التعلم الآلي A-Z ™: Python & R في علوم البيانات
Udemy Review
الموقع
Online(رابط الدورة)
التاريخ
عند الطلب
أقسام الدورات
نظم معلومات و تكنولوجيا
الشهادة
No
اللغة
الإنجليزية
رسوم الدورة
عدد الحضور
غير محدود
المهارات المكتسبة
- Master Machine Learning on Python & R,Have a great intuition of many Machine Learning models,Make accurate predictions,Make powerful analysis,Use Machine Learning for personal purpose,Handle specific topics like Reinforcement Learning,NLP and Deep Learning,Handle advanced techniques like Dimensionality Reduction,Know which Machine Learning model to choose for each type of problem,Build an army of powerful Machine Learning models and know how to combine them to solve any problem
| اسم مقدم الدورة | Udemy |
|---|---|
| مجالات التدريب |
|
| موقعك الإلكتروني (URL) | www.udemy.com |
| حول المزود |
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instructors teaching courses in over 60 languages. There have been over 245
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coursework for employees of their company. Udemy
serves as a platform that allows instructors to build online courses on topics
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classes to create courses.[citation needed] Instructors can
also engage and interact with users via online discussion boards. Courses
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using an iPhone camera. Udemy also offers
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3,000 training courses on topics from digital marketing tactics to office
productivity, design, management, programming,
and more. With Udemy for Business, organizations can also create custom
learning portals for corporate training. |
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.
We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:
- Part 1 - Data Preprocessing
- Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Part 4 - Clustering: K-Means, Hierarchical Clustering
- Part 5 - Association Rule Learning: Apriori, Eclat
- Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
- Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP
- Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
- Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA
- Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.
And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.
