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Title: GitHub - albahnsen/PracticalMachineLearningClass

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Description: Contribute to albahnsen/PracticalMachineLearningClass development by creating an account on GitHub.

Open Graph Description: Contribute to albahnsen/PracticalMachineLearningClass development by creating an account on GitHub.

X Description: Contribute to albahnsen/PracticalMachineLearningClass development by creating an account on GitHub.

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1 - Intro to MLhttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/01-IntroMachineLearning.ipynb
2 - Intro to Python for data analysishttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/02-IntroPython_Numpy_Scypy_Pandas.ipynb
E1 - Data Science Overviewhttps://github.com/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E1-DataScienceOverview
E2 - Python Text Analysishttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E2-%20Python%20Text%20Analysis.ipynb
3 - Linear Regressionhttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/03-linear_regression.ipynb
4 - Logistic Regressionhttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/04-logistic_regression.ipynb
E3 - Supervised vs Unsupervised Overviewhttps://github.com/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E3-LearningOverview.md
E4 - Linear Modelshttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E4-Regression-Linear%26Logistic.ipynb
5 - SVMhttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/05-SVM.ipynb
6 - Decision Tresshttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/06-decision_trees.ipynb
7 - Regularizationhttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/07-regularization.ipynb
E5 - Decision Trees Overviewhttps://github.com/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E5-DecisionTreesOverview.md
E6 - SVM - Regularizationhttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E6-SVM%26Regularization.ipynb
E7 - Decision Treeshttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E7-DecisionTrees.ipynb
8 - Introduction to APIshttps://nbviewer.jupyter.org/format/slides/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/08-IntroductionToAPIs.ipynb#/
9 - Model Deploymenthttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/09-Model_Deployment.ipynb
10 - Creating APIs in AWShttps://nbviewer.jupyter.org/format/slides/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/10-CreatingAPIinAWS.ipynb#/
P1 - Survival Prediction APIhttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/P1-UsedVehiclePricePrediction.ipynb
11 - Bagginghttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/11-Ensembles_Bagging.ipynb
12 - Boostinghttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/12-Ensembles_Boosting.ipynb
E8 - Best Ensemble Overviewhttps://github.com/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E8-EnsembleTreesOverview.md
E9 - Bagginghttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E9-Bagging.ipynb
13 - Random Foresthttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/13-Ensembles_RandomForest.ipynb
E10 - Random Forest Performance Reviewhttps://github.com/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E10-RandomForestPerformanceReview.md
E11 - Random Foresthttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E11-RandomForest.ipynb
14 - Feature Engineeringhttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/14-data_preparation.ipynb
E12 - Gradient Boosting Reviewhttps://github.com/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E12-GradientBoostingRewiew.md
E13 - Categorical Encoding https://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E13-CategoricalEncoding.ipynb
15 - Unbalanced Learninghttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/15-Unbalanced_Datasets.ipynb
E14 - Unbalanced Learning Overviewhttps://github.com/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E14-UnbalancedLearningOverview.md
E15 - Fraud Detectionhttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E15-fraud_detection.ipynb
https://patch-diff.githubusercontent.com/albahnsen/PracticalMachineLearningClass#natural-language-processing
16 - Introduction to NLPhttps://github.com/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/16-IntroNLP.pdf
17 - Introduction to NLP II https://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/17-NaturalLanguageProcessing.ipynb
E16 - NLP Reviewhttps://github.com/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E16-NLPOverview.md
E17 - Sentiment Analysishttps://github.com/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E17-SentimentPrediction.ipynb
P2 - NLP Movies Analysis APIhttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/P2-MovieGenrePrediction.ipynb
18 - Sentiment Analysishttps://github.com/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/18-IntroNLP_II.pdf
E18 - Homeworks Analysishttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E18-ClassHomeworksAnalysis.ipynb
https://patch-diff.githubusercontent.com/albahnsen/PracticalMachineLearningClass#advanced-topics-in-machine-learning
19 - Introduction to Neural Networks - MLP https://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/19-IntroductionDeepLearningMLP.ipynb
20 - Deep Learning in kerashttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/notebooks/20-DeepLearning_keras.ipynb
E19 - Neural Networks Fundamentationhttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E19-NeuralNetworksFundamentation.ipynb
E20 - Neural Networks in Kerashttps://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/E20-NeuralNetworksKeras.ipynb
20 - LSTMhttps://patch-diff.githubusercontent.com/albahnsen/PracticalMachineLearningClass/blob/master
21 - CNNhttps://patch-diff.githubusercontent.com/albahnsen/PracticalMachineLearningClass/blob/master
https://patch-diff.githubusercontent.com/albahnsen/PracticalMachineLearningClass/blob/master
https://patch-diff.githubusercontent.com/albahnsen/PracticalMachineLearningClass#final-project-presentation
P3 - Kaggle Competitionhttps://patch-diff.githubusercontent.com/albahnsen/PracticalMachineLearningClass/blob/master
Readme https://patch-diff.githubusercontent.com/albahnsen/PracticalMachineLearningClass#readme-ov-file
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