Machine Learning Series #2: Python ML Prerequisite Libraries смотреть онлайн

Numpy Notebook: https://colab.research.google.com/drive/1Wddc297jjGnp_dn5K8eP7GXLiFI7rQqT?usp=sharing
Scipy Notebook: https://colab.research.google.com/drive/1MxlJXOihK94UIbr-YpJ5c26kEbxPfFfP?usp=sharing
Pandas Notebook: https://colab.research.google.com/drive/1HEblK-iFtJ64Lnk34-Byiyp1yRg9OdLs?usp=sharing
Matplotlib Notebook: https://colab.research.google.com/drive/1sXQgKX3vwojnp_62BFwvK-jlxr7-oo1F?usp=sharing


Submission Form: https://forms.gle/jUYitFN6iZk6Pykt6
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Part 2 of our Machine Learning series is here! This time, we're going to cover the libraries required for you to work with Machine Learning. We're going to have hands-on exercises to work with numpy, scipy, matplotlib, and pandas.

The three part Machine Learning series will consist of:
1. Introduction to Python Programming
2. Introduction to Python ML prerequisite libraries: numpy, scipy, matplotlib, and pandas
3. Beginner ML with Tensorflow

The events form a series yet remain independent in nature, so having attended part 1 is NOT a must.

If you're interested, but you're not familiar with Python, you can check out a recording of our first event here: https://youtu.be/LXrl-kB6abM

The libraries we will cover are crucial prerequisites to Machine Learning with Python. For more details:

- Numpy: Numerical operations (Matrix operations, vector operations, parallelized scalar operations)
- Pandas: Input data pre-processing (Load data from CSV, XML, JSON, Text files, etc.)
- Matplotlib: Plotting data (Scatter plots, histograms, pie charts, etc.)
- Scipy: Has multiple packages useful for processing raw sensor data and doing interpolation
If it all sounds like technical jargon, no worries! That's what our event is for.