🧠 Introduction to Machine Learning & Deep Learning: From Data to Intelligence

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🧾 Course Description

Machine Learning (ML) and Deep Learning (DL) are the engines powering today’s intelligent systems — from recommendation engines and fraud detection to self-driving cars and chatbots. In “Introduction to Machine Learning & Deep Learning”, you’ll learn the mathematical intuition, practical tools, and coding skills to build and deploy your first models.

This beginner-friendly course covers supervised and unsupervised learning, neural networks, model evaluation, and hands-on implementation using Python, Scikit-learn, TensorFlow, and Keras. Whether you’re a developer, data enthusiast, or aspiring AI engineer, this course builds the right foundation for your ML journey.

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✅ Key Benefits

• 🧠 Build Strong Foundations — Learn core concepts like regression, classification, loss functions, and overfitting
• 🧰 Hands-On Implementation — Code real ML/DL models using Scikit-learn and TensorFlow/Keras
• 🔄 Understand Model Lifecycle — Train, evaluate, optimize, and deploy ML models
• 🎯 Clarity over Complexity — Visual explanations of neural networks and backpropagation
• 🚀 Career Gateway — Prepares you for ML specialization, GenAI, or MLOps tracks

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🎯 Pre-requisites

• Proficiency with Python (data types, functions, loops, libraries like NumPy/Pandas)
• Basic understanding of linear algebra, statistics, and probability (conceptual level)
• No prior experience in ML or DL required

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📚 Curriculum Breakdown

📘 Module 1: Introduction to Machine Learning

• What is ML? Types of ML: supervised, unsupervised, reinforcement
• Use cases: NLP, vision, recommendation, healthcare
• ML workflow: data → model → prediction → evaluation

📊 Module 2: Data Preprocessing & Exploration

• Cleaning and transforming data
• Feature engineering and normalization
• Data splitting: train, test, validation

🔢 Module 3: Supervised Learning Algorithms

• Linear regression, logistic regression
• Decision trees, random forest, SVM
• Model metrics: accuracy, precision, recall, F1-score

🧩 Module 4: Unsupervised Learning

• Clustering: K-Means, Hierarchical
• Dimensionality reduction: PCA
• Real-life use cases: customer segmentation, anomaly detection

🧠 Module 5: Introduction to Deep Learning

• What is a neural network? Perceptron to MLP
• Layers, weights, activation functions
• Forward pass and backpropagation (intuitive explanation)

⚙️ Module 6: Building Neural Networks with TensorFlow/Keras

• Model architecture and compiling
• Training and evaluating a model
• Avoiding overfitting: dropout, early stopping, batch size

🧪 Module 7: Real-World Projects

Choose one:

• Predict house prices (regression)
• Classify images (MNIST / CIFAR-10)
• Detect spam messages (NLP)

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⏱️ Estimated Duration

Daily Study Time	Estimated Duration	Ideal For
2 hours/day	16–18 days (~3 weeks)	Balanced pace with practice
4 hours/day	8–9 days (~1.5 weeks)	For committed learners
6 hours/day	5–6 days (bootcamp)	Immersive and project-intensive

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🎓 Outcome

By the end of Introduction to Machine Learning & Deep Learning, you will:

• Understand the mathematics and workflow behind ML/DL models
• Build, train, and evaluate your own models using Scikit-learn and Keras
• Apply ML/DL to real-world problems in vision, text, and prediction
• Be ready to advance into deep learning, MLOps, LLMs, or AI product development