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- 82% Practical Neural Networks & Deep Learning In R

Practical Neural Networks & Deep Learning In R

$14.99Track price

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8.2/10 (Our Score)
Product is rated as #221 in category Machine Learning

YOUR COMPLETE GUIDE TO PRACTICAL NEURAL NETWORKS & DEEP LEARNING IN R:       

This course covers the main aspects of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science.

 In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge and boost your career to the next level!

LEARN FROM AN EXPERT DATA SCIENTIST:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.

I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic .

This course will give you a robust grounding in the main aspects of practical neural networks and deep learning. 

Instructor Details

Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online). In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveler

Specification: Practical Neural Networks & Deep Learning In R

Duration

5.5 hours

Year

2021

Level

All

Certificate

Yes

Quizzes

No

20 reviews for Practical Neural Networks & Deep Learning In R

4.3 out of 5
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  1. Malissas

    Excellent thank you guys 🙂 Maybe some more advanced stuff in future classes….

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  2. Atela Dondth

    This course is absolutely amazing.

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  3. Gary Hutson

    Fantastic I have read three books on this, so I have a good grounding, but this has really advanced my knowledge, so thank you so much!

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  4. Manshavi Kumar

    There is no explanation of concept in this course. She didn’t even explain the code also. All she did was just reading the code in this course.

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  5. Evie Blackishire

    the instructor extremely knowledgeable.. this is a great course for someone who has a basic understanding of R code and intro Stats. I am Loving it!!!! This is truly money and time well spent.. Would be great if you can go into details on when Data Scientist need to use the various algorithms such as NeuralNet, PCA classification etc

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  6. Hayli Howard

    the course is awesome. Very valuable and add to your professional skills.

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  7. Anil Kumar Pandey

    Informative, tutor explained it in detail

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  8. Diana Telph

    Minerva goes through things nicely and you can clearly tell her personal experience in the topics that are covered.

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  9. Charles Stoy

    Good overview of Neural Networks. What is lacking is an in depth explanation of what each variable represents, why that variable, and each variables relationship to the algorithm. In short, the math and mechanics were glossed over.

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  10. sushma Pandey

    Informative

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  11. Enes Egitmen

    Poor programming practice Sloppy coding with no consistency. You can tell some code is actually borrowed (no harm, but not consistent). No interpretation or insights. Errornous explanations along the way. I started asking questions, then I realized no use, too many to ask. Just download the codes, debug them, and run yourself.

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  12. Locchi Vanessa

    Avrei preferito che, in alcuni casi, avesse preparato il corso prima, in modo da utilizzare i tempi di attesa (anche se non molti) per spiegare meglio le parti del codice meno intuitive. Comunque un ottimo corso, spiegato bene e che fornisce molti strumenti utili per analisi diverse. Grazie!

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  13. Ahmet Pala

    Cross Validation ile alakal daha fazla rnek olabilirdi. Dersi anlatan hoca kullan lan y ntemlerin teorik arka plan n daha detayl anlatabilirdi. Onun d nda ok faydal bir kurstu, te ekk rler.

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  14. Bappa Das

    How to create our own data for CNN has not been dealt. The instructor has used some default freely available dataset.

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  15. Robbie Jones

    It does not have much theory but very hands on

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  16. Ishita Islam

    It began quite well and covered basic and advance all! The theory of the course was really interesting. The depth of explanations is perfect.

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  17. Salwa Kassim

    Very detailed. better than lots of other courses in this topic .

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  18. Pavel Hoq

    Absolutely Perfect !! The course was very interesting, short and straight to the point. Lots of resources, information and very organized sessions! I feel much more confident with my knowledge of deep learning in R.

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  19. Israt Jahan

    So far the course has been an excellent match for me! The instructor is great and the materials are very helpful. Specially the sessions. Thanks a lot!

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  20. Emily Gabir

    Very good information even for someone with intermediate knowledge.

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    Practical Neural Networks & Deep Learning In R
    Practical Neural Networks & Deep Learning In R

    $14.99

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