What you'll learn
In this hands-on project based course, students will learn fundamentals and actual implementation of various machine learning algorithms.
Build regressor, classifier and clusters for real world application using online project working environment and that too without downloading any software.
Make prediction using linear regression and optimization model coefficents using gradient descent algorithm
To build a logistic regression classifier to predict customer purchased decision
To classify mall customers based on k means clustering for market basket analysis. Use of ELBOW method to detect optimal k value
To identifying the gender of a voice using SVM classifier
Data visualization with seaborn and matDescriptionlib library
Model perfromance evalution using metrics like MSE, R-square error, confusion matrix, precision, recall, f1-score
K-fold cross validation method
Requirements
Beginner python is required; but that is also optional.
Google colab -free cloud based Jupyter notebook environment
Description
This project based course consists of video lectures with coding on cloud based Jupyter notebooks.
It guides you to set up an easy and interactive project working environment without downloading any software.
It's a bunch of 5 projects based on machine learning algorithms covering all details of implementation in python.
You can go through side by side video lectures to implement step wise projects inthe given worksheet as per your pace.
Finally you can download whole project code.
Final project solution sheets are also provided.
Who this course is for:
Beginner python developers curious about machine learning algorithms
Anyone who want to upgrade skill in machine learning domain.
Job aspirants who want to start career as machine learning engineer, data scientist etc.
Technologists who is curious to know about machine learning models
Any students who is interested in AI, ML and IOT domains.

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