Machine Learning & Artificial Intelligence

🔍 Course Overview:

This comprehensive course is designed to take you from beginner to expert in the fields of Machine Learning (ML) and Artificial Intelligence (AI). You’ll start with core concepts and progress to real-world applications using Python. Ideal for students, developers, and professionals looking to build a strong foundation and advance their careers in AI/ML.

---

🎯 What You Will Learn:

• Core concepts of AI and ML

• Difference between AI, ML, and Deep Learning

• Supervised, Unsupervised, and Reinforcement Learning

• Data preprocessing and feature engineering

• Model building with Scikit-learn and TensorFlow

• Neural Networks and Deep Learning

• NLP (Natural Language Processing)

• Computer Vision basics

• Model evaluation and optimization

• Real-world project implementation

---

📚 Course Modules:

Module 1: Introduction to AI & ML

• What is Artificial Intelligence?

• History and evolution of AI

• Applications of AI & ML in the real world

Module 2: Python for ML

• Python basics

• Numpy, Pandas, and Matplotlib

• Exploratory Data Analysis

Module 3: Supervised Learning

• Linear & Logistic Regression

• Decision Trees, Random Forest

• Support Vector Machines (SVM)

• K-Nearest Neighbors (KNN)

Module 4: Unsupervised Learning

• Clustering (K-Means, Hierarchical)

• Dimensionality Reduction (PCA)

Module 5: Neural Networks & Deep Learning

• Introduction to Neural Networks

• Activation Functions, Forward & Backpropagation

• Deep Learning with TensorFlow/Keras

Module 6: Natural Language Processing (NLP)

• Text preprocessing

• Sentiment analysis

• Chatbot basics

Module 7: Computer Vision

• Image classification

• CNNs (Convolutional Neural Networks)

Module 8: Model Optimization & Deployment

• Model evaluation metrics

• Hyperparameter tuning

• Model deployment using Flask/Streamlit

Module 9: Capstone Projects

• AI-based Chatbot

• Image classification app

• Predictive analysis (house price, stock market, etc.)

---

✅ Course Objectives:

• Understand AI & ML concepts from scratch

• Build and train models using real-world data

• Apply algorithms to solve real problems

• Deploy models in web applications

---

🛠️ Tools & Technologies:

• Python, Scikit-learn, TensorFlow, Keras

• Pandas, NumPy, Matplotlib, Seaborn

• Jupyter Notebook, Google Colab

• Flask, Streamlit

---

🧑‍💻 Who Should Enroll:

• Beginners in Data Science/AI/ML

• Software Developers

• Engineering/IT Students

• Anyone interested in AI career

---

📋 Prerequisites:

• Basic understanding of programming (preferably Python)

• Logical reasoning and curiosity to learn

---

📜 Certificate:

Get a certificate of completion after finishing the course and submitting the final project.

---