Basics of Machine Learning

What Will You Learn?

• Introduction to Machine Learning: Understand the fundamentals of machine learning, including its definitions, types (supervised, unsupervised, and reinforcement learning), and real-world applications.
• Data Preprocessing: Learn the techniques for preparing data for machine learning models, including data cleaning, normalization, and feature selection.
• Machine Learning Algorithms: Know more about basic machine learning algorithms such as linear regression, logistic regression, decision trees, and k-nearest neighbors, and understand their applications and limitations.
• Model Evaluation and Validation: Discover methods for evaluating and validating machine learning models, including metrics like accuracy, precision, recall, and cross-validation techniques.
• Hands-On Machine Learning Projects: Gain practical experience by applying machine learning algorithms to real-world datasets, building simple models, and interpreting results.
• Introduction to Machine Learning Tools: Get familiar with popular tools and libraries used in machine learning, such as Scikit-Learn, Pandas, and NumPy.