Full Introduction to Knowledge Science and Machine Studying from Fundamental to Superior.
What you’ll be taught
College students can have develop understanding of libraries used for Knowledge Evaluation like Pandas and Numpy.
Study to create impactful visualizations utilizing Matplotlib and Seaborn. By creating these visualizations it is possible for you to to derive higher conclusions from knowledge.
After this course you’ll be taught to construct full Knowledge Science Pipeline from Knowledge preparation to constructing the perfect Machine Studying Mannequin.
The course accommodates sensible part after each new idea mentioned and the course additionally has two initiatives on the finish.
Description
- Learn to use Numpy and Pandas for Knowledge Evaluation. This can cowl all primary ideas of Numpy and Pandas which might be helpful in knowledge evaluation.
- Study to create impactful visualizations utilizing Matplotlib and Seaborn. Creating impactful visualizations is an important step in creating a greater understanding about your knowledge.
- This course covers all Knowledge Preprocessing steps like working with lacking values, Characteristic Encoding and Characteristic Scaling.
- Find out about totally different Machine Studying Fashions like Random Forest, Choice Timber, KNN, SVM, Linear Regression, Logistic regression and many others… All of the video periods will first focus on the essential principle idea behind these algorithms adopted by the sensible implementation.
- Study to how to decide on the perfect hyper parameters to your Machine Studying Mannequin utilizing GridSearch CV. Selecting the perfect hyper parameters is a vital step in rising the accuracy of your Machine Studying Mannequin.
- You’ll be taught to construct an entire Machine Studying Pipeline from Knowledge assortment to Knowledge Preprocessing to Mannequin Constructing. ML Pipeline is a vital idea that’s extensively used whereas constructing giant scale ML initiatives.
- This course has two initiatives on the finish that will probably be constructed utilizing all ideas taught on this course. The primary undertaking is about Diabetes Prediction utilizing a classification machine studying algorithm and second is about prediciting the insurance coverage premium utilizing a regression machine studying algorithm.
English
language
Content material
Welcome and Course Overview
Welcome
Course Overview
Numpy
Numpy Introduction and Set up
Creating Arrays in Numpy
Array Form and Reshape
Array Indexing
Array Iterating
Array Slicing
Looking out and Sorting
Pandas
Pandas Introduction and Set up
Pandas Sequence
Pandas DataFrame
Pandas ReadCSV
Pandas Analyzing DataFrames
Knowledge Visualization
Matplotlib Introduction
Several types of plots in Matplotlib
Seaborn
Knowledge Preparation
Dealing with Lacking Values
Characteristic Encoding
Characteristic Scaling
Machine Studying
Machine Studying Introduction
Supervised Machine Studying
Unsupervised Machine Studying
Practice Take a look at Break up
Regression Evaluation
Linear Regression
Logistic Regression
KNN
SVM
Choice Tree
Random Forest
Ok Means Clustering
GridSearch CV
Machine Studying Pipeline
Machine Studying Pipeline
Initiatives
Diabetes Prediction
Insurance coverage Value Prediction
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