So not only will you learn the theory, but you will also get some hands-on practice building your own models.Īnd as a bonus, this course includes both Python and R code templates which you can download and use on your own projects. Moreover, the course is packed with practical exercises that are based on real-life examples. Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks ![]() Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling This implies the context where Python plays a role (. Part 5 - Association Rule Learning: Apriori, Eclat If youre using R as the primary language for your process then yes, RStudio is a great IDE for Python. And more: Even within the languages a multilingual development is possible, so in Python in the module rpy2 the necessary interface to the R-code is found and in R in the above-mentioned reticulate package the other way round. Part 4 - Clustering: K-Means, Hierarchical Clustering Python is a general purpose programming language created by Guido Van Rossum. RStudio server and the Jupyter Notebook have integrated the necessary support for both languages. However, in R Studio I really appreciate that it holds data frames in the memory and makes it easy to. Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification Install a base version of Python with pip and virtualenv Create a Python environment in your project Activate your Python environment Install Python packages. Currently, I use VS-Code to work with Python. Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression This course is fun and exciting, but at the same time, we dive deep into Machine Learning. The Department of Statistics at U-M has a growing reputation as an international leader in statistical education and research. ![]() With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. We will walk you step-by-step into the World of Machine Learning. 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. Interested in the field of Machine Learning? Then this course is for you!
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