Machine Learning Intermediate
This course provides a comprehensive guide to fundamental and advanced topics in Machine Learning (ML). It is tailored for learners who wish to grasp the essentials of machine learning concepts, data preprocessing techniques, model training, neural networks, and practical applications, thus enabling them to apply ML techniques effectively in real-world scenarios. By the end of this course, learners should be able to understand key ML algorithms, handle data using preprocessing techniques, build predictive models, and undertake projects demonstrating learned skills.
Fundamentals of Machine Learning
Data Preprocessing Techniques
Building and Training Models
Model Validation Techniques
Practical Applications and Case Studies
Introduction to Neural Networks
Tooling and Environment Setup
Version Control and Collaboration
Creating a Machine Learning Portfolio
Common Machine Learning Algorithms
Knowledge of common ML algorithms is essential for applying the right methods to a given data problem. This knowledge aids in understanding algorithm capabilities, limitations, and appropriate applications.
All Machine Learning algorithms explained in 17 min
Machine Learning Algorithm | Types Of Machine Learning | What Is An Algorithm | Simplilearn