Free Coupon Code Practical Machine Learning for Data Scientists 100% OFF

Free Coupon Code Practical Machine Learning for Data Scientists 100% OFF
Free Coupon Code Practical Machine Learning for Data Scientists 100% OFF

Udemy coupon code for Practical Machine Learning for Data Scientists course taught by Coursat.ai Dr. Ahmad ElSallab, which has 8,412 students and is rated 4.7 out of 320 votes. This course is about 13 hour(s) 30 minute(s) in Arabic and was updated on November 13, 2024. You can use this Udemy course with a free certificate and find the coupon at the bottom of this page.

Practical AI and ML

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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.


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