FreeCourseWeb com Practical Machine Learning 2024

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/1. Introduction/

1. Introduction.mp4

2. Define the roles and responsibilities of a machine learning engineer.mp4

3. A day in the life as an ML engineer.html

4. Common applications for ML [optional].mp4

4.1 Good fraud dataset on Kaggle.html

/2. Data Analysis/

1. Intro working with datasets to identify key insights.mp4

1.1 Welcome to Colaboratory.html

2. Getting familiar with some 2020 census data.html

3. The power of pandas.mp4

3.1 Different ways to use pandas and their performance implications.html

3.2 L7_ The Power of Pandas.pdf

3.3 Pandas Cheat Sheet.html

3.4 Pandas User Guide.html

4. Exploring some additional census data.html

5. Exploring some nba player data.html

/3. Data Pipelines/

1. What is an ETL pipeline.mp4

1.1 L8_ ETLs.pdf

2. Introduction to Airflow [optional].html

2.1 We choose Prefect in place of Airflow for our guided assignment.html

3. Introduction to Prefect Creating your first data workflow.html

4. Let's migrate our NBA player data exploration into a workflow.html

/4. Train Models/

1. Train models using tools from industry like scikit learn and xgboost.mp4

2. Making predictions off NBA player data.html

3. Train a PyTorch neural net model.mp4

3.1 Colab notebook follow along.html

/5. Deploy Models/

1. What is ML in Production.mp4

2. Deploying a PyTorch neural net model.html

3. Metrics and Monitoring.mp4

/6. Outro/

1. That's all folks!.mp4

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Bonus Resources.txt

Total files 31