Learn how to build machine learning algorithms in Python and R with two data science experts. Code templates included.
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Udemy Coupon: Machine Learning AZ: Practical Python and R in Data Science – Virtual Course
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If you would like to train in the field of machine learning, then this course is for you
This course has been designed by two professional data scientists who share their knowledge and experience to help you learn complex theories, algorithms, and coding libraries in a simple way.
Instructor Words
We will guide you step by step in the world of machine learning. With each tutorial, you'll develop new skills and improve your understanding of this challenging but lucrative subfield of data science. This course is fun and exciting, but at the same time, we delve into machine learning.
The course is structured as follows:
- Step 1 — – Data preprocessing
- Step 2 — – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Step 3 — – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Step 4 — – Clustering: K-means, hierarchical clustering
- Step 5 — – Learning association rules: Apriori, Eclat
- Step 6 — – Reinforcement learning: Upper confidence limit, Thompson sampling
- Step 7 — – Natural Language Processing: Bag-of-Words Model Algorithms for NLP
- Step 8 — – Deep learning: artificial neural networks, convolutional neural networks
- Step 9 — – Dimensionality reduction: PCA, LDA, core PC
- Step 10 — – Model selection and boosting: k-fold cross-validation, parameter tuning, grid search, XGBoost
Additionally, this data science course is packed with hands-on exercises based on real-life examples.
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Not only will you learn the theory, but you'll also get some practice building your own models.
And as a bonus, this course includes Python and Rcode templates that you can download and use in your own projects.
Important updates (June 2020): CODES EVERYTHING UP TO DATE EEP LEARNING CODED ON TENSORFLOW .0 TOP GRADIENT BOOSTING MODELS, INCLUDING XGBOOST AND EVEN CATBOOST.
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