Machine Learning

Practitioner

We are on your path to a practice in Machine Learning and Artificial Intelligence.
Consider MLP your  resource and information site for news, tools and techniques to maintain your skillset.

MLPractitioner Nano-Courses

Wrangling Nano-Course

 

Wrangling

Data-Munging, Pre-processing or whatever you call it, necessary for any data science or machine-learning projects you may take on.

Pre-Requisite:

Just one. Take a beginning  course in Python.  Preferably using 3.7 or later. There are many inexpensive or free courses out there. Once you are a bit comfortable looking up syntax for methods and making things work, you should be ready to wrangle. You can always go back to basics at any point.

This nano-course isn’t exceptionally deep but it does have moderate complexity if you’re new to this. My recommendation is that you check out the ‘cheat-sheets’ I have posted in the Learn page which were created by others in the open-source community of Python which is really robust.

Pickling

Pickling

Python Pickle() is a built-in function. Pickling is used for serialization of a dataset to save to or retrieve from a file.
Merge Join Concatenate

Merge Join Concatenate

Merge, Join and Concatenation came from the SQL world and totally apply in Python especially if you need to restructure datasets.
Scraping

Scraping

Scraping is made simpler with Python. This essential skill will be valuable for acquiring data and building your own datasets from information on the Internet.
Python File Ops

Python File Ops

Simple file I/O with Python provides a basic skill that you can use to access data and other objects stored on internal, external or cloud storage systems.
Slicing and Dicing

Slicing and Dicing

Slicing into data structures, dataframes and lists are a fundamental Python skill that must be mastered.
Pandas : Part 1

Pandas : Part 1

The Pandas_Playpen.ipynb is an exercise notebook that takes you through the essential constructs of the Pandas library for Python used in data science projects.

Visualization Nano-Course

 

Visualization

We see data. It will be necessary to visualize data during the exploratory data analysis phase of your data science projects. There are great tools and libraries that have been perfected and supported by open source.

Pre-Requisite:

Just one. Take a beginning  course in Python.  Preferably using 3.6 or later. There are many inexpensive or free courses out there. Once you are a bit comfortable looking up syntax for methods and making things work, you should be ready to wrangle. You can always go back to basics at any point.

Visualization is a skill you can develop over time. Prepare to hack around a little bit, consume lots of stack overflow answers and spend time going through Github repos of the myriad python-based projects using the big-three libraries – Matplotlib, Seaborn and Plot.ly. The great thing is that you can do it within a Jupyter Notebook, inline. It helps a lot.

Plotly Fundamentals Part 1

Plotly Fundamentals Part 1

Machine Learning Practitioner MLPractitioner.com We are on your path to a practice in Machine Learning and ...
Seaborn Statistical Part1

Seaborn Statistical Part1

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Matplotlib Gym Part 1

Matplotlib Gym Part 1

Machine Learning Practitioner MLPractitioner.com We are on your path to a practice in Machine Learning and ...