Machine Learning A-Z™ Hands-On Python & R In Data Science (Update 10-2020)

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  • Machine Learning A-Z™ Hands-On Python & R In Data Science (Update 10-2020)
    Machine Learning A-Z™: Hands-On Python & R In Data Science (Update 10-2020)
    Video: .mp4 (1280x720) | Audio: AAC, 44100 kHz, 2ch | Size: 11.7 Gb
    Genre: eLearning Video | Duration: 43h 51m | Language: English

Machine Learning A-Z™ Hands-On Python & R In Data Science (Update 10-2020) Machine Learning A-Z™: Hands-On Python & R In Data Science (Update 10-2020) Video: .mp4 (1280x720) | Audio: AAC, 44100 kHz, 2ch | Size: 11.7 Gb Genre: eLearning Video | Duration: 43h 51m | Language: English Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included. 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 that 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. Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me
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