Course Outline

• Introduction to Machine Learning: Overview of machine learning concepts, types of machine learning, and applications in real-world scenarios.
• Fundamentals of Python: Introduction to Python programming language, data types, control structures, and functions.
• Data Preprocessing: Techniques for cleaning, transforming, and preparing data for machine learning models, including handling missing values and outliers.
• Supervised Learning Algorithms: Dive into supervised learning algorithms such as linear regression, logistic regression, decision trees, and ensemble methods.
• Unsupervised Learning Algorithms: Explore unsupervised learning algorithms including clustering, dimensionality reduction, and anomaly detection.
• Model Evaluation and Validation: Methods for evaluating and validating machine learning models, including cross-validation, performance metrics, and overfitting/underfitting.
• Feature Engineering: Techniques for feature selection, extraction, and transformation to improve model performance.
• Neural Networks and Deep Learning: Introduction to artificial neural networks, deep learning architectures, and training neural networks using frameworks like TensorFlow and Keras.
• Model Deployment and Real-World Applications: Deployment strategies for machine learning models, building end-to-end machine learning pipelines, and integrating models into real-world applications.
• Ethical Considerations in Machine Learning: Discussion on ethical issues, bias, fairness, and transparency in machine learning algorithms and applications.