https://github.com/datawithsolu/21-Days-Of-Machine-Learning
A structured 21-day guide with curated resources, including videos, blogs, and documentation, to master machine learning, data science, and Python tools.
https://github.com/datawithsolu/21-Days-Of-Machine-Learning
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A structured 21-day guide with curated resources, including videos, blogs, and documentation, to master machine learning, data science, and Python tools.
- Host: GitHub
- URL: https://github.com/datawithsolu/21-Days-Of-Machine-Learning
- Owner: datawithsolu
- Created: 2020-03-17T16:48:57.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-08-15T09:55:07.000Z (10 months ago)
- Last Synced: 2024-11-18T03:37:12.454Z (7 months ago)
- Homepage:
- Size: 8.79 KB
- Stars: 38
- Watchers: 5
- Forks: 18
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 21-Days-Of-Machine-Learning
## Day 1: Introduction to Data Science
### Resources:
- [YouTube Video: Introduction to Data Science (30 mins)](https://www.youtube.com/watch?v=edZ_JYpOM8U)
- [Intellipaat Blog: What is Data Science?](https://intellipaat.com/blog/what-is-data-science/)
- [Medium Blog: Data Science Introduction by Ankit Rathi](https://medium.com/@rathi.ankit/data-science-introduction-e03773919c6)
- [Towards Data Science Blog: Intro to Data Science](https://towardsdatascience.com/intro-to-data-science-531079c38b22)
## Application in Day-to-Day Life
### Resources:
- [YouTube Video: Machine Learning in Day-to-Day Life (6 mins)](https://www.youtube.com/watch?v=HKcO3-6TYr0)
- [GeeksforGeeks Blog: Machine Learning Introduction](https://www.geeksforgeeks.org/machine-learning-introduction/)
- [Medium Blog: 9 Applications of Machine Learning in Day-to-Day Life](https://medium.com/app-affairs/9-applications-of-machine-learning-from-day-to-day-life-112a47a429d0)
- [DataFlair Blog: Machine Learning Applications](https://data-flair.training/blogs/machine-learning-applications/)
## Day 2 & 3: Python
### Resources:
- [YouTube Video: Python for Beginners (7 hrs)](https://www.youtube.com/watch?v=YfO28Ihehbk)
- [Official Python Documentation](https://docs.python.org/3/tutorial/)
- [TutorialsPoint Blog: Python](https://www.tutorialspoint.com/python/index.htm)
## Day 4: Exercises in Python
### Resources:
- [W3Resource: Python Exercises](https://www.w3resource.com/python-exercises/)
- [HackerRank: Python Domain](https://www.hackerrank.com/domains/python)
- [CodingBat: Python](https://codingbat.com/python)
- [Practice Python](https://www.practicepython.org/)
- [Pynative: Python Exercises with Solutions](https://pynative.com/python-exercises-with-solutions/)
- [Edabit: Python Challenges](https://edabit.com/challenges/python3)
- [Exercism: Python Exercises](https://exercism.io/tracks/python/exercises)
- [Programming with Mosh: Python Exercises and Questions for Beginners](https://programmingwithmosh.com/python/python-exercises-and-questions-for-beginners/)
### Installation of Anaconda Distribution and Others:
- [YouTube Video: Installing Anaconda (14 mins)](https://www.youtube.com/watch?v=5mDYijMfSzs)
- [Official Anaconda Installation Documentation](https://docs.anaconda.com/anaconda/install/)
- [Programiz Blog: Python First Program](https://www.programiz.com/python-programming/first-program)
## Day 5: Numpy
### Resources:
- [YouTube Video: Numpy Tutorial (1 hr)](https://www.youtube.com/watch?v=QUT1VHiLmmI)
- [Official Numpy Basics Documentation](https://docs.scipy.org/doc/numpy/user/basics.html)
- [W3Resource: Numpy Exercises](https://www.w3resource.com/python-exercises/numpy/index.php)
- [Machine Learning Plus: 101 Numpy Exercises](https://www.machinelearningplus.com/python/101-numpy-exercises-python/)
## Day 5: Pandas
### Resources:
- [YouTube Video: Pandas Tutorial (4 hrs)](https://www.youtube.com/watch?v=ZyhVh-qRZPA&list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS)
- [YouTube Video: Pandas Project](https://www.youtube.com/watch?v=eMOA1pPVUc4)
- [Official Pandas Documentation](https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html)
- [W3Resource: Pandas Exercises](https://www.w3resource.com/python-exercises/pandas/index.php)
## Day 6: Visualization
### Matplotlib
### Resources:
- [YouTube Video: Matplotlib Tutorial (3 hrs)](https://www.youtube.com/watch?v=UO98lJQ3QGI&list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_)
- [Official Matplotlib Documentation](https://matplotlib.org/tutorials/index.html)
- [Machine Learning Plus: Complete Guide to Matplotlib](https://www.machinelearningplus.com/plots/matplotlib-tutorial-complete-guide-python-plot-examples/)
### Seaborn
### Resources:
- [YouTube Video: Seaborn Tutorial (2.5 hrs)](https://www.youtube.com/watch?v=GcXcSZ0gQps)
- [Official Seaborn Documentation](https://seaborn.pydata.org/introduction.html)
- [Elite Data Science: Seaborn Tutorial](https://elitedatascience.com/python-seaborn-tutorial)
### Plotly
### Resources:
- [YouTube Video: Plotly Tutorial (2 hrs)](https://www.youtube.com/watch?v=NPznsxeL3FM&list=PLH6mU1kedUy9HTC1n9QYtVHmJRHQ97DBa)
- [Official Plotly Documentation](https://plotly.com/python/)
## Day 7 & 8: Mathematics
### Topics:
- Linear Algebra
- Matrix
- Statistics
### Resources:
- [Math is Fun: Data Blog](https://www.mathsisfun.com/data/)
- [YouTube Playlist: Mathematics for Data Science (7 hrs)](https://www.youtube.com/watch?v=uhxtUt_-GyM&list=PL1328115D3D8A2566)
## Day 9: Machine Learning Basics
### Machine Learning and Types
### Resources:
- [YouTube Video: What is Machine Learning? (6 mins)](https://www.youtube.com/watch?v=HcqpanDadyQ)
- [YouTube Video: Types of Machine Learning (20 mins)](https://www.youtube.com/watch?v=xtOg44r6dsE)
- [Towards Data Science Blog: Machine Learning Introduction](https://towardsdatascience.com/machine-learning-an-introduction-23b84d51e6d0)
### Exploratory Data Analysis (EDA)
### Resources:
- [YouTube Video: Exploratory Data Analysis (45 mins)](https://www.youtube.com/watch?v=5NcbVYhQJvw)
- [Towards Data Science Blog: EDA Guide](https://towardsdatascience.com/exploratory-data-analysis-8fc1cb20fd15)
### Data Extraction
### Resources:
- [Towards Data Science Blog: Extracting Data for Machine Learning](https://towardsdatascience.com/extracting-data-for-machine-learning-f90b97a97f4c)
### Data Cleansing and Transformation
### Resources:
- [Towards Data Science Blog: The Ultimate Guide to Data Cleaning](https://towardsdatascience.com/the-ultimate-guide-to-data-cleaning-3969843991d4)
- [Towards Data Science Blog: Data Preparation for Machine Learning](https://towardsdatascience.com/data-preparation-for-machine-learning-cleansing-transformation-feature-engineering-d2334079b06d)
- [Pediaa Blog: Difference Between Data Cleansing and Data Transformation](https://pediaa.com/difference-between-data-cleansing-and-data-transformation/)
### Data Preparation
### Resources:
- [YouTube Video: Data Preparation (7 mins)](https://www.youtube.com/watch?v=Zi-0rlM4RDs)
- [Machine Learning Mastery Blog: Test, Validation, and Training Datasets](https://machinelearningmastery.com/difference-test-validation-datasets/)
- [Towards Data Science Blog: Train, Validation, and Test Sets](https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7)
## Day 10: Model Building
### Model
### Resources:
- [YouTube Video: What is a Model? (12 mins)](https://www.youtube.com/watch?v=60oFFvywKrQ)
### Types of Models
### Resources:
- [Machine Learning Mastery Blog: Types of Learning in Machine Learning](https://machinelearningmastery.com/types-of-learning-in-machine-learning/)
- [Towards Data Science Blog: Types of Machine Learning Algorithms](https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861)
### Regression
### Resources:
- [Analytics Vidhya Blog: Comprehensive Guide to Regression](https://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/)
- [GeeksforGeeks Blog: Regression and Classification in Machine Learning](https://www.geeksforgeeks.org/regression-classification-supervised-machine-learning/)
### Linear Regression
### Resources:
- [YouTube Video: Linear Regression (30 mins)](https://www.youtube.com/watch?v=E5RjzSK0fvY)
- [Towards Data Science Blog: Introduction to Linear Regression](https://towardsdatascience.com/introduction-to-machine-learning-algorithms-linear-regression-14c4e325882a)
- [Machine Learning Mastery Blog: Linear Regression for Machine Learning](https://machinelearningmastery.com/linear-regression-for-machine-learning/)
### Logistic Regression
### Resources:
- [YouTube Video: Logistic Regression (30 mins)](https://www.youtube.com/watch?v=zAULhNrnuL4)
- [Towards Data Science Blog: Logistic Regression Explained](https://towardsdatascience.com/logistic-regression-explained-9a4f25c21c96)
## Day 11: Mathematics for Machine Learning
### Topics:
- Linear Algebra
- Matrix
- Statistics
### Resources:
- [Math is Fun: Data Blog](https://www.mathsisfun.com/data/)
- [YouTube Playlist: Mathematics for Data Science (7 hrs)](https://www.youtube.com/watch?v=uhxtUt_-GyM&list=PL1328115D3D8A2566)
## Day 12: Supervised and Unsupervised Learning
### Resources:
- [YouTube Video: Supervised Learning (10 mins)](https://www.youtube.com/watch?v=FnB-oJY7tB0)
- [YouTube Video: Unsupervised Learning (12 mins)](https://www.youtube.com/watch?v=UsVsVxnFQEM)
- [YouTube Video: Supervised and Unsupervised Learning (10 mins)](https://www.youtube.com/watch?v=8Wu2B_TzHgo)
- [Towards Data Science Blog: Supervised vs Unsupervised Learning](https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d)
- [GeeksforGeeks Blog: Supervised Learning](https://www.geeksforgeeks.org/supervised-unsupervised-learning/)
## Day 13: Overfitting and Underfitting
### Resources:
- [YouTube Video: Overfitting and Underfitting (9 mins)](https://www.youtube.com/watch?v=Yc6xF1g1KaM)
- [Towards Data Science Blog: Overfitting vs Underfitting](https://towardsdatascience.com/overfitting-vs-underfitting-a-complete-example-53aa59f25538)
- [Machine Learning Mastery Blog: Avoid Overfitting in Machine Learning](https://machinelearningmastery.com/avoid-overfitting-by-early-stopping-with-xgboost-in-python/)
- [GeeksforGeeks Blog: Overfitting and Underfitting](https://www.geeksforgeeks.org/underfitting-and-overfitting-in-machine-learning/)
## Day 14: Training, Testing, and Validation
### Resources:
- [YouTube Video: Train, Test, and Validation Sets (6 mins)](https://www.youtube.com/watch?v=5VRiGlmRzZU)
- [Machine Learning Mastery Blog: Test, Validation, and Training Datasets](https://machinelearningmastery.com/difference-test-validation-datasets/)
- [Towards Data Science Blog: Train, Validation, and Test Sets](https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7)
## Day 15: Bias and Variance
### Resources:
- [YouTube Video: Bias and Variance (10 mins)](https://www.youtube.com/watch?v=EuBBz3bI-aA)
- [Towards Data Science Blog: Bias vs Variance](https://towardsdatascience.com/bias-vs-variance-5ce26a17f000)
- [GeeksforGeeks Blog: Bias-Variance Tradeoff](https://www.geeksforgeeks.org/bias-variance-trade-off-in-machine-learning/)
- [Analytics Vidhya Blog: Bias-Variance Tradeoff](https://www.analyticsvidhya.com/blog/2020/08/bias-variance-tradeoff-machine-learning/)
## Day 16: Error Metrics
### Resources:
- [YouTube Video: Error Metrics (12 mins)](https://www.youtube.com/watch?v=_2wjKueRDTs)
- [Towards Data Science Blog: Classification Metrics for Machine Learning](https://towardsdatascience.com/choosing-the-right-metric-for-evaluating-machine-learning-models-part-2-86d5649a5428)
- [GeeksforGeeks Blog: Different Types of Error Metrics in Machine Learning](https://www.geeksforgeeks.org/different-types-of-error-metrics-in-machine-learning/)
## Day 17: K-Nearest Neighbors (KNN)
### Resources:
- [YouTube Video: K-Nearest Neighbors (12 mins)](https://www.youtube.com/watch?v=4HKqjENq9OU)
- [Towards Data Science Blog: KNN Algorithm](https://towardsdatascience.com/knn-k-nearest-neighbors-a2fa87c71dc7)
- [Analytics Vidhya Blog: K-Nearest Neighbors Algorithm](https://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/)
## Day 18: Support Vector Machine (SVM)
### Resources:
- [YouTube Video: Support Vector Machine (SVM) (15 mins)](https://www.youtube.com/watch?v=efR1C6CvhmE)
- [Towards Data Science Blog: Support Vector Machine](https://towardsdatascience.com/support-vector-machines-are-very-powerful-9c748fdd0af7)
- [Analytics Vidhya Blog: SVM Guide](https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/)
## Day 19: Decision Tree and Random Forest
### Resources:
- [YouTube Video: Decision Tree (15 mins)](https://www.youtube.com/watch?v=eKD5gxPPeY0)
- [YouTube Video: Random Forest (12 mins)](https://www.youtube.com/watch?v=J4Wdy0Wc_xQ)
- [Towards Data Science Blog: Decision Trees vs Random Forest](https://towardsdatascience.com/decision-trees-and-random-forests-5e1a098d1cf1)
- [GeeksforGeeks Blog: Decision Tree and Random Forest](https://www.geeksforgeeks.org/decision-tree-and-random-forest-in-python/)
## Day 20: Unsupervised Learning - Clustering
### Resources:
- [YouTube Video: K-Means Clustering (10 mins)](https://www.youtube.com/watch?v=4b5d3muPQmA)
- [Towards Data Science Blog: Clustering](https://towardsdatascience.com/unsupervised-learning-with-python-173c51dc7f03)
- [GeeksforGeeks Blog: Clustering Algorithms](https://www.geeksforgeeks.org/clustering-algorithms/)
- [Machine Learning Plus Blog: Clustering in Python](https://www.machinelearningplus.com/statistics/clustering-in-python/)
## Day 21: Final Project
### Project: End-to-End Machine Learning Project
### Resources:
- [YouTube Video: End-to-End Machine Learning Project (4 hrs)](https://www.youtube.com/watch?v=0ct7r3pZwpA)
- [Towards Data Science Blog: End-to-End Machine Learning Project](https://towardsdatascience.com/end-to-end-machine-learning-project-5cf4bc3e4b68)
- [Kaggle: Titanic Dataset](https://www.kaggle.com/c/titanic/data)
- [GitHub: Machine Learning Projects](https://github.com/topics/machine-learning-project)