🚀 Machine Learning

🧠 Machine Learning Mastery – From Basics to Real-World Applications

This course provides a complete journey into the world of Machine Learning (ML)—starting from foundational theory and progressing to hands-on projects that solve real-world problems using powerful tools like Python, Scikit-learn, Pandas, NumPy, and TensorFlow.

📚 What You’ll Learn:

• 🔍 Foundations of ML
  Understand supervised, unsupervised, and reinforcement learning. Learn key algorithms including linear & logistic regression, decision trees, k-NN, SVM, and clustering techniques.

• 📊 Data Preprocessing & Feature Engineering
  Learn how to clean, visualize, and transform data for optimal model performance. Work with real-world datasets using Pandas, Matplotlib, and Seaborn.

• 🤖 Model Building & Evaluation
  Build machine learning models using Scikit-learn and TensorFlow, tune hyperparameters, and use metrics like accuracy, precision, recall, and F1-score to evaluate them.

• 🧠 Introduction to Deep Learning
  Get started with neural networks, backpropagation, and frameworks like Keras. Build simple deep learning models for image and text data.

• 💡 Capstone Projects
  Work on mini-projects such as:

  • Predicting housing prices

  • Email spam classification

  • Image recognition using CNNs

  • Recommendation systems

• 🔐 Ethics, Bias, and ML in Society
  Understand the importance of ethics, fairness, and bias in machine learning applications, along with the societal impact of automation and AI.

🎓 Ideal for:

• Beginners with basic programming and math skills

• Engineers, analysts, and professionals looking to upskill in AI/ML

• Students aiming for careers in Data Science, AI, or Research