Machine Learning: Intro for Non-Coders
Duration: 3 Hours
Introduction to Machine Learning:
(for non-coders or someone who are new to the concept)
Machine Learning has become an integral part of many commercial applications and research projects. This course will teach practical ways to build your machine learning solutions. Machine learning is a form of data analysis that gives computers the ability to learn and process information with little human intervention. Because it can be used in numerous fields, it is a promising new technology with tens of thousands of current job openings.
Machine learning is about extracting knowledge from data. It is a research field at the intersection of statistics, artificial intelligence, and computer science and is also known as predictive analytics or statistical learning. The application of machine learning methods has in recent years, become ubiquitous in everyday life. From automatic recommendations of which movies to watch, to what food to order or which products to buy, to personalized online radio and recognizing your friends in your photos, many modern websites and devices have machine learning algorithms at their core. When you look at a complex website like Facebook, Amazon, or Netflix, it is very likely that every part of the site contains multiple machine learning models.
This course will cover the following topics;
- Welcome to Machine Learning
- Introduction to Supervised Learning and Unsupervised Learning
- Data Preprocessing and Feature Engineering. Imputation of Missing Values and Feature Selection and Outliers.
- Regression Models
- Introduction to Clustering, Classification and Neural Networks
- Model Evaluation and Improvement Techniques.
- What is Natural Language Processing
What to Expect:
After taking this course, students should know methods used in machine learning and have hands-on experience in solving various business problems using R and Python. In this course students will know the methods and tools widely applied to the field of machine learning: linear models for regression and classification, clustering methods, working with text data, neural networks, reinforcement learning, and other advanced topics. Students will use different business data sets with R and Python.