How to build a machine learning model - Complete guide for your first data science project

SO many juicy links to help you on your journey (you can thank me by liking, subscribing and sharing ;) )

** note: the links below are affiliate links, meaning I receive a small referral commission

FIND A SUPER COOL DATA SCIENCE MENTOR
https://www.sharpestminds.com/?r=damsel

MY RECOMMENDED DATA SCIENCE COURSERA COURSES

Professional Data Science Certificate (IBM): https://coursera.pxf.io/QOvm49

Python for Everybody (University of Michigan): https://coursera.pxf.io/kj1BZv

Machine Learning (Stanford): https://coursera.pxf.io/e45Vg6

Statistics with Python (University of Michigan): https://coursera.pxf.io/Gj4zG6

And finally, for data people who didn’t major in computer science and are tired of getting made fun of by software engineers:
Data Structures and Algorithms (UC San Diego) - https://coursera.pxf.io/rnq6Ld

OTHER LINKS - MENTIONED IN VIDEO

Titanic Classifier Project on Kaggle: https://www.kaggle.com/c/titanic

Data Science project ideas: https://dev.to/anujgupta/50-top-data-science-project-ideas-for-beginners-and-experts-a3d

Public datasets: https://github.com/awesomedata/awesome-public-datasets

Scraping data from the web:
https://towardsdatascience.com/web-scraping-make-your-own-dataset-cc973a9f0ee5

Pandas profiling - an EDA Python Library:
https://github.com/pandas-profiling/pandas-profiling

Data prep for machine learning: https://towardsdatascience.com/data-preparation-for-machine-learning-cleansing-transformation-feature-engineering-d2334079b06d

Feature Scaling / Standardization and Normalization:
https://www.analyticsvidhya.com/blog/2020/04/feature-scaling-machine-learning-normalization-standardization/

Feature Engineering: https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/

Dimensionality Reduction:
https://machinelearningmastery.com/dimensionality-reduction-for-machine-learning/

Choosing a machine learning algorithm: https://www.kdnuggets.com/2019/10/choosing-machine-learning-model.html

K-Fold Cross Validation:
https://machinelearningmastery.com/k-fold-cross-validation/

Hyperparameter Tuning: https://towardsdatascience.com/hyperparameter-tuning-c5619e7e6624

Grid Search: https://medium.com/fintechexplained/what-is-grid-search-c01fe886ef0a

https://scikit-learn.org/stable/modules/grid_search.html

Feature Selection: https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/

Feature Importances: https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html

https://towardsdatascience.com/explaining-feature-importance-by-example-of-a-random-forest-d9166011959e

Choosing a performance metric: https://www.kdnuggets.com/2018/06/right-metric-evaluating-machine-learning-models-2.html

Model Deployment
Flask - a microweb framework for Python, great for wrapping your model into a web app: https://opensource.com/article/18/4/flask

Deploying ML models with Flask and Heroku: https://medium.com/analytics-vidhya/deploy-machinelearning-model-with-flask-and-heroku-2721823bb653

Deploying Machine Learning Models on GCP: https://heartbeat.fritz.ai/deploying-machine-learning-models-on-google-cloud-platform-gcp-7b1ff8140144

#datascience #machinelearning #codingproject #pythonproject

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