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Machine Learning with Scikit-Learn in 7 Hours

Machine Learning in practice with Python’s scikit-learn on real-world datasets!
3.4
3.4/5
(4 reviews)
40 students
Created by

7.6

CourseMarks Score®

6.5

Freshness

6.4

Feedback

9.4

Content

Platform: Udemy
Video: 7h 22m
Language: English
Next start: On Demand

Top Scikit-learn courses:

Detailed Analysis

CourseMarks Score®

7.6 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

6.5 / 10
This course was last updated on 2/2019.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

6.4 / 10
We analyzed factors such as the rating (3.4/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.4 / 10
Video Score: 8.7 / 10
The course includes 7h 22m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 4 hours 17 minutes of 4 Scikit-learn courses on Udemy.
Detail Score: 10.0 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
1 resources.
0 exercise.
0 test.

Table of contents

Description

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. scikit-learn has evolved as a robust library for machine learning applications in Python with support for a wide range of supervised and unsupervised learning algorithms. If you’re a data scientist or an IT professional who wants to learn machine learning using Python’s popular machine learning library scikit-learn, then go for this course.
This course teaches you how to perform your day-to-day machine learning tasks with scikit-learn. It’s a perfect blend of concepts and practical examples which makes it easy to understand and implement. You will begin with learning important machine learning algorithms that are commonly used in the field of data science such as Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Feature engineering. You will also be taken through supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning with hands-on practical projects.
Contents and Overview
This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.
In the first course, Fundamentals of Machine Learning with scikit-learn, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms you will be learning are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Feature engineering.
In the second course, Learn Machine Learning in 3 Hours, you will learn key ML algorithms and how they can be trained for classification and regression. You will also work with supervised and unsupervised learning to help to get to grips with both types of algorithms.
In the third course, Real-World Machine Learning Projects with Scikit-Learn, you will build powerful projects using scikit-learn. Using algorithms, you will learn to read trends in the market to address market demand. You’ll delve more deeply to decode buying behavior using Classification algorithms; cluster the population of a place to gain insights into using K-Means Clustering; and create a model using Support Vector Machine classifiers to predict heart disease.
By the end of this course, you will get hands-on with machine learning using powerful features of scikit-learn to implement the best machine learning practices.
Meet Your Expert(s):
We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:
•Giuseppe Bonaccorso is a Machine Learning and big data consultant with more than 12 years of experience. He has an M.Eng. in electronics engineering from the University of Catania, Italy, and further postgraduate specialization from the University of Rome, Tor Vergata, Italy, and the University of Essex, UK.During his career, he has covered different IT roles in several business contexts, including public administration, military, utilities, healthcare, diagnostics, and advertising. He has developed and managed projects using many technologies, including Java, Python, Hadoop, Spark, Theano, and TensorFlow. His main interests are in artificial intelligence, machine learning, data science, and the philosophy of the mind.

•After taking a Physics degree at Oxford, Thomas Snell entered the Biophysics industry. Performing numerical simulation; from there, took a numerical simulation PhD in Geophysics. During his PhD, Thomas developed a keen interest in Machine Learning, eventually founding two open source projects: a cryptocurrency trader and an evolutionary system to design quantum algorithms. Shortly after sharing these projects with the open source community, he worked as a Data Scientist while finishing his PhD, developing a system to cluster job data and predict career paths for groups of individuals.

•Nikola Živković is a software developer with over 7 years of experience in the industry. He earned a Master’s degree in Computer Science from the University of Novi Sad back in 2011, but by then he was already working for several companies. At the moment he is a part of the Vega IT Sourcing team from Novi Sad. During his time in the industry, he worked on large enterprise systems, small web projects, data- and time-sensitive projects, as well as on machine learning projects. Apart from that, he has experience in knowledge sharing, talking at meetups, conferences, and as a guest lecturer at the University of Novi Sad.

You will learn

✓ Predict the values of continuous variables using linear regression and K Nearest Neighbors
✓ Create ensemble models with Random-Forest and Gradient-boosting methods and see your model performance improve drastically
✓ Build a portfolio of tools and techniques that can readily be applied to your own projects
✓ Use Support Vector Machines to learn how to train your model to predict the chances of heart disease
✓ Analyze the population and generate results in line with ethnicity and other factors using K-Means Clustering
✓ Understand the buying behavior of your customers using Customer Segmentation to drive the sales of your products

Requirements

• Prior programming experience in Python is assumed.

This course is for

• This course is for IT professionals, data science enthusiasts, and students who are keen to learn machine learning skills using Python and scikit-learn. This course is also for Python developers/statisticians who want to leverage machine learning techniques with hands-on examples.

How much does the Machine Learning with Scikit-Learn in 7 Hours course cost? Is it worth it?

The course costs $14.99. And currently there is a 82% discount on the original price of the course, which was $84.99. So you save $70 if you enroll the course now.
The average price is $12.2 of 4 Scikit-learn courses on Udemy.

Does the Machine Learning with Scikit-Learn in 7 Hours course have a money back guarantee or refund policy?

YES, Machine Learning with Scikit-Learn in 7 Hours has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Machine Learning with Scikit-Learn in 7 Hours course, but there is a $70 discount from the original price ($84.99). So the current price is just $14.99.

Who is the instructor? Is Packt Publishing a SCAM or a TRUSTED instructor?

Packt Publishing has created 1,267 courses that got 65,326 reviews which are generally positive. Packt Publishing has taught 386,164 students and received a 3.9 average review out of 65,326 reviews. Depending on the information available, Packt Publishing is a TRUSTED instructor.
Tech Knowledge in Motion
Packt has been committed to developer learning since 2004. A lot has changed in software since then – but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.
With an extensive library of content – more than 4000 books and video courses -Packt’s mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what’s important to them now.
From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.

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7.6

CourseMarks Score®

6.5

Freshness

6.4

Feedback

9.4

Content

Platform: Udemy
Video: 7h 22m
Language: English
Next start: On Demand

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