Machine Learning

This course provides a deep dive into machine learning (ML), enabling students to create systems that learn from data and improve over time. It covers supervised and unsupervised learning, algorithms like regression, classification, clustering, and neural networks. Students will implement ML models using Python and libraries such as Scikit-learn, Pandas, and TensorFlow. Through hands-on projects, learners gain experience in data preprocessing, model training, evaluation, and real-world applications of ML in sectors like finance, healthcare, and marketing. Key Takeaways: - Understand the principles and types of machine learning. - Work with real-world data to train and test ML models. - Use Python libraries like Scikit-learn and TensorFlow effectively. - Analyze model performance and avoid overfitting or bias. - Apply ML techniques in practical domains like finance and healthcare.