Build ML models in python without downloading any software
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 matplotlib 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.
- 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.
