Gain insights into simplifying machine learning with PyCaret. Delve into regression, classification, clustering, and anomaly detection, and learn to deploy applications using Streamlit.

Simplifying Machine Learning with PyCaret_ .png

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PyCaret is an low-code, open-source, machine learning library for Python. It can be used in machine learning tasks such as data preparation and model deployment.

In this course, you will learn multiple topics related to machine learning. You will start with a brief introduction to the basic concepts of machine learning, and then continue with case studies of regression, classification, clustering, and anomaly detection based on the respective modules of the PyCaret library. Finally, we will focus on using the Streamlit library to develop and deploy machine learning applications.

By the end of this course, you will have the skills to deploy the robust PyCaret library for any of your machine learning projects.

Simplifying Machine Learning with PyCaret in Python

# Creating the model
We can now train the Gradient Boosting Regressor using the `create_model()` function, which uses $k$-fold cross-validation to evaluate model accuracy. 

# Create the model

 model = create_model('gbr', fold = 10)

Create model

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The dataset is partitioned into $k$ subsamples, with one of them being retained for validation, while the rest are used to train the model. This process is repeated $k$ times, with the resulting metrics for each subsample being displayed when it’s completed.
# Tuning the model
**Hyperparameter** tuning is a technique where the various parameters of a machine learning model are tweaked to optimize its performance. The `tune_model()` function can be used to apply hyperparameter tuning on a trained model by using the randomized search method, where a random sample of possible combinations is tested.

In regression tasks, learn to create, tune, plot, save, and make predictions from the machine learning model.

	MAE	MSE	RMSE	R2	RMSLE	MAPE
0	2324.8131	24607040.5795	4960.5484	0.8393	0.3810	0.1751
1	2571.2202	29114809.8137	5395.8141	0.7413	0.4376	0.2551
2	2483.0865	21498482.3051	4636.6456	0.8585	0.4261	0.2000
3	2545.2911	25489822.2086	5048.7446	0.8158	0.3937	0.1765
4	2218.7259	19926308.9440	4463.8894	0.8240	0.4237	0.2161
5	1991.9530	17546103.1066	4188.8069	0.9050	0.2883	0.1446
6	2557.6356	30138770.5115	5489.8789	0.8316	0.4120	0.1911
7	1904.3245	16102540.3261	4012.7971	0.8950	0.3101	0.1860
8	1789.5416	17459155.1593	4178.4154	0.8269	0.3299	0.1574
9	2368.0500	26271250.1647	5125.5488	0.8125	0.4560	0.1721
Mean	2275.4641	22815428.3119	4750.1089	0.8350	0.3858	0.1874
SD	274.1307	4766740.8468	501.8900	0.0435	0.0546	0.0297

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Building the Model

Creating the model

Create model