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TORRENT DETAILS
Practical Machine Learning (2024)
TORRENT SUMMARY
Status:
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Explore some data. Build data workflows. Build models. Deploy models.
What you'll learn:
Define the roles and responsibilities of a machine learning engineer
Work with datasets using pandas and identify key insights
Leverage data pipeline tools to create data workflows
Train models using libraries like scikit learn, xgboost, and PyTorch
Learn about MLOps and deploy models using backend technology like Triton Inference Server
Requirements:
Some programming or python experience is ideal
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FILE LIST
Filename
Size
~Get Your Files Here !/1. Introduction/1. Introduction.mp4
18.4 MB
~Get Your Files Here !/1. Introduction/2. Define the roles and responsibilities of a machine learning engineer.mp4
9.6 MB
~Get Your Files Here !/1. Introduction/3. A day in the life as an ML engineer.html
123 B
~Get Your Files Here !/1. Introduction/4. Common applications for ML [optional].mp4
68.2 MB
~Get Your Files Here !/1. Introduction/4.1 Good fraud dataset on Kaggle.html
122 B
~Get Your Files Here !/2. Data Analysis/1. Intro working with datasets to identify key insights.mp4
9.9 MB
~Get Your Files Here !/2. Data Analysis/1.1 Welcome to Colaboratory.html
102 B
~Get Your Files Here !/2. Data Analysis/2. Getting familiar with some 2020 census data.html
123 B
~Get Your Files Here !/2. Data Analysis/3. The power of pandas.mp4
2.2 MB
~Get Your Files Here !/2. Data Analysis/3.1 Different ways to use pandas and their performance implications.html
149 B
~Get Your Files Here !/2. Data Analysis/3.2 L7_ The Power of Pandas.pdf
54.9 KB
~Get Your Files Here !/2. Data Analysis/3.3 Pandas Cheat Sheet.html
109 B
~Get Your Files Here !/2. Data Analysis/3.4 Pandas User Guide.html
113 B
~Get Your Files Here !/2. Data Analysis/4. Exploring some additional census data.html
123 B
~Get Your Files Here !/2. Data Analysis/5. Exploring some nba player data.html
127 B
~Get Your Files Here !/3. Data Pipelines/1. What is an ETL pipeline.mp4
6.8 MB
~Get Your Files Here !/3. Data Pipelines/1.1 L8_ ETLs.pdf
59.1 KB
~Get Your Files Here !/3. Data Pipelines/2. Introduction to Airflow [optional].html
6.4 KB
~Get Your Files Here !/3. Data Pipelines/2.1 We choose Prefect in place of Airflow for our guided assignment.html
120 B
~Get Your Files Here !/3. Data Pipelines/3. Introduction to Prefect Creating your first data workflow.html
127 B
~Get Your Files Here !/3. Data Pipelines/4. Let's migrate our NBA player data exploration into a workflow.html
127 B
~Get Your Files Here !/4. Train Models/1. Train models using tools from industry like scikit learn and xgboost.mp4
16.1 MB
~Get Your Files Here !/4. Train Models/2. Making predictions off NBA player data.html
127 B
~Get Your Files Here !/4. Train Models/3. Train a PyTorch neural net model.mp4
75.9 MB
~Get Your Files Here !/4. Train Models/3.1 Colab notebook follow along.html
146 B
~Get Your Files Here !/5. Deploy Models/1. What is ML in Production.mp4
18 MB
~Get Your Files Here !/5. Deploy Models/2. Deploying a PyTorch neural net model.html
127 B
~Get Your Files Here !/5. Deploy Models/3. Metrics and Monitoring.mp4
15.6 MB
~Get Your Files Here !/6. Outro/1. That's all folks!.mp4
