Dive into the world of modern machine learning with our comprehensive Specialization! Explore supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and Silicon Valley's best practices for AI and ML innovation. 🚀
By the end of this Specialization, you'll master key concepts and gain practical skills to tackle real-world challenges with machine learning. Whether you're breaking into AI or advancing your ML career, this Specialization is your ideal starting point. Applied Learning Project
Upon completion, you'll be equipped to:
• 🐍 Build ML models in Python using NumPy and scikit-learn.
• 📊 Develop and train supervised ML models for prediction and binary classification tasks, including linear and logistic regression.
• 🧠 Construct and train a neural network with TensorFlow for multi-class classification.
• 🛠️ Implement best practices for ML development to ensure model generalization in real-world scenarios.
• 🌳 Utilize decision trees and tree ensemble methods like random forests and boosted trees.
• 🤖 Explore unsupervised learning techniques such as clustering and anomaly detection.
• 💡 Create recommender systems using collaborative filtering and content-based deep learning.
• 🎮 Build a deep reinforcement learning model.
Get ready to level up your machine learning skills and tackle the most challenging problems With ME! 💪