Day 1 of 30 days of Scikit Learn Series with Projects
Welcome back peeps. Happy to share that we have just finished —
Finished Series —
60 Days of Data Science and Machine Learning with projects Series
We are now starting a new series — 30 days of scikit learn with Projects Series . This series would run in parallel with —
Ongoing Series —
Projects Videos —
All the projects, data structures, SQL, algorithms, system design, Data Science and ML , Data Analytics, Data Engineering, , Implemented Data Science and ML projects, Implemented Data Engineering Projects, Implemented Deep Learning Projects, Implemented Machine Learning Ops Projects, Implemented Time Series Analysis and Forecasting Projects, Implemented Applied Machine Learning Projects, Implemented Tensorflow and Keras Projects, Implemented PyTorch Projects, Implemented Scikit Learn Projects, Implemented Big Data Projects, Implemented Cloud Machine Learning Projects, Implemented Neural Networks Projects, Implemented OpenCV Projects,Complete ML Research Papers Summarized, Implemented Data Analytics projects, Implemented Data Visualization Projects, Implemented Data Mining Projects, Implemented Natural Leaning Processing Projects, MLOps and Deep Learning, Applied Machine Learning with Projects Series, PyTorch with Projects Series, Tensorflow and Keras with Projects Series, Scikit Learn Series with Projects, Time Series Analysis and Forecasting with Projects Series, ML System Design Case Studies Series videos will be published on our youtube channel ( just launched).
Subscribe today!
What is Scikit Learn?
In simple terms, Scikit learn is an open source and one of the most useful library for machine learning in Python. It has tools for predictive data analysis.
Scikit learn is a library which is written in Python and built upon Scipy, Matplotlib and Numpy provides a set of useful and efficient tools for machine learning and statistical modeling including regression, classification, clustering, predictive data analysis and dimensionality reduction etc and known as the most robust and useful library for Machine Learning.
As we go further in the series, we will see how powerful and useful scikit learn is.
Goal
Let’s set a clear objective.
The goal is to develop an intuition and understand (in the depth) the practical side of Scikit learn and build projects.
I have created a GitHub repo for this series where we will be maintaining our code.
Tools
We will be using Google Colabs and Jupyter Notebooks.
Prerequisite to this series
Complete 60 days of Data Science and Machine Learning before starting this series ( link below) —
Let’s talk about what are we going to cover in this series —
We will be covering —
Supervised learning
Linear Models
Linear and Quadratic Discriminant Analysis
Support Vector Machines
Stochastic Gradient Descent
Nearest Neighbors
Gaussian Processes
Cross decomposition
Naive Bayes
Decision Trees
Ensemble methods
Feature selection
Semi-supervised learning
Neural network models
Unsupervised learning
Gaussian mixture models
Clustering
Biclustering
Matrix factorization
Covariance estimation
Outlier Detection
Density Estimation
Model selection and evaluation
Cross-validation
Hyper-parameters Tuning
Performance Metrics
Validation curves
Visualizations
Data viz using Matplotlib and seaborn
Dataset transformations
ML Pipelines
Feature extraction
Preprocessing data
Imputation of missing values
Dimensionality reduction
Kernel Approximation
Scikit Learn Projects ( 40)
scikit learn projects repo
That’s it for now. Day 2 : Coming soon!
Let me know if you have questions in the comment section below. Subscribe/ Follow, Like/Clap as it would encourage me to write more in my free time
Stay Tuned and Keep coding!!
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If you are interested, you can join my newsletter through which I send tech interview tips, techniques, patterns, hacks — Software Development, ML, Data Science, Startups and Technology projects to more than 30K readers. You can subscribe to Tech Brew :
For Python Projects —
For complete 60 days of Data Science and ML : Day 1 — Day 60 : Quick Recap of 60 days of Data Science and ML
Follow for more updates.
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