Core Statistics for datascience
Core Statistics for Datascience
Outline for Talk
Part 1:
Part 2:
Notebooks in this repo [EDA, Feature Engineering and Predictive Modeling Theory]:
- Data Science Workflow
- Trends Supervised Learning
- Clustering
- GMM
- PCA
- Recommendations
- Network Analysis
Part 3 (Bonus):
- Doing ML in the cloud: walk through census project AWS Sagemaker
Additional Related Topics from Noah Gift
His most recent books are:
- Pragmatic A.I.: An introduction to Cloud-Based Machine Learning (Pearson, 2018)
- Python for DevOps (O’Reilly, 2020).
His most recent video courses are:
- Essential Machine Learning and A.I. with Python and Jupyter Notebook LiveLessons (Pearson, 2018)
- AWS Certified Machine Learning-Specialty (ML-S) (Pearson, 2019)
- Python for Data Science Complete Video Course Video Training (Pearson, 2019)
- AWS Certified Big Data - Specialty Complete Video Course and Practice Test Video Training (Pearson, 2019)
- Building A.I. Applications on Google Cloud Platform (Pearson, 2019)
- Pragmatic AI and Machine Learning Core Principles (Pearson, 2019)
- Data Engineering with Python and AWS Lambda (Pearson, 2019)
His most recent online courses are: