Learn the core ideas in machine learning, and build your first models.
Note: In This Course, In Task 1 , there is no exercise, there is basic introductory material for Machine learning
The "Intro to Machine Learning" course on Kaggle is designed to help you learn the fundamental concepts and techniques of machine learning, as well as to build your first models. Here's a breakdown of what you will learn from the course:
How Models Work
Introduction to the basic concepts of machine learning models. Understanding how models make predictions. Basic Data Exploration
Techniques to load and understand your dataset. Initial data exploration and analysis. Your First Machine Learning Model
Building your first machine learning model. Practical steps to implement a simple model. Model Validation
Methods to measure and validate the performance of your model.
Comparing different models based on performance metrics.
Underfitting and Overfitting
Concepts of underfitting and overfitting in machine learning.
Techniques to fine-tune your model for optimal performance.
Random Forests
Introduction to Random Forests, a more advanced machine learning algorithm.
Implementation and advantages of using Random Forests.
Machine Learning Competitions
Insight into machine learning competitions.
Strategies to improve and track your progress in competitions.
Preparation and Prerequisites:
Basic knowledge of Python is required to follow along with the course exercises.
Next Steps After the Course:
The course prepares you for more advanced topics such as Machine Learning Explainability, Intermediate Machine Learning, and Intro to Deep Learning. Course Structure:
The course includes tutorials and exercises for practical implementation. It is estimated to take around 3 hours to complete, and you will receive a certificate upon completion. Instructor:
The course is taught by Dan Becker. By completing this course, you will gain a solid foundation in machine learning, enabling you to build and validate models, understand data, and participate in Kaggle competitions effectively. This knowledge can also serve as a basis for publishing research papers if you achieve novel results during your participation in competitions.
Certificate Link: https://github.com/NiharJani2002/Kaggle-Intro-To-Machine-Learning/blob/master/Nihar%20Jani%20-%20Intro%20to%20Machine%20Learning.png
