Welcome to week 2 - a continuation of my progress series.
The previous week was quite a marathon and from that my guess is that this coming week will be more challenging but I'm still prepared because Once you begin, there's no quitting.
Monday
This is Day 6 of the data science marathon.
I did an introduction to scikit-learn with main focus on ensemble algorithms.
One thing that I always find fascinating is that every time I take a beginner approach towards learning, I always encounter totally new concepts. Today I learnt about the simplicity of the Scikit-learn API design where I looked at the basic interfaces, the submodules of Scikit-learn i.e
- Datasets : sklearn.datasets
- Preprocessing : sklearn.preprocessing
- Impute : sklearn.impute
- Feature Selection : sklearn.feature_selection
- Linear Models : sklearn.linear_model
- Ensemble Methods : sklearn.ensemble
- Clustering : sklearn.cluster
- Matrix Decomposition : sklearn.decomposition
- Manifold Learning : sklearn.manifold
- Metrics : sklearn.metrics
- Pipeline : sklearn.pipeline
- Model Selection : sklearn.model_selection
Ensemble learning on the other hand is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem
Tuesday
For day 7, my focus was on XGBoost Algorithm which is an ensemble learning technique to build a strong classifier from several weak classifiers in series.
XGBoost stands for “Extreme Gradient Boosting” and it is an optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable to implement Machine Learning algorithms under the Gradient Boosting framework. It provides a parallel tree boosting to solve many data science problems in a fast and accurate way.
Stay tuned for Wednesday and the rest of the week.
Hope you have a great and fruitful day! 👋 🌱
For any errors observed in this article, please mention them in the comments. 🧑🏻💻
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