Machine Learning, Deep Learning + AWS Sagemaker training
Blue Chip Training and Consulting
Skills for the next step in your career
What you'll learn
Course content
Introduction
Basic python + Pandas + Plotting
Machine Learning: Numpy + Scikit Learn
Machine Learning: Classification + Time Series + Model Diagnostics
Unsupervised Learning
Requirements
领英推荐
Description
This is a course on Machine Learning, Deep Learning (Tensorflow + PyTorch) and Bayesian Learning (yes all 3 topics in one place!!!). Yes BOTH Pytorch and Tensorflow for Deep Learning.
We learn how to deploy models in AWS Sagemaker. Along the way we heavily use boto3 to spin up sagemaker instances.
We start off the course by analysing data using pandas, and implementing some algorithms from scratch using Numpy. These algorithms include linear regression, Classification and Regression Trees (CART), Random Forest and Gradient Boosted Trees.
We start off using TensorFlow for our Deep Learning lessons. This will include Feed Forward Networks, Convolutional Neural Nets (CNNs) and Recurrent Neural Nets (RNNs). For the more advanced Deep Learning lessons we use PyTorch with PyTorch Lightning.
We focus on both the programming and the mathematical/ statistical aspect of this course. This is to ensure that you are ready for those theoretical questions at interviews, while being able to put Machine Learning into solid practice.
Some of the other key areas in Machine Learning that we discuss include, unsupervised learning, time series analysis and Natural Language Processing. Scikit-learn is an essential tool that we use throughout the entire course.
We spend quite a bit of time on feature engineering
I would like to highlight that we talk about Machine Learning Deployment, since this is a topic that is rarely talked about. The key to being a good data scientist is having a model that doesn't decay in production.
contact us
email - [email protected]