End-to-end data science and machine learning project

End-to-end data science and machine learning project, available at $69.99, has an average rating of 4, with 20 lectures, 2 quizzes, based on 2 reviews, and has 28 subscribers.

You will learn about End-to-end pipeline of a data science project How to conduct data cleaning and exploratory data analysis How to train and compare different ML models How to boost and increase the performance of your models This course is ideal for individuals who are Beginner Python developers curious about data science and machine learning It is particularly useful for Beginner Python developers curious about data science and machine learning.

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Summary

Title: End-to-end data science and machine learning project

Price: $69.99

Average Rating: 4

Number of Lectures: 20

Number of Quizzes: 2

Number of Published Lectures: 20

Number of Published Quizzes: 2

Number of Curriculum Items: 22

Number of Published Curriculum Objects: 22

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

End-to-end pipeline of a data science project

How to conduct data cleaning and exploratory data analysis

How to train and compare different ML models

How to boost and increase the performance of your models

Who Should Attend

Beginner Python developers curious about data science and machine learning

Target Audiences

Beginner Python developers curious about data science and machine learning

Welcome to the course wine quality prediction! In this course you will learn how to work with data from end-to-end and create a machine learning model that predicts the quality of wines.

This data set contains records related to red and white variants of the Portuguese Vinho Verde wine. It contains information from 1599 red wine samples and 4898 white wine samples. Input variables in the data set consist of the type of wine (either red or white wine) and metrics from objective tests (e.g. acidity levels, PH values, ABV, etc.).

It is super important to notice that you will need python knowledge to be able to understand this course. You are going to develop everything using Google Colab, so there is no need to download Python or Anaconda. You also need basic knowledge of Machine Learning and data science, but don’t worry we will cover the theory and the practical needs to understand how each of the models that we are going to use work.

In our case, we will work with a classification problem (a set from the supervised learning algorithms). That means that we will use the quality as the target variable and the other variables as the inputs. In this sense, we will some examples to train our model and predict the quality of other wines.

You will learn to work with Decision Trees, Logistic Regression, how to use LazyPredict and how to tune the hyperparameters using Grid Search.

Course Curriculum

Chapter 1: Getting started

Lecture 1: Welcome

Lecture 2: Dataset information

Lecture 3: Dataset features

Lecture 4: Dataset download

Chapter 2: Data cleaning & Exploratory data analysis

Lecture 1: Data Cleaning

Lecture 2: Exploratory data analysis

Chapter 3: Modeling

Lecture 1: Outliers and IQR

Lecture 2: Dealing with outliers

Lecture 3: Theory behind the models

Lecture 4: Logistic Regression – Theory

Lecture 5: Logistic Regression

Lecture 6: Cross validation

Lecture 7: K-Nearest Neighbors – Theory

Lecture 8: Decision Tree – Theory

Lecture 9: Training other models

Lecture 10: Random Forest – Theory

Lecture 11: Random Forest

Lecture 12: Grid Search

Lecture 13: Result – How to create the barplot

Lecture 14: Final notebook

Instructors

Sara Malvar
Machine Learning Engineer

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5 stars: 1 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!