Machine Learning Course - A Beginner's Guide
Machine Learning Course - Simplified
Ratings 4.00 / 5.00
What You Will Learn!
- Understanding the basics of supervised and unsupervised learning
- Python libraries like Numpy, Pandas, etc. to analyze your data efficiently
- Linear Regression, Logistic Regression, and Decision Trees for building machine learning models
- Understand how to solve Classification and Regression problems using machine learning
- How to evaluate your machine learning models using the right evaluation metrics?
- Improve and enhance your machine learning model’s accuracy through feature engineering
- Projects covered - a) Customer Churn Prediction and b) NYC Taxi Trip Duration Prediction
Description
Machine Learning is the science of teaching machines how to learn by themselves. Machine Learning is re-shaping and revolutionising the world and disrupting industries and job functions globally.
Machine learning is so extensive that you probably use it numerous times a day without even knowing it. From unlocking your mobile phones using your face to giving your attendance using a biometric machine, machine learning is being used in almost every stage.
In this age of machine learning, every aspiring data scientist is expected to up-skill themselves in machine learning techniques & tools and apply them in real-world business problems.
Machine Learning problems can be divided into 3 broad classes:
Supervised Machine Learning
Unsupervised Machine Learning
Reinforcement Learning
Supervised Machine Learning: When you have past data with outcomes (labels in machine learning terminology) and you want to predict the outcomes for the future – you would use Supervised Machine Learning algorithms. Supervised Machine Learning problems can again be divided into 2 kinds of problems:
Classification Problems: When you want to classify outcomes into different classes. For example – whether a customer would default on their loan or not is a classification problem which is of high interest to any Bank
Regression Problem: When you are interested in answering how much – these problems would fall under the Regression umbrella. For example – what is the expected amount of default from a customer is a Regression problem
Unsupervised Machine Learning: There are times when you don’t want to exactly predict an Outcome. You just want to perform a segmentation or clustering. For example – a bank would want to have a segmentation of its customers to understand their behavior. This is an Unsupervised Machine Learning problem as we are not predicting any outcomes here.
Reinforcement Learning: It is said to be the hope of true artificial intelligence. And it is rightly said so because the potential that Reinforcement Learning possesses is immense. It is a slightly complex topic as compared to traditional machine learning but an equally crucial one for the future.
Who Should Attend!
- Beginners in Data Science
