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Practical Machine Learning for Data Scientists | Free Udemy Course
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Practical Machine Learning for Data Scientists | Free Udemy Course

Free $64.99100% off
Price expires 1 year ago or 91 uses

Practical AI and ML | Free Udemy Course

(88 ratings)
2245 students
Last updated 8/2022Course Language ArabicCourse Caption Course Length 13:39:49 to be exact 49189 seconds!Number of Lectures 62
This course includes:
  • 13.5 hours hours of on-demand video
  • 1 article
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion

This course is a comprehensive introduction to AI and Machine Learning, targeting Data Scientists and Machine Learning engineers. It starts with setting the boundaries of Artificial Intelligence, Machine Learning, Deep Learning, and their relation to Data Science. What is expected as a member an AI team, and how to speak the same language. What is possible and what is not, and what defines a good AI project. The basics of supervised learning are covered, including the main ingredients of the Machine Learning problem, and the different solution setups. We cover both Linear models (Linear Regression, Logistic Regression, Support Vector Machines (SVM)) and Non-linear models (Polynomial Regression, Kernel SVM, Deep Neural Networks (DNN)). A universal approach is given to tackle any ML problem in a systematic way, covering data preparation, Exploratory Data Analysis (EDA), Model selection, Model evaluation, Model design, Fine tuning and Regularization. An end-to-end is given to illustrate this process with code in Google Colab Notebooks. We also cover the Machine Learning Meta algorithms and Ensemble methods: Voting, BAGGing, Boosting Decision Trees and Random Forests. Finally, we introduce unsupervised learning, covering dimensionality reduction algorithms, like Manifold Learning like Locally Linear Embedding (LLE) and Projection methods like Principal Component Analysis (PCA) and Clustering, like K-Means. Throughout the course, Python language is used. Popular Machine Learning libraries are used, like scikit-learn, in addition to pandas and keras.Who this course is for:Beginner level data scientists

Course Content:

Sections are minimized for better readability, click the section title to view the course content

1 Lectures | 05:13
4 Lectures | 02:29:20
20 Lectures | 04:21:21
17 Lectures | 02:58:11
11 Lectures | 02:06:23
8 Lectures | 01:39:20
1 Lectures | 00:01
(88 course ratings)






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