In this course we learn that stand alone data analysis is fine but what most companies these days are looking for is to do Predictive analysis using their data. In this advanced course, we will make you ready to start doing Predictive Analysis on your data by showing you how to build Machine Learning models with scikit-learn and pandas.
In this course, you will be training models and be making data based predictions using scikit-learn.The user will like this as a standalone product as Making Predictions data using Machine Learning is an absolute minimum skill for any Data Analyst \ Data Scientist these days. We will teach users how to use scikit-learn to make data based predictions. User will learn how to bring in their data using pandas, apply some machine learning models and take out the predictions. We will also walk the user through various popular Machine Learning algorithms.
By the end of this course, the user will be quite confident of doing Predictive Analysis on their own. This subject matter is big enough that 2-3 hours of stand alone course is absolute bare minimum to achieve it.
About the author
Harish Garg is a Data Analyst, author, and Software Developer who is really passionate about Data Science and the Python programming language. He is a graduate from Udacity's Data Analyst Nanodegree program. He has 17 years of industry experience, which includes data analysis using Python, developing and testing enterprise and consumer software, managing projects and software teams, and creating training material and tutorials. Harish also worked for 11 years for Intel Security (previously McAfee, Inc.).
He regularly contributes articles and tutorials on data analysis and Python. He is also active in the open data community and is a contributing member of the Data4Democracy open data initiative. He has written data analysis pieces for think tan takshashila.
This video will introduce the scikit-learn library, how to install it, and verify the setup.
Learn how to load and process internal and external datasets into scikit-learn.
Learn how to train and run a classification model.
This video explores how to load, clean and process your data to make it ready for machine learning models in scikit-learn.
Learn how to Evaluate performance and accuracy of a machine learning model.
Explore different ways to select the best features for building a machine learning model with high accuracy.
Learn how to tune features in a machine learning model for the best performance and accuracy.
Learn how to train and run Naive Bayes classifiers.
Explore how to build machine learning using Support Vector Machine algorithm.
Learn how to use Decision Tree classifiers for machine learning.
Learn how to start with sentiment analysis by creating a corpus of text or a bag of words ready.
Learn how to build and train machine learning model on your corpus of text using scikit-learn.
Learn how to make predictions based on the bag of words using scikit-learn machine learning algorithms.
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