Machine Learning A-Z™: Hands-On Python & R In Data Science – Free Udemy Courses


Perspectives:
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Created by means of Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Crew

English

What Will I Be informed?

  • Grasp Machine Learning on Python & R
  • Have an ideal instinct of many Machine Learning fashions
  • Make correct predictions
  • Make robust research
  • Make powerful Machine Learning fashions
  • Create sturdy added price to your small business
  • Use Machine Learning for private function
  • Deal with particular subjects like Reinforcement Learning, NLP and Deep Learning
  • Deal with complicated tactics like Dimensionality Relief
  • Know which Machine Learning type to select for each and every form of downside
  • Construct a military of robust Machine Learning fashions and know the way to mix them to resolve any downside

Necessities

  • Just a few highschool arithmetic stage

Description

within the box of Machine Learning? Then this direction is for you!

This direction has been designed by means of two skilled Data Scientists in order that we will proportion our wisdom and permit you to be informed advanced idea, algorithms and coding libraries in a easy means.

We will be able to stroll you step-by means of-step into the Global of Machine Learning. With each educational you’re going to broaden new abilities and give a boost to your working out of this difficult but profitable sub-box of Data Science.

This direction is a laugh and thrilling, however on the identical time we dive deep into Machine Learning. It’s structured the next means:

  • Phase 1 – Data Preprocessing
  • Phase 2 – Regression: Easy Linear Regression, A couple of Linear Regression, Polynomial Regression, SVR, Resolution Tree Regression, Random Woodland Regression
  • Phase 3 – Classification: Logistic Regression, Ok-NN, SVM, Kernel SVM, Naive Bayes, Resolution Tree Classification, Random Woodland Classification
  • Phase 4 – Clustering: Ok-Manner, Hierarchical Clustering
  • Phase 5 – Affiliation Rule Learning: Apriori, Eclat
  • Phase 6 – Reinforcement Learning: Higher Self belief Certain, Thompson Sampling
  • Phase 7 – Herbal Language Processing: Bag-of-phrases type and algorithms for NLP
  • Phase 8 – Deep Learning: Synthetic Neural Networks, Convolutional Neural Networks
  • Phase 9 – Dimensionality Relief: PCA, LDA, Kernel PCA
  • Phase 10 – Type Variety & Boosting: okay-fold Move Validation, Parameter Tuning, Grid Seek, XGBoost

Additionally, the direction is filled with sensible workouts which can be in response to are living examples. So no longer best will you be informed the idea, however you’re going to additionally get some palms-on follow development your individual fashions.

And as an advantage, this direction comprises each Python and R code templates which you’ll be able to download and use by yourself initiatives.

Who’s the objective target market?

  • Any individual considering Machine Learning
  • Scholars who’ve no less than highschool wisdom in math and who wish to get started studying Machine Learning
  • Any intermediate stage individuals who know the fundamentals of system studying, together with the classical algorithms like linear regression or logistic regression, however who wish to be informed extra about it and discover all of the other fields of Machine Learning.
  • Any individuals who don’t seem to be that happy with coding however who’re considering Machine Learning and wish to observe it simply on datasets.
  • Any scholars in school who wish to get started a profession in Data Science.
  • Any information analysts who wish to stage up in Machine Learning.
  • Any individuals who don’t seem to be happy with their process and who wish to develop into a Data Scientist.
  • Any individuals who wish to create added price to their trade by means of the use of robust Machine Learning equipment

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Content material retrieved from: https://www.udemy.com/machinelearning/.

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