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  • Author: mitsumi
  • Date: 17-06-2021, 14:01
17-06-2021, 14:01

Introduction to Machine Learning with Scikit-Learn

Category: Tutorials

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Introduction  to Machine Learning with Scikit-Learn
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 48.0 KHz
Language: English | Size: 1.21 GB | Duration: 2h 32m
Learn the three main techniques of machine learning: regression, classification and clustering, using Scikit-Learn


What you'll learn
In this course you will learn: Machine Learning and Scikit-Learn
You will be able to recognize problems that can be solved with Machine Learning
Select the right technique (is it a classification problem? a regression? needs preprocessing?)
Train and evaluate regression models with Scikit-Learn to forecast numerical quantities.
Train and evaluate classification models with Scikit-Learn to predict categories.
Use clustering techniques to group your data and discover insights.

Description
This course introduces machine learning covering the three main techniques used in industry: regression, classification, and clustering.

It is designed to be self-contained, easy to approach, and fast to assimilate.

You will learn:

What machine learning is

Where machine learning is used in industry

How to recognize the technique you should use

How to solve regression problems to predict numerical quantities

How to solve classification problems to predict categorical quantities

How to use clustering to group your data and discover new insights

The course is designed to maximize the learning experience for everyone and includes 50% theory and 50% hands-on practice. It includes labs with hands-on exercises and solutions.

No software installation required. You can run the code on Google CoLab and get started right away.

This course is the fastest way to get up to speed in machine learning and Scikit Learn.

Why Machine Learning?

Machine Learning has taken the world by a storm in the last 10 years, revolutionizing every company and empowering many applications we use every day.

Here are some examples of where you can find machine learning today: recommender systems, image recognition, sentiment analysis, price prediction, machine translation, and many more!

There are over 3000 job announcements requiring Scikit Learn in the United States alone, and almost 80000 jobs mentioning machine learning in the US. Machine Learning engineers can easily earn six figure salaries in major cities, and companies are investing Billions of dollars in developing their teams.

Even if you already have a job, understanding how machine learning works will empower you to start new projects and get visibility in your company.

Why Scikit Learn?

It's the best Python library to learn machine learning

Simple, yet powerful API for predictive data analysis

Used in many industries: tech, biology, finance, insurance

Built on standard libraries such as NumPy, SciPy, and MatDescriptionlib

Who this course is for:
Python enthusiasts that want to deepen their knowledge of machine learning
Software engineers looking to add machine learning skills to their toolbelt
College students looking for hands-on practice in machine learning
Data Analysts looking to expand their skills into machine learning

Screenshots

Introduction  to Machine Learning with Scikit-Learn


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