Clustering and Classification with Machine Studying in Python [Video]
5 hours 50 minutes
|About||On this age of massive information, corporations throughout the globe use Python to sift via the avalanche of data at their disposal. By changing into proficient in unsupervised and supervised studying in Python, you may give your organization a aggressive edge and degree up in your profession. This course offers you a strong grounding in clustering and classification, the principle elements of machine studying.
The course consists of seven sections that may enable you grasp Python machine studying. You’ll start with an introduction to Python information science and Anaconda, which is a robust Python-driven framework for information science. Subsequent, you will delve into Pandas and browse information constructions, together with CSV, Excel, and HTML information. As you advance, you’ll carry out information cleansing and munging to take away NAsno information and uncover deal with conditional information, group by attributes, and do far more. You’ll additionally grasp primary ideas of unsupervised studying equivalent to Okay-means clustering and its implementation on the Iris dataset. The course will take you thru the idea of dimension discount and have choice for machine studying and enable you perceive Principal Element Evaluation (PCA) utilizing two case research. You’ll become familiar with the linear and non-linear classification of SVM together with Gradient Boosting Machine (GBM) and Naive Bayes Classification. Lastly, you’ll discover neural networks and uncover the highly effective H20 framework and for deep studying classification. Moreover, you’ll study perceptrons and Synthetic Neural Networks (ANN) for binary classification.
By the top of this course, you’ll use packages equivalent to NumPy, Pandas, and Matplotlib to work with actual information in Python.
All code and supporting information for this course can be found at
|Course Size||5 hours 50 minutes|
|Date Of Publication||30 Dec 2019|