Great Learning has posted this 11 hour full course on Data Science with Python for Beginners course.

Index:

  • Statistics vs Machine Learning – 2:15
  • Types of Statistics – 8:55
  • Types of Data – 1:50:35
  • Correlation – 2:45:50
  • Covariance – 2:52:23
  • Basics of Python – 4:24:36
  • Python Data Structures – 4:43:58
  • Flow Control Statements in Python – 4:55:58
  • Numpy – 5:32:48
  • Pandas – 5:51:30
  • Matplolib – 6:14:28
  • Linear Regression – 6:38:14
  • Logistic Regression – 9:54:34

FinTech is gaining more and more traction as a recognized industry.

The University of Toronto has launched a FinTech boot camp and applications are open.

The course, which is 24 weeks long and is composed of two night classes during the week and one class on the weekends.

“We felt this was another opportunity for us to move forward and be able to provide the skills that employers are looking for in this marketplace,” said MacDonald in an interview. “If you look at Toronto, it’s really becoming one of the fastest-growing financial tech centers in the world. By introducing the FinTech boot camp, we’re hoping to respond to some of that and capitalize on the evolving marketplace in the GTA.”

Curious to see the Open Application Model (OAM) in action?

Look no further than Rudr – the Kubernetes reference implementation of OAM.

In this episode of Azure Fridays, Mackenzie Olson and Sudhanva Huruli join Donovan Brown to demo how Rudr provides clear separation of concerns for DevOps practices for application developers, application operators, and infrastructure operators. With Rudr, we describe our distributed application, and then run two instances on AKS and GKE respectively.

More Resources

Here’s an interesting talk on Dask from AnacondaCon 2018.

Tom Augspurger. Scikit-Learn, NumPy, and pandas form a great toolkit for single-machine, in- memory analytics. Scaling them to larger datasets can be difficult, as you have to adjust your workflow to use chunking or incremental learners. Dask provides NumPy- and pandas-like data containers for manipulating larger than memory datasets, and dask-ml provides estimators and utilities for modeling larger than memory datasets.

These tools scale your usual workflow out to larger datasets. We’ll discuss some of the challenges data scientists run into when scaling out to larger datasets. We’ll then focus on demonstrations of how dask and dask-ml solve those challenges. We’ll see examples of how dask can expose a cluster of machines to scikit-learn’s built-in parallelization framework. We’ll see how dask-ml can train estimators on large datasets.AnacondaCon 2018. Tom Augspurger. Scikit-Learn, NumPy, and pandas form a great toolkit for single-machine, in- memory analytics.

Scaling them to larger datasets can be difficult, as you have to adjust your workflow to use chunking or incremental learners. Dask provides NumPy- and pandas-like data containers for manipulating larger than memory datasets, and dask-ml provides estimators and utilities for modeling larger than memory datasets. These tools scale your usual workflow out to larger datasets. We’ll discuss some of the challenges data scientists run into when scaling out to larger datasets. We’ll then focus on demonstrations of how dask and dask-ml solve those challenges. We’ll see examples of how dask can expose a cluster of machines to scikit-learn’s built-in parallelization framework. We’ll see how dask-ml can train estimators on large datasets.

This may be old news by now, but here’s an interesting write up on OpenAI’s decision to standardize development on PyTorch.

OpenAI has opted to standardise its development on PyTorch, saying the move should make it easier for its developers “to create and share optimised implementations of our models”. The AI non-profit turned profit making concern with a non-profit arm said the move would help it increase its research productivity […]