Download Udemy Data Science and Machine Learning Basic to Advanced

What you would learn in Data Science and Machine Learning Basic to Advanced course?

• Learn to utilize Numpy and Pandas to aid in Data Analysis. This tutorial will go over the fundamental notions associated with Numpy and Pandas, which are helpful for data analysis.

• Learn how to create powerful visualizations with Matplotlib and Seaborn. Making powerful visualizations is the first element in gaining knowledge of your data.

• This course covers all Data Processing processes, including working with missing values, feature encoding, and feature scaling.

• Learn about the various Machine Learning Models like Random Forest, Decision Trees, KNN, SVM, Linear Regression, Logistic regression, and Linear Regression... The videos will begin by discussing the fundamental theory behind these algorithms, followed by the application in practice.

• Learn how to select the most appropriate hyperparameter for Your Machine Learning Model using GridSearch CV. Making the right choice of hyperparameters is an essential measure to increase the precision that you can achieve with the accuracy of your Machine Learning Model.

• You will be taught how to create an entire Machine Learning Pipeline from Data collection through Data Processing and, finally, Model Building. ML Pipeline is a vital idea widely utilized when building large-scale ML projects.

• The course includes two projects to be completed at the end, developed with the knowledge learned during this class. The first concerns Diabetes Prediction using a classification machine learning algorithm. The other is predicting the insurance premium using an algorithm for regression machine learning.

Content of the Course:

• Students can comprehend libraries to perform Data Analysis like Pandas and Numpy.
• Learn how to create powerful visualizations by using Matplotlib as well as Seaborn. By creating these visualizations, you'll be able to make better decisions from the data.
• Following this course, you'll learn how to build an entire Data Science Pipeline from Data preparation to creating the most effective Machine Learning Model.
• The course includes a practical portion for each concept covered, and there are two final projects.

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