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Study to create Machine Studying Algorithms in Python and R from two Information Science consultants. Code templates included.
Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Group
What Will I Study?
- Grasp Machine Studying on Python & R
- Have a fantastic instinct of many Machine Studying fashions
- Make correct predictions
- Make highly effective evaluation
- Make strong Machine Studying fashions
- Create robust added worth to what you are promoting
- Use Machine Studying for private goal
- Deal with particular subjects like Reinforcement Studying, NLP and Deep Studying
- Deal with superior methods like Dimensionality Discount
- Know which ML mannequin to decide on for every kind of drawback
- Construct a military of highly effective Machine Studying fashions and know tips on how to mix them to resolve any drawback
- Just a few highschool arithmetic degree
within the subject of ML? Then this course is for you!
This course has been designed by two skilled Information Scientists in order that we are able to share our information and make it easier to be taught advanced principle, algorithms and coding libraries in a easy means.
We’ll stroll you step-by-step into the World of ML. With each tutorial you’ll develop new expertise and enhance your understanding of this difficult but profitable sub-field of Information Science.
This course is enjoyable and thrilling, however on the identical time we dive deep into Machine Studying. It’s structured the next means:
- Half 1 – Information Preprocessing
- Half 2 – Regression: Easy Linear Regression, A number of Linear Regression, Polynomial Regression, SVR, Resolution Tree Regression, Random Forest Regression
- Half 3 – Classification: Logistic Regression, Ok-NN, SVM, Kernel SVM, Naive Bayes, Resolution Tree Classification, Random Forest Classification
- Half 4 – Clustering: Ok-Means, Hierarchical Clustering
- Half 5 – Affiliation Rule Studying: Apriori, Eclat
- Half 6 – Reinforcement Studying: Higher Confidence Certain, Thompson Sampling
- Half 7 – Pure Language Processing: Bag-of-words mannequin and algorithms for NLP
- Half 8 – Deep Studying: Synthetic Neural Networks, Convolutional Neural Networks
- Half 9 – Dimensionality Discount: PCA, LDA, Kernel PCA
- Half 10 – Mannequin Choice & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Furthermore, the course is filled with sensible workouts that are primarily based on reside examples. So not solely will you be taught the idea, however additionally, you will get some hands-on observe constructing your individual fashions.
And as a bonus, this course contains each Python and R code templates which you’ll be able to obtain and use by yourself initiatives.
Who’s the audience?
- Anybody fascinated by Machine Studying
- College students who’ve no less than highschool information in math and who wish to begin studying ML
- Any intermediate degree individuals who know the fundamentals of machine studying, together with the classical algorithms like linear regression or logistic regression, however who wish to be taught extra about it and discover all of the totally different fields.
- Any people who find themselves not that comfy with coding however who’re fascinated by Machine Studying and wish to apply it simply on datasets.
- Any college students in school who wish to begin a profession in Information Science.
- Any knowledge analysts who wish to degree up.
- Any people who find themselves not glad with their job and who wish to change into a Information Scientist.
- Any individuals who wish to create added worth to their enterprise through the use of highly effective ML instruments
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