Study to create Machine Learning Algorithms in Python and R from two Data Science specialists. Code templates included.
What you’ll be taught
- Grasp Machine Learning on Python & R
- Have a fantastic instinct of many Machine Learning fashions
- Make correct predictions
- Make highly effective evaluation
- Make sturdy Machine Learning fashions
- Create sturdy added worth to your corporation
- Use Machine Learning for private function
- Deal with particular subjects like Reinforcement Learning, NLP and Deep Learning
- Deal with superior strategies like Dimensionality Discount
- Know which Machine Learning mannequin to decide on for every kind of downside
- Construct a military of highly effective Machine Learning fashions and know the right way to mix them to resolve any downside
- Just a few highschool arithmetic degree.
within the area of Machine Learning? Then this course is for you!
This course has been designed by two skilled Data Scientists in order that we will share our information and enable you be taught complicated idea, algorithms and coding libraries in a easy manner.
We’ll stroll you step-by-step into the World of Machine Learning. With each tutorial you’ll develop new abilities and enhance your understanding of this difficult but profitable sub-area of Data Science.
This course is enjoyable and thrilling, however on the identical time we dive deep into Machine Learning. It’s structured the next manner:
- Half 1 – Data Preprocessing
- Half 2 – Regression: Easy Linear Regression, A number of Linear Regression, Polynomial Regression, SVR, Determination Tree Regression, Random Forest Regression
- Half 3 – Classification: Logistic Regression, Okay-NN, SVM, Kernel SVM, Naive Bayes, Determination Tree Classification, Random Forest Classification
- Half 4 – Clustering: Okay-Means, Hierarchical Clustering
- Half 5 – Affiliation Rule Learning: Apriori, Eclat
- Half 6 – Reinforcement Learning: Higher Confidence Certain, Thompson Sampling
- Half 7 – Pure Language Processing: Bag-of-phrases mannequin and algorithms for NLP
- Half 8 – Deep Learning: Synthetic Neural Networks, Convolutional Neural Networks
- Half 9 – Dimensionality Discount: PCA, LDA, Kernel PCA
- Half 10 – Mannequin Choice & Boosting: okay-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Furthermore, the course is filled with sensible workouts that are primarily based on actual-life examples. So not solely will you be taught the speculation, however additionally, you will get some fingers-on apply constructing your individual fashions.
And as a bonus, this course contains each Python and R code templates which you’ll download and use by yourself initiatives.
Who’s the target market?
- Anybody focused on Machine Learning.
- College students who’ve not less than highschool information in math and who need to begin studying Machine Learning.
- Any intermediate degree individuals who know the fundamentals of machine studying, together with the classical algorithms like linear regression or logistic regression, however who need to be taught extra about it and discover all of the totally different fields of Machine Learning.
- Any people who find themselves not that comfy with coding however who’re focused on Machine Learning and need to apply it simply on datasets.
- Any college students in school who need to begin a profession in Data Science.
- Any knowledge analysts who need to degree up in Machine Learning.
- Any people who find themselves not happy with their job and who need to change into a Data Scientist.
- Any individuals who need to create added worth to their enterprise by utilizing highly effective Machine Learning instruments.
Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Workforce, SuperDataScience Help
Final up to date 7/2020
Dimension: 11.50 GB
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