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Machine Learning and Data Science 2021

machine-learning-and-data-science-2021

Online Courses Udemy - Machine Learning and Data Science 2021, Learn how to deploy Machine Learning and how to be a GOOD Data Scientist. Learn the basics and the advanced concepts
  • New
  • Created by Sachin Abeywardana
  • English [Auto]
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What you'll learn

  • Data Analysis with Pandas
  • Algorithms from scratch using Numpy
  • Using Sklearn to its full effect
  • Model Deployment
  • Model Diagnostics
  • Natural Language Processing
  • Unsupervised Learning
  • Time series modelling with FB Prophet
  • Natual Language Processing with Spacy

Description

This is a course mainly about Machine Learning. If you are new to python we cover the basics of python before moving on to other topics.
We start off 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.
The focus of this course is on programming, however, we do cover the maths when it is important to do so. 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 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 and making sure our models don't overfit. Diagnosing models by splitting into training and testing as well as looking at the correct metric can make a world of difference.
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.
I hope you enjoy this course and please don't hesitate to contact me for further information.
Who this course is for:

Anyone interested in Machine Learning.

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