Machine Learning A-Z™: Hands-On Python & R In Data Science

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Categories: Python

Language : ENG Duration : 40:57:28 Videos : 263 Author : Udemy

What you’ll learn Master Machine Learning on Python & R Have a great intuition of many Machine Learning models Make accurate predictions Make powerful analysis Make robust Machine Learning models Create strong added value to your business 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 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
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