Data Science. You have heard it. Done some of it. But not sure of the Big picture? Welcome to this hands-on Data Science workshop.
This will help any Data Science aspirants to understand and implement end-end data science techniques from the scratch to complex Machine Learning algorithms to a dataset. The whole process is split into 5 modules,
1. Warming up
What are we trying to predict? What type of problem is it? Supervised or Unsupervised Learning? Classification or Regression? Binary or Multi-class? Uni-variate or multi-variate? Data import and environment setup with packages.
2. Exploratory Data Analysis
Is the data tidy? Imbalance? Outliers and missing values? Visualize the data. What inferences from charts?
3. Feature Engineering
Create new useful features. Interaction Terms. One-hot encoding. Manual feature selection. Automatic feature extraction. Dimensionality reduction. Cross-validation.
4. Machine Learning Model Building
Build powerful 6 ML models with parallel processing. Automatic hyper-parameter tuning. Converting sheep into wolf: Ensemble modelling
5. Get the Best
Metric evaluation. ML Models selection. Infer and conclude the prediction.