Machine Learning is so hot right now.
Today’s modern machine learning libraries have thoughtful APIs that allow anyone who is comfortable with code to dive into ML
What this course is about:
Machine Learning is so hot right now. This machine learning class, taught in Python, provides a quick introduction to all of the classic machine learning techniques.
If you want to learn how to deal with data, these techniques should be the core of your bag of tricks. This course will cover both theory and implementation of these critical machine learning techniques in Python.
Who this program is for:
This course is for people who 1) want to get a sense of what Deep Learning is all about and 2) who’d like to use the high-level libraries Keras and TensorFlow to write code that implement the basics of Deep Learning on their own laptops or in the cloud via Jupyter Notebooks.
Sean Reed. Sean is a developer with both front-end and back-end application development skills. He started his career tackling tough software coding problems as a physicist and derivatives trader and moved on to Python when the opportunity arose in the last few years. Sean Reed teaches courses and gives talks related to Natural Language Processing, Neural Networks, and other machine learning topics because he thinks it is great to figure out how to apply new techniques to solve problems using data. He has a B.S. in Physics from Fordham and an M.A. in Economics from New York University.
You will learn:
- Regression: Linear Regression, Polynomial Regression, Backward Elimination of Regressors
- Classification: Naive Bayes, Logistic Regression, Support Vector Machines
- Resampling, Bootstrapping, and Cross Validation
- Regularization: Lasso and Ridge Regression
- Dimension Reduction Trees: Decision Trees, Bagging, Boosting, Random Forest
- Unsupervised Learning: K-Means Clustering,
- Neural Networks: Intro To Artificial Neural Networks and Deep Learning
Prerequisites & Preparation:
- Python Programming 101