Machine Learning Data Science Masterclass in Python and R

Machine Learning & Data Science Masterclass in Python and R, available at $59.99, has an average rating of 4.2, with 204 lectures, 19 quizzes, based on 66 reviews, and has 723 subscribers.

You will learn about Create machine learning applications in Python as well as R Apply Machine Learning to own data You will learn Machine Learning clearly and concisely Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. ) No dry mathematics – everything explained vividly Use popular tools like Sklearn, and Caret You will know when to use which machine learning model This course is ideal for individuals who are Developers interested in Machine Learning It is particularly useful for Developers interested in Machine Learning.

Enroll now: Machine Learning & Data Science Masterclass in Python and R

Summary

Title: Machine Learning & Data Science Masterclass in Python and R

Price: $59.99

Average Rating: 4.2

Number of Lectures: 204

Number of Quizzes: 19

Number of Published Lectures: 204

Number of Published Quizzes: 19

Number of Curriculum Items: 223

Number of Published Curriculum Objects: 223

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Create machine learning applications in Python as well as R

Apply Machine Learning to own data

You will learn Machine Learning clearly and concisely

Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. )

No dry mathematics – everything explained vividly

Use popular tools like Sklearn, and Caret

You will know when to use which machine learning model

Who Should Attend

Developers interested in Machine Learning

Target Audiences

Developers interested in Machine Learning

This course contains over 200 lessons, quizzes, practical examples, – the easiest way if you want to learn Machine Learning.

Step by step I teach you machine learning. In each section you will learn a new topic – first the idea / intuition behind it, and then the code in both Python and R.

Machine Learning is only really fun when you evaluate real data. That’s why you analyze a lot of practical examples in this course:

Estimate the value of used cars

Write a spam filter

Diagnose breast cancer

All code examples are shown in both programming languages – so you can choose whether you want to see the course in Python, R, or in both languages!

After the course you can apply Machine Learning to your own data and make informed decisions:

You know when which models might come into question and how to compare them. You can analyze which columns are needed, whether additional data is needed, and know which data needs to be prepared in advance.

This course covers the important topics:

Regression

Classification

On all these topics you will learn about different algorithms. The ideas behind them are simply explained – not dry mathematical formulas, but vivid graphical explanations.

We use common tools (Sklearn, NLTK, caret, data.table, ), which are also used for real machine learning projects.

What do you learn?

Regression:

Linear Regression

Polynomial Regression

Classification:

Logistic Regression

Naive Bayes

Decision trees

Random Forest

You will also learn how to use Machine Learning:

Read in data and prepare it for your model

With complete practical example, explained step by step

Find the best hyper parameters for your model

“Parameter Tuning”

Compare models with each other:

How the accuracy value of a model can mislead you and what you can do about it

K-Fold Cross Validation

Coefficient of determination

My goal with this course is to offer you the ideal entry into the world of machine learning.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Why Machine Learning?

Lecture 2: Who am I? How Is The Course Structured?

Lecture 3: Udemy Reviews Update

Lecture 4: Python Or R?

Lecture 5: Download Required Materials

Lecture 6: Get the most from Tutorials.EU

Chapter 2: Setting Up The Python Environment

Lecture 1: Installing Required Tools

Lecture 2: Crash Course: Our Jupyter-Environment

Lecture 3: How To Find The Right File In The Course Materials

Chapter 3: Setting Up The R Environment

Lecture 1: Installing R And RStudio

Lecture 2: Crash Course: R and RStudio

Lecture 3: How To Find The Right File In The Course Materials

Lecture 4: Note About The Next Lectures

Lecture 5: Intro: Vectores in R

Lecture 6: Intro: data.table In R

Chapter 4: Basics Machine-Learning

Lecture 1: Whats A Model?

Lecture 2: Which Problems Is Machine Learning Used For

Chapter 5: Linear Regression

Lecture 1: Intuiton: Linear Regression (Part 1)

Lecture 2: Intuition: Linear Regression (Part 2)

Lecture 3: Intuition Comprehend With Geogebra

Lecture 4: Python: Read Data And Draw Graphic

Lecture 5: Note: Excel

Lecture 6: Python: Linear Regression (Part 1)

Lecture 7: Python: Linear Regression (Part 2)

Lecture 8: R: Linear Regression (Part 1)

Lecture 9: R: Linear Regression (Part 2)

Lecture 10: R: Linear Regression (Part 3)

Lecture 11: R: Linear Regression (Part 4)

Lecture 12: Excursus (optional): Why Do We Use The Quadratic Error?

Chapter 6: Project: Linear Regression

Lecture 1: Intro: Project Linear Regression (Used Car Sales)

Lecture 2: Python: Sample Solution

Lecture 3: R: Sample Solution

Chapter 7: Train/Test

Lecture 1: Intuition: Train / Test

Lecture 2: Python: Train / Test (Part 1)

Lecture 3: Python: Train / Test (Part 2)

Lecture 4: Python: Train / Test – Challenge

Lecture 5: R: Train / Test (Part 1)

Lecture 6: R: Train / Test (Part 2)

Lecture 7: R: Train / Test – Challenge

Chapter 8: Linear Regression With Multiple Variables

Lecture 1: Intuition: Linear regression with multiple variables (Part 1)

Lecture 2: Intuition: Linear regression with multiple variables (Part 2)

Lecture 3: Python: Linear regression with multiple variables (Part 1)

Lecture 4: Python: Linear regression with multiple variables (Part 2)

Lecture 5: R: Linear regression with multiple variables (Part 1 + 2)

Chapter 9: Compare models: coefficient of determination

Lecture 1: Intuition: R2 – The coefficient of determination (Part 1)

Lecture 2: Intuition: R2 – The coefficient of determination (Part 2)

Lecture 3: Python: Calculate R2

Lecture 4: Python: Compare models by R2

Lecture 5: R: Calculate R2

Lecture 6: R: Compare models by R2

Chapter 10: Practical project: Coefficient of Determination

Lecture 1: Introduction: Practical project: coefficient of determination

Lecture 2: Note: Where can you find the project?

Lecture 3: Python, practical project: Calculate coefficient of determination

Lecture 4: R, Praxisprojekt: Bestimmtheitsma? berechnen

Chapter 11: Concept: Types of data and how to process them

Lecture 1: Intuition: Data Types (Part 1) – What Types Are There?

Lecture 2: Intuition: Data Types (Part 2) – Metric & Nominal Data

Lecture 3: Intuition: Data Types (Part 3) – Ordinal Data

Lecture 4: Python: Processing Nominal Data (Part 1, Preparing Data)

Lecture 5: Python: Processing Nominal Data (Part 2)

Lecture 6: R: Process nominal data (Part 1 + 2)

Lecture 7: Optional excursus: Why were we allowed to remove a column?

Chapter 12: Polynomial Regression

Lecture 1: Intuition: Polynomial Regression (Part 1)

Lecture 2: Intuition: Polynomial Regression (Part 2)

Lecture 3: Python: Polynomial Regression (Part 1)

Lecture 4: Python: Polynomial Regression (Part 2)

Lecture 5: R: Polynomial Regression (Part 1)

Lecture 6: R: Polynomial Regression (Part 1)

Chapter 13: Practice Project: Polynomial Regression

Lecture 1: Presentation: Practice Project Polynomial Regression

Lecture 2: Python: Sample Solution: Project Polynomial Regression

Lecture 3: R: Sample Solution: Project Polynomial Regression

Chapter 14: Excursus R: Vectorize calculations in R (matrices, )

Lecture 1: R: Vectors and matrices

Lecture 2: R: Access elements in vectors

Lecture 3: R: Naming of elements

Lecture 4: R: Matrices

Lecture 5: R: Name matrices

Lecture 6: R: DataTables

Chapter 15: Excursus Python: Vectorize Calculations (Numpy)

Lecture 1: Excursus Python: Why Numpy? (Part 1)

Lecture 2: Excursus Python: Why Numpy? (Part 2)

Lecture 3: Excursus Python: Numpy (Arrays)

Instructors

Denis Panjuta
Teaches over 400,000 students to code

Rating Distribution

1 stars: 4 votes

2 stars: 0 votes

3 stars: 7 votes

4 stars: 26 votes

5 stars: 29 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!