Artificial Intelligence and Machine Learning
in Artificial IntelligenceAbout this course
Welcome to the ultimate Machine Learning course designed for beginners and intermediate learners! Whether you’re a student, data enthusiast, or aspiring AI professional, this course will help you understand ML algorithms, data preprocessing, model building, and real-world applications.
🎯 What You Will Learn
✅ Introduction to Machine Learning – Basics, types of ML (Supervised, Unsupervised, Reinforcement Learning)
✅ Data Preprocessing & Feature Engineering – Handling missing data, encoding categorical variables, feature scaling
✅ Popular ML Algorithms Explained – Linear Regression, Decision Trees, SVM, Random Forest, K-Means Clustering, and more
✅ Model Training & Evaluation – Train-test split, cross-validation, hyperparameter tuning
✅ Real-World ML Projects – Hands-on projects using Python, Pandas, NumPy, Scikit-Learn
✅ Model Deployment & Optimization
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Overview of AI and ML concepts and real-world applications.
End of Module Task
Project: Tic-Tac-Toe AI
o Objective: Develop an AI agent that learns to play tic-tac-toe using reinforcement
learning.
o Unique Challenge: The AI must improve its gameplay over time by learning from
past mistakes.
Explanation of Supervised, Unsupervised, and Reinforcement
Learning
Guide to setting up the environment for AI & ML (libraries, IDE,
tools).
Handling missing data, encoding categorical features, scaling, and
normalizing data.
Introduction to the ML model building process: data collection,
preprocessing, model training, and evaluation.
Splitting data into training and testing sets, cross-validation.
Identifying overfitting and underfitting, using regularization
techniques.
Hands-on with Scikit-Learn for building basic models.
Implement a basic ML model for a given dataset (e.g., Titanic survival
prediction)
Overview of popular machine learning algorithms (e.g., Linear
Regression, Decision Trees)
Understanding perceptrons, activation functions, and neural
network architecture.
