Introduction to machine learning with Python, robust models

Introduction to machine learning with Python, robust models, available at Free, has an average rating of 4.36, with 17 lectures, based on 15 reviews, and has 802 subscribers.

You will learn about Training basic machine learning model Using Kneighbors machine learning classifier How to predict on unseen data How to deal with classification problems This course is ideal for individuals who are A person who want to get started with machine learning or A data scientist eager to train machine learning models or A person want to learn about robust classification machine learning algorithm It is particularly useful for A person who want to get started with machine learning or A data scientist eager to train machine learning models or A person want to learn about robust classification machine learning algorithm.

Enroll now: Introduction to machine learning with Python, robust models

Summary

Title: Introduction to machine learning with Python, robust models

Price: Free

Average Rating: 4.36

Number of Lectures: 17

Number of Published Lectures: 17

Number of Curriculum Items: 17

Number of Published Curriculum Objects: 17

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Training basic machine learning model
Using Kneighbors machine learning classifier
How to predict on unseen data
How to deal with classification problems

Who Should Attend

A person who want to get started with machine learning
A data scientist eager to train machine learning models
A person want to learn about robust classification machine learning algorithm

Target Audiences

A person who want to get started with machine learning
A data scientist eager to train machine learning models
A person want to learn about robust classification machine learning algorithm

This course covers the basic aspects of machine learning in python. We have worked on real life examples to make things clear. We have covered one the most used machine learning algorithm, k nearest neighbor along with the famous flowers classification example.

What you will learn in this course:

Why and how machine learning.
How to use python for working with data in excel or csv format.
How to make data suitable for machine learning algorithm.
How to train a machine learning algorithm and make prediction from our model.

and much more! gear up!

It is advanced that you should practice the codes as well with us. This will create a strong base of yours in the field of data science and machine learning.

Each code is explained in this course so if you are new to python, still you will find this course helpful. You can contact us in any case as well.

The way models are trained in this course, we can train other model as well with little changes. That’s way we think that this course is good suit for you. Furthermore if you find any error in this course or any other thing that we should concern about, feel free to contact us.

When you will start the course, take it slow and steady. This course can take your 3-4 days (maybe more or maybe less) for sure. But we believe that you will learn some robust stuff here.

Good luck for your journey!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What is machine learning

Lecture 3: Fundamentals of machine learning

Chapter 2: K neighbors classifier

Lecture 1: KNN algorithm

Lecture 2: Creating first model

Lecture 3: Loading data

Lecture 4: Loading and understanding data

Lecture 5: Features and labels

Lecture 6: separating features and labels live

Lecture 7: creating models and having predictions

Lecture 8: creating knn model explanation

Lecture 9: predictions at once

Lecture 10: How our model works

Lecture 11: All in once

Chapter 3: Iris dataset

Lecture 1: Working with iris dataset

Lecture 2: Video lecture: Working with iris data

Chapter 4: What next

Lecture 1: all done for this course, what now?

Instructors

Harman Waheed
Data Scientist

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 5 votes
4 stars: 4 votes
5 stars: 6 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!

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