Udemy Coupon: Machine Learning AZ: Practical Python and R in Data Science – Virtual Course

Online Machine Learning course, in promotion of Udemy. A course that has already been taken by more than 800.000 and has been given an average rating of 4.5.
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FutureLearnUS

Learn how to build machine learning algorithms in Python and R with two data science experts. Code templates included.

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COURSE IN PROMOTION OF UDEMY

This course has been recommended by the satisfaction of thousands of previous students

Udemy is one of the most relevant e-Learning platforms worldwide for training in digital skills and other types of personal growth interests. Millions of people study thousands of courses, from anywhere in the world. AulaPro selects the best courses from different platforms such as Udemy and others, so that our visitors can choose the most appropriate course for their needs, with the certainty of having selected the best among the best.

Udemy courses are developed by experts in their fields, worldwide. The courses that you find in this selection of AulaPro, have exceeded the evaluation of tens of thousands of students, sometimes hundreds of thousands, with an average rating greater than 4.5 out of 5, so it really is a great option, and much more if you can get a discount on the normal value of the course.

 
 

Udemy Coupon: Machine Learning AZ: Practical Python and R in Data Science – Virtual Course

Take advantage of the special price you can get today, and acquire this powerful virtual course, which can help you improve your professional profile.

New Customer Offer! Top Courses From $14.99 When You First Visit Udemy

EXCLUSIVE CLASSROOM: Offer for new students. Learn with the best courses and experts on Udemy from just USD $14.99.

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If you would like to train in the field of machine learning, then this course is for you

This course has been designed by two professional data scientists who share their knowledge and experience to help you learn complex theories, algorithms, and coding libraries in a simple way.

Instructor Words

We will guide you step by step in the world of machine learning. With each tutorial, you'll develop new skills and improve your understanding of this challenging but lucrative subfield of data science. This course is fun and exciting, but at the same time, we delve into machine learning.

The course is structured as follows:

  • Step 1 — – Data preprocessing
  • Step 2 — – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
  • Step 3 — – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
  • Step 4 — – Clustering: K-means, hierarchical clustering
  • Step 5 — – Learning association rules: Apriori, Eclat
  • Step 6 — – Reinforcement learning: Upper confidence limit, Thompson sampling
  • Step 7 — – Natural Language Processing: Bag-of-Words Model Algorithms for NLP
  • Step 8 — – Deep learning: artificial neural networks, convolutional neural networks
  • Step 9 — – Dimensionality reduction: PCA, LDA, core PC
  • Step 10 — – Model selection and boosting: k-fold cross-validation, parameter tuning, grid search, XGBoost

Additionally, this data science course is packed with hands-on exercises based on real-life examples.

There's little time left. Switch to annual and save! Coursera Plus for only USD $199. Click and find out how.

Not only will you learn the theory, but you'll also get some practice building your own models.

And as a bonus, this course includes Python and Rcode templates that you can download and use in your own projects.

Microsoft Azure Certification Training

Important updates (June 2020): CODES EVERYTHING UP TO DATE EEP LEARNING CODED ON TENSORFLOW .0 TOP GRADIENT BOOSTING MODELS, INCLUDING XGBOOST AND EVEN CATBOOST.

Visit this course on the Udemy platform

Visit the page of this course on the platform, and learn more details.


Visit this course on the Udemy platform

Visit the page of this course on the platform, and learn more details.


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