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  1. Chapter 1

   1. Course Overview - Machine Learning For Biology.srt

   10. 06. Tuples Examples.srt

   11. 07. Dictionaries Examples.srt

   12. 08. Ranges Examples.srt

   13. 09. Conditionals.srt

   14. 10. If Statement Examples.srt

   15. 11. Loops.srt

   16. 12. Functions.srt

   17. 13. Parameters And Return Values Examples.srt

   18. 14. Classes And Objects.srt

   19. 15. Inheritance Examples.srt

   2. What you'll need.srt

   20. 16. Static Members Examples.srt

   21. 17. Summary And Outro.srt

   23. What Is Machine Learning.mp4

   24. What Is Supervised Learning.mp4

   25. Source Files.html

   26. 01 Regression Introduction.srt

   27. 02 What Is Regression.srt

   28. 03 What Is Linear Regression.srt

   3. Source Files.html

   4. 00. Intro To Course And Python.srt

   5. 01. Variables.srt

   6. 02. Type Conversion Examples.srt

   7. 03. Operators.srt

   8. 04. Collections.srt

   9. 05. List Examples.srt

   Python_Language_Basics.ipynb

   Source Files

    01 What is Machine Learning.pptx

    Regression Slides.pptx

    Types of Machine Learning Models.pptx

    What is Linear Regression.pptx

    What is Supervised Learning.pptx

   Source files - Course overview

    00 Course Overview - Machine Learning for BIology.pptx

    01 What you'll need.pptx

  10. 13 Prepare heart disease data for machine learning

   1. 01 Load Data Via Data File.srt

   2. 02 Clean And Preprocess Heart Disease Data For Machine Learning.srt

   3. 03 Process Heart Disease Data For Machine Learning.srt

   4.1 01 Load and analyze blood cell data.py

   4.3 02 Clean and preprocess heart disease data for machine learning.py

   4.5 cleaned_heart_disease_data.csv

   4.7 processed.cleveland.data

  11. 14 Predict heart disease with machine learning

   04a What is stochastic gradient descent.pptx

   05a What is Ada Boost.pptx

   1. 04A What Is Stochastic Gradient Descent.srt

   2. 04B Build A Linear Classifier With Stochastic Gradient Descent.srt

   3. 05A What Is Ada Boost.srt

   4. 05B Build An Ada Boost Classifier.srt

   5. 06 Build A K Nearest Neighbors Machine Learning Model.srt

   6.1 04 Build a linear classifier with stochastic gradient descent.ipynb

   6.3 05 Build an Ada Boost classifier.ipynb

   6.5 06 Build a K Nearest Neighbors machine learning model.ipynb

   processed.cleveland.csv

  12. 15 Deep learning and neural networks introduction

   1. 01 What Is Deep Learning.srt

   2. 02 What Is A Neural Network.srt

  13. 16 Build a neural network to find malaria in cells

   1. 00 Project Preview.srt

   2. 01 Load Data Via Tensorflow.srt

   3. 02 Visualize Malaria Cell Images.srt

   4. 03 Extract A Subset Of Samples.srt

   5. 04 Build A Neural Network.srt

   6. 05 Train And Evaluate Model Accuracy.srt

  2. 05 Build a K Nearest neighbors regression model to predict diabetes

   1. 00 Project Preview.srt

   2. 01 Load And Analyze Data.srt

   3. 01 What Is K Nearest Neighbors.srt

   4. 02 Build A K Nearest Neighbors Regression Model To Predict Diabetes.srt

   5.1 01 Load and analyze data.ipynb

   5.3 02 Build a K Nearest neighbors regression model to predict diabetes.ipynb

   5.5 Diabetes dataset visual.png

   5.7 What is K Nearest Neighbours.pptx

  3. 06 Build Regression Machine Learning Models to Detect Diabetes

   1. 03A What Is The Random Forest Classifier Model.srt

   2. 03B Build More Regression Models And Find The Best One.srt

   3. 04 Select Top Features Via Variance Threshold.srt

   4. 05 Visualize Linear Regression With Matplotlib Pyplot.srt

   5.1 03 Build more regression models and find the best one.ipynb

   5.3 04 Select top features via variance threshold.ipynb

   5.5 05 Visualize linear regression with Matplotlib Pyplot.ipynb

   5.7 Linear regression results.png

   5.9 What is the Random Forest Classifier Model (1).pptx

  4. 07 Data analysis and transformation on blood cell data

   1. 00 Project Preview.srt

   2. 01 Load And Analyze Blood Cell Data.srt

   3. 02 Clean Data With Missing Values.srt

   4. 03 Process Data For Machine Learning.srt

   5. 04A What Is Principal Component Analysis.srt

   6. 04B Reduce Data Dimensionality With Principal Component Analysis.srt

   7.1 01 Load and analyze blood cell data.ipynb

   7.11 What is Principal Component Analysis.pptx

   7.2 01 Load and analyze blood cell data.py

   7.4 02 Clean data with missing values.py

   7.6 03 Process data for machine learning.py

   7.8 04 Reduce data dimensionality with principal component analysis.py

  5. 08 Cluster blood cells based on fluorescent intensities

   1. 05A What Is Unsupervised Learning.srt

   2. 05B What Is K Means Clustering.srt

   3. 05C Build A Kmeans Clustering Model.srt

   4. 06 Visualize Clusters Found Via Kmeans.srt

   Source files - Cluster blood cells based on fluorescent intensities

    05 Build a KMeans clustering model.py

    06 Visualize clusters found via KMeans.py

    What is Unsupervised Learning.pptx

  6. 09 Preprocess a malignant vs benign cancer mass dataset

   09 Source files

    01 Load and analyze cancer dataset

     01 Load and analyze cancer dataset.py

    02 Preprocess cancer data for machine learning

     02 Preprocess cancer data for machine learning.py

   1. 00 Project Preview.mp4

   2. 01 Load And Analyze Cancer Dataset.mp4

   3. 02 Preprocess Cancer Data For Machine Learning.mp4

   4. Source Files.html

  7. 10 Build an SVM model to classify malignant vs benign cancer mass

   03a Why do we need SVM.pptx

   03b How does SVM work.pptx

   03c SVM on Non-Linear Data.pptx

   03d What are SVM kernels.pptx

   03e What is the precision-recall score.pptx

   1. 03A Why Do We Need SVM.srt

   2. 03B How Does SVM Work.srt

   3. 03C SVM On Non-Linear Data.srt

   4. 03D What Are SVM Kernels.srt

   5. 03E What Is The Precision-Recall Score.srt

   6. 03F Build An Svm Model To Classify Malignant Vs Benign Mass.srt

   7.1 03 Build an SVM model to classify malignant vs benign mass.ipynb

  8. 11 Build a logistic regression model to classify malignant vs benign cancer mass

   1. 04A What Is Logistic Regression.srt

   2. 04B Build A Logistic Regression Model.srt

   3.1 Source files.zip

   Source files

    What is Deep Learning.pptx

    What is a Neural Network.pptx

  9. 12 Improve model accuracy with tuning methods

   01a What is Cross Validation.pptx

   02a What is grid search cross validation.pptx

   03a What is Nested Cross Validation.pptx

   04a Make Decisions with Decision Trees.pptx

   1. 01A What Is Cross Validation.srt

   2. 01B Find Model Error With Cross Validation.srt

   3. 02A What Is Grid Search Cross Validation.srt

   4. 02B Find Optimal Hyperparameters With Grid Search.srt

   5. 03A What Is Nested Cross Validation.srt

   6. 03B Find Best Model Parameters With Nested Cross Validation.srt

   7. 04A What Is The Decision Tree Model.srt

   8. 04B Compare Models With Nested Cross Validation.srt

   9.1 01 Find model error with cross validation.ipynb

   9.3 02 Find optimal hyperparameters with grid search.ipynb

   9.5 03 Find best model parameters with nested cross validation.ipynb

   9.7 04 Compare models with nested cross validation.ipynb

  Bonus Resources.txt