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Machine Learning and Deep Learning for Biology with Python and TensorFlow
01 Course Overview
00 Course Overview - Machine Learning For Biology (6:49)
01 What You'll Need (3:27)
Source files - Course overview
02 Introduction to Python (Prerequisite)
00. Intro To Course And Python (9:57)
01. Variables (19:19)
02. Type Conversion Examples (10:06)
03. Operators (28:54)
04. Collections (8:24)
05. List Examples (19:41)
06. Tuples Examples (8:36)
07. Dictionaries Examples (14:26)
08. Ranges Examples (8:32)
09. Conditionals (6:43)
10. If Statement Examples (21:32)
11. Loops (29:42)
12. Functions (17:01)
13. Parameters And Return Values Examples (13:54)
14. Classes And Objects (34:11)
15. Inheritance Examples (17:29)
16. Static Members Examples (11:05)
17. Summary And Outro (4:08)
Source Code
03 Machine Learning Introduction
01 What Is Machine Learning (5:26)
02 What Is Supervised Learning (10:39)
Source Files
04 Regression Fundamentals - Theory Behind the Code
01 Regression Introduction (8:58)
02 What Is Regression (19:55)
03 What Is Linear Regression (5:03)
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05 Build a K Nearest neighbors regression model to predict diabetes
00 Project Preview (2:14)
01 Load And Analyze Data (8:25)
01 What Is K Nearest Neighbours (8:07)
02 Build A K Nearest Neighbors Regression Model To Predict Diabetes (10:24)
Source Files
06 Build Regression Machine Learning Models to Detect Diabetes
03A What Is The Random Forest Classifier Model (5:42)
03B Build More Regression Models And Find The Best One (4:08)
04 Select Top Features Via Variance Threshold (12:42)
05 Visualize Linear Regression With Matplotlib Pyplot (6:14)
Source Files
07 Data analysis and transformation on blood cell data
00 Project Preview (2:56)
01 Load And Analyze Blood Cell Data (10:39)
02 Clean Data With Missing Values (12:28)
03 Process Data For Machine Learning (8:53)
04A What Is Principal Component Analysis (7:27)
04B Reduce Data Dimensionality With Principal Component Analysis (4:55)
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08 Cluster blood cells based on fluorescent intensities
05A What Is Unsupervised Learning (8:17)
05B What Is K Means Clustering (11:58)
05C Build A Kmeans Clustering Model (12:23)
06 Visualize Clusters Found Via Kmeans (8:09)
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09 Preprocess a malignant vs benign cancer mass dataset
00 Project Preview (2:22)
01 Load And Analyze Cancer Dataset (5:46)
02 Preprocess Cancer Data For Machine Learning (5:24)
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10 Build an SVM model to classify malignant vs benign cancer mass
03A Why Do We Need Svm (7:15)
03B How Does Svm Work (6:28)
03C Svm On Non-Linear Data (4:48)
03D What Are Svm Kernels (4:44)
03E What Is The Precision-Recall Score (4:42)
03F Build An Svm Model To Classify Malignant Vs Benign Mass (4:08)
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11 Build a logistic regression model to classify malignant vs benign cancer mass
04A What Is Logistic Regression (4:32)
04B Build A Logistic Regression Model (3:44)
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12 Improve model accuracy with tuning methods
01A What Is Cross Validation (8:25)
01B Find Model Error With Cross Validation (3:46)
02A What Is Grid Search Cross Validation (5:46)
02B Find Optimal Hyperparameters With Grid Search (9:37)
03A What Is Nested Cross Validation (14:29)
03B Find Best Model Parameters With Nested Cross Validation (4:43)
04A What Is The Decision Tree Model (10:51)
04B Compare Models With Nested Cross Validation (4:00)
Source Files
13 Prepare heart disease data for machine learning
01 Load Data Via Data File (11:11)
02 Clean And Preprocess Heart Disease Data For Machine Learning (11:44)
03 Process Heart Disease Data For Machine Learning (8:39)
14 Predict heart disease with machine learning
04B Build A Linear Classifier With Stochastic Gradient Descent (8:04)
04A What Is Stochastic Gradient Descent (11:28)
05A What Is Ada Boost (5:48)
05B Build An Ada Boost Classifier (7:17)
06 Build A K Nearest Neighbors Machine Learning Model (8:03)
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15 Deep learning and neural networks introduction
01 What Is Deep Learning (7:42)
02 What Is A Neural Network (8:47)
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16 Build a neural network to find malaria in cells
00 Project Preview (1:15)
01 Load Data Via Tensorflow (4:06)
02 Visualize Malaria Cell Images (8:49)
03 Extract A Subset Of Samples (8:03)
04 Build A Neural Network (5:40)
05 Train And Evaluate Model Accuracy (9:23)
Source Files
00 Course Overview - Machine Learning For Biology
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