Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.
What you'll learn
- How to build algorithms
- Over fitting
- Regularization
- Model selection
Requirements
- Laptop/Desktop
- Internet Speed minimum 2 mbps
Target Audience
- Software Developer
- Machine learning
- Introduction to Machine Learning – Products, Applications, Use cases, Latest Trends
- Types of Machine Learning
- What is a ML Model?
- Introduction to Feature Engineering
- Methods for Feature Engineering
- One Hot Encoding
- Introduction to Supervised Learning
- Classification Vs Regression Algorithms
- Understanding the concept and working with real dataset
- Introduction to Un-Supervised Learning – Use-cases, Names of the Algorithms
- Classification Vs Regression Vs Clustering Algorithms
- K-Means Clustering Algorithm
- Introduction to Reinforcement Learning – Use-cases, Names of the Algorithms
- Q-Learning Algorithm -> Projects on Machine Learning
- Tips on Machine Learning
The Pre - Learning course is all about learning and implementing Python from scratch. So, no prior expertise is required in this learning period.
The lectures will be recorded and all of them will be available online at the website.
Anyone, regardless of their field of study, can apply. We do, however, expect you to have a strong interest in the domain and determination throughout the learning period.
