Applied Machine Learning in Python using scikit-learn, mlxtend and pandas
“A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning.”
With the advent of Deep Learning algorithms a decade back, the field of data science and machine learning has witnessed renewed zeal and enthusiasm. Today, every firm is eager to hire a data scientist who can derive value out of the data, but the key question is - Where should I begin? Various industry leaders are deploying deep learning models, should I do the same? Is traditional machine learning still relevant in this era to solve my business problem?
In this workshop we will address these question and take a deep dive into applying some of the most widely used traditional machine learning algorithms on real life use cases. We will utilize open source libraries - scikit-learn, pandas & mlxtend for this purpose.
The key steps we will employ to tackle each problem are:
- Understanding the algorithm
- Importing the data
- Data wrangling using pandas
- Machine learning model development using scikit-learn/mlxtend
- Model performance evaluation
Each exercise will employ a jupyter notebook based learning environment.
The workshop session (2.5 hours) will be divided as follows:
- Introduction to Machine Learning - 5 mins
- Why traditional machine learning is still relevant! - 5 mins
- Exercise #1: Real Estate Valuation using Regression Algorithm (OLS) - 25 mins
- Exercise #2: Market Basket Analysis using Association Rule Learning Algorithm (Apriori) - 25 mins
- Exercise #3: Credit Risk Analysis using Instance-based Algorithm (kNN) - 20 mins
- Break - 10 mins
- Exercise #4: Macroeconomic Analysis of Countries using Clustering Algorithm (k-Means) - 25 mins
- Exercise #5: Credit Risk Analysis using Decision Tree Algorithm (CART) - 25 mins
- Closing Remarks and Q&A - 10 mins
- Technical: Basic Python Programming
- Software: Python 3.6+
Please install the following python packages -
pip install scikit-learn, pandas, mlxtend, matplotlib
Workshop Slides - Link
Ashita is a strategy consultant with a keen sense of technology and its application in business. She has worked across industries spanning - e-commerce, retail, manufacturing and pharmaceuticals; analysing and solving critical business problems via the application of cutting-edge analytics. She also has extensive experience in application development using python as she spearheaded the development of award winning advanced 3D visualization toolkit for optical microscopy at IIT Kanpur (BTech) and developed new Batching algorithms in Python for Operations Management at IIM Ahmedabad (PGP MBA).
Apart from being a data science researcher with publications in the 5th IEEE International Conference on Data Science and Engineering 2019 and Data Science Congress 2018, Ashita is also a full stack Flutter developer and creator of Flutter Gems - A Curated Package Guide for Flutter which is currently being used by more than 3500 mobile developers across the globe.
Ashita is also an ardent Pythonista and an active contributor to the Indian Python Community as she has previously conducted a talk in SciPy India 2017 and a workshop on machine learning in SciPy India 2019.