What you will learn?
Fundamentals of machine learning algorithms, including linear regression, decision trees, and clustering methods
Techniques for data preprocessing, feature selection, and dimensionality reduction
Model evaluation strategies, including cross-validation, confusion matrices, and performance metrics
Implementation of machine learning models using Python and libraries such as scikit-learn, TensorFlow, and Keras
Strategies for hyperparameter tuning and model optimization to improve performance
Practical applications and case studies to solve real-world problems using machine learning
About this course
The "Machine Learning" course provides an in-depth exploration of the foundational techniques and applications in the field of machine learning. Students will gain practical experience with algorithms for supervised and unsupervised learning, model evaluation, and optimization. The course combines theoretical concepts with hands-on projects using popular tools and frameworks, enabling participants to build and deploy machine learning models for real-world problems. By integrating lectures, coding exercises, and case studies, this course aims to equip learners with the skills necessary to leverage machine learning in diverse domains.
P.S
This course is by DeepLearningAI and is offered FREE on YouTube.
Requirements
Basic understanding of programming concepts and proficiency in Python is required
Familiarity with fundamental mathematical concepts, including algebra and statistics, is beneficial
No prior machine learning experience is necessary, though a willingness to engage in hands-on coding and project work is expected
Access to a computer with internet connectivity for course materials, coding exercises, and online resources
