
* Beginner-friendly learning path
* AI & Machine Learning fundamentals
* Python for AI/ML
* Data analysis and visualization
* Supervised & Unsupervised Learning
* Deep Learning & Neural Networks
* Natural Language Processing (NLP)
* Computer Vision fundamentals
* Introduction to Generative AI & LLMs
* Hands-on exercises and practical projects
* Model evaluation and optimization
* Real-world use cases
* Portfolio-ready capstone project
Topics Covered:
* What is Artificial Intelligence?
* History and evolution of AI
* AI vs Machine Learning vs Deep Learning
* Types of Artificial Intelligence
* How modern AI systems work
* Real-world applications of AI
* AI opportunities and limitations
* Responsible and ethical AI
* Career opportunities in AI/ML
Outcome: Understand the AI ecosystem and how AI, ML, and Deep Learning are connected.
Topics Covered:
* Python fundamentals
* Variables and data types
* Operators
* Conditional statements
* Loops
* Functions
* Lists, tuples, sets, and dictionaries
* Working with files
* Exception handling
* Introduction to Jupyter Notebook
* Python libraries for AI/ML
Key Libraries: NumPy • Pandas • Matplotlib • Scikit-learn
Practical: Write Python programs and perform basic data manipulation.
Topics Covered:
* Mathematics required for ML
* Scalars, vectors, and matrices
* Basic Linear Algebra
* Mean, median, and mode
* Variance and standard deviation
* Probability fundamentals
* Probability distributions
* Correlation and covariance
* Basic calculus concepts
* Gradient and optimization intuition
Outcome: Build the mathematical foundation needed to understand ML algorithms.
Topics Covered:
* Understanding datasets
* Importing datasets
* DataFrames with Pandas
* Data cleaning
* Handling missing values
* Removing duplicates
* Detecting outliers
* Data transformation
* Exploratory Data Analysis (EDA)
* Data visualization
* Identifying patterns and relationships
Tools: Pandas • NumPy • Matplotlib
Practical Project: Perform complete exploratory data analysis on a real-world dataset.
Topics Covered:
* Preparing data for Machine Learning
* Categorical data encoding
* Label Encoding
* One-Hot Encoding
* Feature scaling
* Normalization
* Standardization
* Feature selection
* Feature engineering
* Train-test splitting
* Data leakage
* Building preprocessing pipelines
Outcome: Transform raw data into ML-ready datasets.
Topics Covered:
* What is Machine Learning?
* How machines learn from data
* ML workflow
* Features and target variables
* Training and testing data
* Supervised Learning
* Unsupervised Learning
* Reinforcement Learning overview
* Overfitting and underfitting
* Bias and variance
* Generalization
* Simple Linear Regression
* Multiple Linear Regression
* Polynomial Regression
* Decision Tree Regression
* Random Forest Regression
* Logistic Regression
* K-Nearest Neighbors
* Decision Trees
* Random Forest
* Naive Bayes
* Support Vector Machines
* Accuracy
* Precision
* Recall
* F1 Score
* Confusion Matrix
* ROC-AUC
* MAE
* MSE
* RMSE
* R² Score
Practical Projects:
* House Price Prediction
* Customer Churn Prediction
Topics Covered:
* Introduction to Unsupervised Learning
* Clustering
* K-Means Clustering
* Hierarchical Clustering
* DBSCAN overview
* Dimensionality Reduction
* Principal Component Analysis (PCA)
* Understanding clusters
* Business applications of clustering
Practical Project: Customer Segmentation using Machine Learning.
Topics Covered:
* Cross-validation
* Hyperparameter tuning
* Grid Search
* Randomized Search
* Feature importance
* Regularization
* Building ML pipelines
* Comparing multiple algorithms
* Selecting the best-performing model
* Avoiding common ML mistakes
Outcome: Learn how to improve model performance systematically.
Topics Covered:
* Introduction to Deep Learning
* Biological vs Artificial Neurons
* Artificial Neural Networks
* Input, hidden, and output layers
* Activation functions
* Forward propagation
* Loss functions
* Backpropagation
* Gradient Descent
* Training Neural Networks
* Introduction to TensorFlow/Keras
* Convolutional Neural Networks
* Sequence models overview
Practical: Build and train a basic neural network.
Topics Covered:
* Introduction to NLP
* Text preprocessing
* Tokenization
* Stop-word removal
* Stemming
* Lemmatization
* Bag of Words
* TF-IDF
* Word embeddings overview
* Text classification
* Sentiment analysis
* Introduction to Transformers
Practical Project: Build a Sentiment Analysis application.
Topics Covered:
* Introduction to Computer Vision
* How computers understand images
* Image representation
* Image preprocessing
* Convolutional Neural Networks
* Image Classification
* Object Detection overview
* Transfer Learning
* Real-world Computer Vision applications
Practical Project: Build an Image Classification model.
Topics Covered:
* What is Generative AI?
* Generative AI vs Traditional AI
* Introduction to Large Language Models
* How LLMs work
* Tokens and context
* Transformers and Attention
* Prompt Engineering fundamentals
* Zero-shot and Few-shot prompting
* Embeddings
* Vector databases overview
* Retrieval-Augmented Generation (RAG)
* AI Agents overview
* Responsible use of Generative AI
Practical: Build a simple LLM-powered application.
Topics Covered:
* Saving and loading trained models
* Turning an ML model into an application
* Introduction to APIs
* Introduction to Streamlit
* Creating an ML web application
* Model deployment concepts
* Cloud deployment overview
* Model monitoring
* Updating models with new data
Practical: Deploy a trained ML model as an interactive application.
Topics Covered:
* Responsible AI principles
* Bias in AI systems
* Fairness
* Transparency
* Explainability
* Data privacy
* AI security considerations
* Hallucinations and model limitations
* Human oversight
* Ethical considerations when developing AI applications
Learners will apply their knowledge to build an end-to-end AI/ML project.
1. Define a real-world problem
2. Collect or select a dataset
3. Explore and clean the data
4. Perform feature engineering
5. Select suitable algorithms
6. Train multiple models
7. Evaluate model performance
8. Optimize the best model
9. Build a simple user interface
10. Deploy and present the final solution
* Customer Churn Prediction
* Fraud Detection System
* Sales Forecasting
* Loan Approval Prediction
* Recommendation System
* Sentiment Analysis
* Image Classification
* AI Document Assistant
* FAQ/Knowledge-Base Chatbot
Programming: Python
Data: NumPy • Pandas
Visualization: Matplotlib
Machine Learning: Scikit-learn
Deep Learning: TensorFlow / Keras
Development: Jupyter Notebook • Google Colab
Deployment: Streamlit
Generative AI: LLM APIs • Embeddings • RAG concepts
Version Control: Git & GitHub fundamentals
By completing the course, learners should be able to:
* Understand core AI and Machine Learning concepts
* Write Python code for AI/ML applications
* Clean, analyze, and visualize datasets
* Prepare data for Machine Learning
* Build regression and classification models
* Apply clustering techniques
* Evaluate and optimize ML models
* Understand and build basic Neural Networks
* Work with NLP and Computer Vision concepts
* Understand Generative AI and LLM fundamentals
* Build basic AI-powered applications
* Deploy simple ML applications
* Complete end-to-end AI/ML projects
* Create projects for an AI/ML portfolio
This course is suitable for:
* Students and fresh graduates
* Beginners interested in Artificial Intelligence
* Developers looking to enter AI/ML
* Data and technology enthusiasts
* Working professionals looking to upskill
* Entrepreneurs interested in AI
* Anyone who wants practical exposure to modern AI technologies
No previous AI or Machine Learning experience is required.
Basic computer knowledge is recommended. Programming, mathematics, data analysis, and Machine Learning concepts are introduced progressively throughout the course.
Learn → Practice → Build → Evaluate → Deploy
Each major section combines conceptual understanding with practical implementation so learners can move beyond simply knowing AI terminology and start building working solutions.
Master the foundations. Work with real data. Train intelligent models. Explore modern Generative AI. Build projects that demonstrate what you can do.
AI & Machine Learning Mastery
Curriculum
1 chapters · 1 lessons · Enroll to unlock
Introduction to Artificial Intelligence
In this chapter, you’ll explore what Artificial Intelligence (AI) really means, how it has evolved, and how machines can perform tasks that typically require human intelligence. You’ll also understand the relationship between AI, Machine Learning, and Deep Learning, along with their practical applications in the real world.
FAQs