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CS2008-machine-learning-practice

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Repository containing introduction to scikit-learn to provide hands-on problem solving experience for all the methods and models learnt in MLT.

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##  Machine-Learning Practice

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### Course structure

[WEEK 1](https://github.com/faizanxmulla/machine-learning-practice/tree/main/W1%20-%20Introduction) - 	End-to-end machine learning project on scikit-learn

[WEEK 2](https://github.com/faizanxmulla/machine-learning-practice/tree/main/W2%20-%20Data%20Preprocessing) - 	End-to-end machine learning project on scikit-learn (continued)

[WEEK 3](https://github.com/faizanxmulla/machine-learning-practice/tree/main/W3%20-%20Regression) - 	Regression on scikit-learn - Linear regression Gradient-descent- Batch (MBGD) and Stochastic (SGD).

[WEEK 4](https://github.com/faizanxmulla/machine-learning-practice/tree/main/W4%20-%20Regression%20(cont.)) - 	Polynomial regression, Regularized models

[WEEK 5](https://github.com/faizanxmulla/machine-learning-practice/tree/main/W5%20-%20Classification) - 	Logistic regression

[WEEK 6](https://github.com/faizanxmulla/machine-learning-practice/tree/main/W6%20-%20Classification%20(cont.)) - 	Classification on scikit-learn - Binary classifier

[WEEK 7](https://github.com/faizanxmulla/machine-learning-practice/tree/main/W7%20-%20Softmax%20Regression-KNN) - 	Classification on scikit-learn - Multiclass classifier

[WEEK 8](https://github.com/faizanxmulla/machine-learning-practice/tree/main/W8%20-%20SVM) - 	Support Vector Machines using scikit-learn

[WEEK 9](https://github.com/faizanxmulla/machine-learning-practice/tree/main/W9%20-%20Decision%20Trees) - 	Decision Trees using scikit-learn

[WEEK 10](https://github.com/faizanxmulla/machine-learning-practice/tree/main/W10%20-%20Ensemble-RandomForests) - 	Ensemble Learning and Random Forests using scikit-learn

[WEEK 11](https://github.com/faizanxmulla/machine-learning-practice/tree/main/W11%20-%20Clustering) - 	Clustering using scikit-learn

[WEEK 12](https://github.com/faizanxmulla/machine-learning-practice/tree/main/W12%20-%20Neural%20Networks) - 	Neural networks models in scikit-learn

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### What you’ll learn

* Understand the life cycle of a machine learning project - typical steps involved and tools that can be used in each step.

* Using machine learning algorithms to solve practical problems using libraries like scikit-learn and tensorflow.

* Fine tuning the algorithms through regularization, feature selection, and better models.

* Develop an understanding of evaluation of machine learning algorithms and decide the next steps based on the analysis.

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CS2008-machine-learning-practice

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