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Machine Learning A-Z™

Hands-On Python & R In Data Science

Interested in the field of Machine Learning? Then this course is for you.

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:

  • Part 1 - Data Preprocessing
  • Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
  • Part 3 - Classification: Logistic Regression, K- So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.

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Udemy

Rating 4.3 based on 30,773 ratings
Length 41.5 hours
Starts On Demand (Start anytime)
Cost $10
From Udemy
Instructors Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, SuperDataScience Support
Download Videos Only via the Udemy mobile app
Language English
Subjects Business Data Science
Tags Business Data & Analytics

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What people are saying

We analyzed reviews for this course to surface learners' thoughts about it

enjoy machine learning in 72 reviews

Very well structured and real world use cases.. nice tutorial... ... "until then enjoy Machine Learning" I will miss this sentence most!!

Thank you very much, see you on the next course and until then "ENJOY MACHINE LEARNING, Happy analyzing! "

Enjoy Machine Learning :) I think, those who want to do data science or machine learning Job, this course will give you clear picture in terms of Thorey and practice.

Enjoy Machine Learning!

:) templates are readily applicable to your business problems.. :D Enjoy machine learning!

And enjoy Machine Learning!

Until then: enjoy Machine Learning!

- Constantly saying 'Enjoy Machine Learning' does not make the experience any different.

natural language processing in 23 reviews

I would love to see more on natural language processing (especially how it is used to solve complex problems) and dimensionality reduction (GDA, for example).

But in some cases even the intuition part is missing like for SVR (Support Vector Regression) or NLP (Natural Language Processing) where the instructor jumps directly to coding.

Thank you, The instructor walked me through the basics of the natural language processing and the best part that I like the most is the way he explained the whole working of the procedure, giving a good idea of how the whole process is going to work from starting till the end which helped me get a clear idea to apply this knowledge to other datasets on my own.

So far, I have gone through only the Natural Language Processing for solving my assignment.

Suggestions for the instructors: -Add some more intuition sections, particularly for SVR and for XGBoost -The section on natural language processing doesn't seem to fit with the rest of the course.

I would have liked additional real world examples, especially on Natural language processing.

Audio and video quality are poor in many sessions like reinforcement learning, natural language processing etc.

I particularly enjoyed the section where we compared different classification models for their performance in Natural language processing.

upper confidence in 9 reviews

I was also missing a bit a deeper connection with real world situations, especially in the output and how to iterate over datasets and results Very easy to follow - Concepts like Naive Bayes Classification, Upper Confidence Bound are explained in very simple terms.

While I have experience in ML, it was a great refresher and I did learn a few new methods such as XGBoost, Upper Confidence Bounds, and Thompson Sampling.

Overall it was a great Learning Experience Upper Confidence bound, Thompson and Bayesian concepts need to be explained better.

They could give us some source of reference material to read and understand some terms used in the course - Concepts like Naive Bayes Classification, Upper Confidence Bound are explained in very simple terms.

Upper Confidence bound, Thompson and Bayesian concepts need to be explained better.

feet wet in 9 reviews

Additionally, the use of pre-prepared templates to build the models means that this is not an effective course for practicing Python or R. If you are looking for a fun course to learn some rudimentary basics about machine learning models and you are looking to get your feet wet with data science then this course is great.

Big picture, this course was immensely helpful in helping me to get my feet wet into machine learning, specifically with learning the landscape and the terminology and the libraries.

If you get your feet wet with some basic ML course and then want to take your skills to the next level, this course would definitely benefit you.

nitty gritty in 9 reviews

I really appreciate the fact that all the concepts were implemented in both python and R. Great overall course so far i have been introduced to the basics ready to get into the nitty gritty assignments and see what i can learn from that Really amazing course.

Great to have real world examples of the various models Gets right into the nitty gritty of how to execute code.

So far the content has been very good, though I feel like some of the nitty gritty coding points could have been explained in more depth.

so far i have been introduced to the basics ready to get into the nitty gritty assignments and see what i can learn from that It is a good course and personally for me with the theoretical background in Machine learning, it was fun to implement the projects and play around.

direto ao ponto in 8 reviews

Professor vai direto ao ponto, sem rodeios.

Great course - so I am so excited that I want to try another courses by these authors))) A didática é muito boa, objetiva, indo direto ao ponto de como executar os comandos sem voltas desnecessárias.

Curso extremamente didático, com o conteúdo muito bem dividido e explicado, mas sem exageros e digressões, sempre direto ao ponto sem deixar pontas soltas.

A didática é muito boa, objetiva, indo direto ao ponto de como executar os comandos sem voltas desnecessárias.

Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Research Scientist-Machine Learning $55k

Cloud Architect - Azure / Machine Learning $75k

Watson Machine Learning Engineer $81k

Machine Learning Software Developer $103k

Software Engineer (Machine Learning) $116k

Applied Scientist, Machine Learning $130k

Autonomy and Machine Learning Solutions Architect $131k

Applied Scientist - Machine Learning -... $136k

RESEARCH SCIENTIST (MACHINE LEARNING) $147k

Machine Learning Engineer 2 $161k

Machine Learning Scientist Manager $170k

Machine Learning Scientist, Personalization $213k

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Udemy

Rating 4.3 based on 30,773 ratings
Length 41.5 hours
Starts On Demand (Start anytime)
Cost $10
From Udemy
Instructors Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, SuperDataScience Support
Download Videos Only via the Udemy mobile app
Language English
Subjects Business Data Science
Tags Business Data & Analytics

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