21-Days-Of-Machine-Learning

Day: 1

Introduction of Data Science

https://www.youtube.com/watch?v=edZ_JYpOM8U (30 Mins)

https://intellipaat.com/blog/what-is-data-science/ (Blog)

https://medium.com/@rathi.ankit/data-science-introduction-e03773919c6 (Blog)

https://towardsdatascience.com/intro-to-data-science-531079c38b22 (Blog)

Application in Day to Day Life

https://www.youtube.com/watch?v=HKcO3-6TYr0 (6 mins)

https://www.geeksforgeeks.org/machine-learning-introduction/ (Blog)

https://medium.com/app-affairs/9-applications-of-machine-learning-from-day-to-day-life-112a47a429d0 (Blog)

https://data-flair.training/blogs/machine-learning-applications/ (Blog)

Day: 2 & 3

Python

https://www.youtube.com/watch?v=YfO28Ihehbk (7 Hrs)

https://docs.python.org/3/tutorial/ (Official Website)

https://www.tutorialspoint.com/python/index.htm (Blog)

Day: 4

Exercises in Python

https://www.w3resource.com/python-exercises/

https://www.hackerrank.com/domains/python

https://codingbat.com/python

https://www.practicepython.org/

https://pynative.com/python-exercises-with-solutions/

https://edabit.com/challenges/python3

https://exercism.io/tracks/python/exercises

https://programmingwithmosh.com/python/python-exercises-and-questions-for-beginners/

Installation of Anaconda Distribution and others

https://www.youtube.com/watch?v=5mDYijMfSzs (14 mins)

https://docs.anaconda.com/anaconda/install/ (Official Website)

https://www.programiz.com/python-programming/first-program (Blog)

Day: 5

Numpy

https://www.youtube.com/watch?v=QUT1VHiLmmI (1 Hrs)

https://docs.scipy.org/doc/numpy/user/basics.html (Official Website)

https://www.w3resource.com/python-exercises/numpy/index.php (Exercises)

https://www.machinelearningplus.com/python/101-numpy-exercises-python/ (Exercises)

Pandas

https://www.youtube.com/watch?v=ZyhVh-qRZPA&list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS (4 Hrs)

https://www.youtube.com/watch?v=eMOA1pPVUc4 (Project)

https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html (Official Website)

https://www.w3resource.com/python-exercises/pandas/index.php (Exercises)

Day: 6

Visualization

Day: 7 & 8

Maths

Linear algebra

Matrix

Statistics

https://www.mathsisfun.com/data/ (Blog)

https://www.youtube.com/watch?v=uhxtUt_-GyM&list=PL1328115D3D8A2566 (7 Hrs)

Day: 9

Machine Learning and Types

https://www.youtube.com/watch?v=HcqpanDadyQ (6 Mins)

https://www.youtube.com/watch?v=xtOg44r6dsE (20 Mins)

https://towardsdatascience.com/machine-learning-an-introduction-23b84d51e6d0 (Blog)

Exploratory Data Analysis

https://www.youtube.com/watch?v=5NcbVYhQJvw (45 Mins)

https://towardsdatascience.com/exploratory-data-analysis-8fc1cb20fd15 (Blog)

Data Extraction

https://towardsdatascience.com/extracting-data-for-machine-learning-f90b97a97f4c (Blog)

Data Cleansing and Transformation

https://towardsdatascience.com/the-ultimate-guide-to-data-cleaning-3969843991d4 (Blog)

https://towardsdatascience.com/data-preparation-for-machine-learning-cleansing-transformation-feature-engineering-d2334079b06d (Blog)

https://pediaa.com/difference-between-data-cleansing-and-data-transformation/ (Blog)

Data Preparation

https://www.youtube.com/watch?v=Zi-0rlM4RDs (7 Mins)

https://machinelearningmastery.com/difference-test-validation-datasets/ (Blog)

https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7 (Blog)

Day: 10

Model

https://www.youtube.com/watch?v=60oFFvywKrQ (12 Mins)

Types of Model

https://machinelearningmastery.com/types-of-learning-in-machine-learning/ (Blog)

https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861 (Blog)

Regression

https://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/ (Blog)

https://www.geeksforgeeks.org/regression-classification-supervised-machine-learning/ (Blog)

Linear Regression

https://www.youtube.com/watch?v=E5RjzSK0fvY (30 Mins)

https://towardsdatascience.com/introduction-to-machine-learning-algorithms-linear-regression-14c4e325882a (Blog)

https://machinelearningmastery.com/linear-regression-for-machine-learning/ (Blog)

https://towardsdatascience.com/understanding-the-ols-method-for-simple-linear-regression-e0a4e8f692cc (Blog)

Bias and Variance

https://www.youtube.com/watch?v=EuBBz3bI-aA (7 Mins)

https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229 (Blog)

https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/ (Blog)

Overfitting and Underfitting

https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/ (Blog)

MSE,MAE, R-Square

https://medium.com/@george.drakos62/how-to-select-the-right-evaluation-metric-for-machine-learning-models-part-1-regrression-metrics-3606e25beae0 (Blog)

Day: 11

Polynomial Regression

https://www.youtube.com/watch?v=Qnt2vBRW8Io (6 Mins)

https://towardsdatascience.com/polynomial-regression-bbe8b9d97491 (Blog)

Ridge Regression

https://www.youtube.com/watch?v=Q81RR3yKn30 (30 Mins)

https://towardsdatascience.com/ridge-regression-for-better-usage-2f19b3a202db (Blog)

Lasso Regression

https://www.youtube.com/watch?v=NGf0voTMlcs (9 Mins)

https://towardsdatascience.com/ridge-and-lasso-regression-a-complete-guide-with-python-scikit-learn-e20e34bcbf0b (Blog)

Elastic Net Regression

https://www.youtube.com/watch?v=1dKRdX9bfIo (6 Mins)

https://hackernoon.com/an-introduction-to-ridge-lasso-and-elastic-net-regression-cca60b4b934f (Blog)

https://www.datacamp.com/community/tutorials/tutorial-ridge-lasso-elastic-net (Blog)

Day: 12

Support Vector Regression(SVR)

https://www.youtube.com/watch?v=Fq_mwvdzzjI (10 Mins)

https://towardsdatascience.com/an-introduction-to-support-vector-regression-svr-a3ebc1672c2 (Blog)

Decision Tree Regression

https://www.youtube.com/watch?v=g9c66TUylZ4 (23 Mins)

https://towardsdatascience.com/decision-tree-algorithm-explained-83beb6e78ef4 (Blog)

https://medium.com/pursuitnotes/decision-tree-regression-in-6-steps-with-python-1a1c5aa2ee16 (Blog)

Day: 13

Random Forest Regression

https://www.youtube.com/watch?v=D_2LkhMJcfY (8 Mins)

https://www.geeksforgeeks.org/random-forest-regression-in-python/ (Blog)

https://towardsdatascience.com/random-forest-and-its-implementation-71824ced454f (Blog)

Ensemble Method

https://towardsdatascience.com/ensemble-methods-in-machine-learning-what-are-they-and-why-use-them-68ec3f9fef5f (Blog)

https://blog.statsbot.co/ensemble-learning-d1dcd548e936 (Blog)

Bagging and Boosting

https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205 (Blog)

Bootstrap

https://machinelearningmastery.com/a-gentle-introduction-to-the-bootstrap-method/ (Blog)

https://towardsdatascience.com/an-introduction-to-the-bootstrap-method-58bcb51b4d60 (Blog)

Day: 14

Classification

https://www.youtube.com/watch?v=pXdum128xww (34 Mins)

https://medium.com/datadriveninvestor/classification-algorithms-in-machine-learning-85c0ab65ff4 (Blog)

https://medium.com/@Mandysidana/machine-learning-types-of-classification-9497bd4f2e14 (Blog)

Logistic Regression

https://www.youtube.com/watch?v=VCJdg7YBbAQ (1 Hrs)

https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc (Blog)

https://machinelearningmastery.com/logistic-regression-for-machine-learning/ (Blog)

K-Nearest Neighbors (K-NN)

https://www.youtube.com/watch?v=HVXime0nQeI (6 Mins)

https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761 (Blog)

https://www.geeksforgeeks.org/k-nearest-neighbours/ (Blog)

https://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/ (Blog)

Precision and Recall

https://towardsdatascience.com/beyond-accuracy-precision-and-recall-3da06bea9f6c (Blog)

https://towardsdatascience.com/precision-vs-recall-386cf9f89488 (Blog)

https://machinelearningmastery.com/precision-recall-and-f-measure-for-imbalanced-classification/ (Blog)

F-Score

https://towardsdatascience.com/accuracy-recall-precision-f-score-specificity-which-to-optimize-on-867d3f11124 (Blog)

AUC & ROC

https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5 (Blog)

https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/ (Blog)

Day: 15

Support Vector Machine (SVM)

https://www.youtube.com/watch?v=efR1C6CvhmE&t=897s (21 Mins)

https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47 (Blog)

Naive Bayes

https://www.youtube.com/watch?v=l3dZ6ZNFjo0 (45 Mins)

https://towardsdatascience.com/naive-bayes-classifier-81d512f50a7c (Blog)

Principal Component Analysis (PCA)

https://www.youtube.com/watch?v=FgakZw6K1QQ (22 Mins)

https://towardsdatascience.com/a-one-stop-shop-for-principal-component-analysis-5582fb7e0a9c (Blog)

Cross Validation

https://www.youtube.com/watch?v=7062skdX05Y (17 Mins)

https://towardsdatascience.com/cross-validation-in-machine-learning-72924a69872f (Blog)

https://machinelearningmastery.com/k-fold-cross-validation/ (Blog)

Grid Search

https://www.youtube.com/watch?v=HdlDYng8g9s (17 Mins)

https://www.youtube.com/watch?v=Ah4wsTXghwI (8 Mins)

https://towardsdatascience.com/grid-search-for-model-tuning-3319b259367e (Blog)

https://www.datacamp.com/community/tutorials/parameter-optimization-machine-learning-models (Blog)

UnderSampling and OverSampling

https://machinelearningmastery.com/random-oversampling-and-undersampling-for-imbalanced-classification/ (Blog)

https://machinelearningmastery.com/combine-oversampling-and-undersampling-for-imbalanced-classification/ (Blog)

https://www.kaggle.com/residentmario/undersampling-and-oversampling-imbalanced-data (Blog)

Balanced and InBalanced Data

https://medium.com/analytics-vidhya/what-is-balance-and-imbalance-dataset-89e8d7f46bc5 (Blog)

https://www.analyticsvidhya.com/blog/2017/03/imbalanced-data-classification/ (Blog)

https://www.kdnuggets.com/2017/06/7-techniques-handle-imbalanced-data.html (Blog)

Day: 16

Decision Tree Classification

https://www.youtube.com/watch?v=7VeUPuFGJHk (20 Mins)

https://towardsdatascience.com/decision-tree-classification-de64fc4d5aac (Blog)

https://www.datacamp.com/community/tutorials/decision-tree-classification-python (Blog)

Random Forest Classification

https://www.youtube.com/watch?v=D_2LkhMJcfY (8 Mins)

https://towardsdatascience.com/random-forest-classification-and-its-implementation-d5d840dbead0 (Blog)

https://towardsdatascience.com/understanding-random-forest-58381e0602d2 (Blog)

Out of Bag (OOB)

https://towardsdatascience.com/what-is-out-of-bag-oob-score-in-random-forest-a7fa23d710 (Blog)

Confusion Matrix

https://www.youtube.com/watch?v=8Oog7TXHvFY (36 Mins)

https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62 (Blog)

Day: 17

Clustering

https://www.youtube.com/watch?v=UhVn2WrzMnI (24 Mins)

https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/ (Blog)

https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 (Blog)

K-Means Clustering

https://www.youtube.com/watch?v=4b5d3muPQmA (8 Mins)

https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1 (Blog)

https://towardsdatascience.com/k-means-clustering-algorithm-applications-evaluation-methods-and-drawbacks-aa03e644b48a (Blog)

Hierarchical Clustering

https://www.youtube.com/watch?v=7xHsRkOdVwo (12 Mins)

https://towardsdatascience.com/understanding-the-concept-of-hierarchical-clustering-technique-c6e8243758ec (Blog)

Day: 18

NLP

https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL (3 Hrs)

https://towardsdatascience.com/your-guide-to-natural-language-processing-nlp-48ea2511f6e1 (Blog)

Day: 19

XGBoost

https://www.youtube.com/watch?v=kho6oANGu_A (22 Mins)

https://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/ (Blog)

https://towardsdatascience.com/https-medium-com-vishalmorde-xgboost-algorithm-long-she-may-rein-edd9f99be63d (Blog)

Day: 20

Deploy Model

https://www.youtube.com/watch?v=-UYyyeYJAoQ (36 Mins)

https://towardsdatascience.com/simple-way-to-deploy-machine-learning-models-to-cloud-fd58b771fdcf (Blog)

https://towardsdatascience.com/a-simple-way-to-deploy-any-machine-learning-model-106d463e9a4b (Blog)

https://www.kdnuggets.com/2019/06/approaches-deploying-machine-learning-production.html (Blog)

https://www.analyticsvidhya.com/blog/2017/09/machine-learning-models-as-apis-using-flask/ (Blog)

Day: 21

Projects

https://elitedatascience.com/machine-learning-projects-for-beginners

https://data-flair.training/blogs/machine-learning-project-ideas/

https://www.dezyre.com/article/top-10-machine-learning-projects-for-beginners/397

https://www.ubuntupit.com/top-20-best-machine-learning-projects-for-beginner-to-professional/

https://www.simplilearn.com/machine-learning-projects-for-beginners-article

Interview Preparation

https://github.com/iNeuronai/interview-question-data-science-

Learn More

Deep Learning

Tableau

Flask

https://www.youtube.com/watch?v=MwZwr5Tvyxo&list=PL-osiE80TeTs4UjLw5MM6OjgkjFeUxCYH (10 Hrs)

https://palletsprojects.com/p/flask/ (Blog)

https://www.fullstackpython.com/flask.html (Blog)

joskid / 21-days-of-machine-learning Goto Github PK

21-days-of-machine-learning's Introduction

21-Days-Of-Machine-Learning

Day: 1

Introduction of Data Science

Application in Day to Day Life

Day: 2 & 3

Python

Day: 4

Exercises in Python

Installation of Anaconda Distribution and others

Day: 5

Numpy

Pandas

Day: 6

Visualization

Matplotlib

Seaborn

Plotly

Day: 7 & 8

Maths

Linear algebra

Matrix

Statistics

Day: 9

Machine Learning and Types

Exploratory Data Analysis

Data Extraction

Data Cleansing and Transformation

Data Preparation

Day: 10

Model

Types of Model

Regression

Linear Regression

Bias and Variance

Overfitting and Underfitting

MSE,MAE, R-Square

Day: 11

Polynomial Regression

Ridge Regression

Lasso Regression

Elastic Net Regression

Day: 12

Support Vector Regression(SVR)

Decision Tree Regression

Day: 13

Random Forest Regression

Ensemble Method

Bagging and Boosting

Bootstrap

Day: 14

Classification

Logistic Regression

K-Nearest Neighbors (K-NN)

Precision and Recall

F-Score

AUC & ROC

Day: 15

Support Vector Machine (SVM)

Naive Bayes

Principal Component Analysis (PCA)

Cross Validation

Grid Search

UnderSampling and OverSampling

Balanced and InBalanced Data

Day: 16

Decision Tree Classification

Random Forest Classification

Out of Bag (OOB)

Confusion Matrix

Day: 17

Clustering

K-Means Clustering

Hierarchical Clustering

Day: 18

NLP

Day: 19

XGBoost

Day: 20