Full Stack Data Science
69 videos • 2,299 views • by DeepNeuron
1
1.0 Descriptive Statistics - Measures of Central Tendency
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2
2.0 Descriptive Statistics - Measures of Dispersion Part 1
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3.0 Measures of Dispersion Part 2
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4.0 Measures Of Dispersion - Part 3
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5.0 Shape Measures
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6 Introduction to Python
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7.1 String Manipulation & Print formatting options in Python
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8 Tuple, Dictionary, Sets and Conditional Programming in Python
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7 2 List Data Structure in Python
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9 Conditional Flow and Control Structures
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11
9.1 Functions in Python
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12
10 Lambda Function, Non key var argument, Keyword variable argument in Python
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13
11 Numpy (Arrays, & Matrices) & Introduction to Pandas in Python
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14
Confidence Interval Assignment Solutions , & Anova Test
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15
Introduction to Hypothesis Testing
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16
12 Data Analytics with Pandas & Data Visualization in Python
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17
Inferential Statisitcs
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18
Two Way Anova with and Without Replicatin
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19
Chisq Test
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20
Introduction to Data Science , Machine Learning , & its use cases
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21
What is data science ?
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22
Linear Regression Theory and Intuition
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23
Implementing Linear Regression Model in Python using Sklearn and Statsmodel packages
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24
Linear Regression - Model Evaluation
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25
Recap of Linear Regression Model using Excel
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26
K Fold Cross Validation
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27
Multiple Linear Regression - Model Output Interpretation Part - 1
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Multiple Linear Regression - Model Output Interpretation Part - 2
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29
Validating Linear Regression - Model Assumptions
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30
Overview of Regularization Technique
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31
Machine Learning life cycle
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32
Exploratory Data Analysis
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33
Feature Engineering - Dummy Encoding/ One Hot Encoding, & Ordinal Encoding
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34
Feature Scaling
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35
Gradient Descent Algorithm
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36
Implementation of Gradient Descent and Stochastic Gradient Descent in Python
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37
How to Determine the number of iterations to use in Gradient Descent
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38
Intuition of Logistic Regression
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39
Logistic Regression Model Diagnostic
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40
Case Study - Churn Prediction Part 1
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41
Churn Prediction in Python Part 2
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Churn Prediction in Python - Part 3
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43
Recap on Topics Covered part of Logistic Regression Model Diagnostic
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44
Interpretation of ROC AUC
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45
Concordance and Discordance Metrics
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46
Kappa Statistic Metric
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47
How to Interpret Logistic Regression model output in Python?
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48
Decision Tree
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49
Bagging , & Random Forest model
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50
What is Boosting in Machine Learning ?
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51
Intuition of Gradient Boosting Machine for Regression
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52
Intuition of Gradient Boosting for Classification
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53
Hyper Parameter Tuning - Random Forest
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54
Hyper Parameter Tuning
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55
Gradient Boosting for Classification
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56
Unsupervised Machine Learning - KMeans & Hierarchical Clustering
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57
Implementing KMeans and Hierarchical Cluster in Python
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58
Time Series - Arima intuition
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59
Implementing Time Series in Python
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60
Multi-layer Perceptron - Neural Network
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61
Mathematical representation of forward propagation
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62
Activation functions in neural network
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63
How does Back propagation works
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64
Implementation of neural network using keras, & tensorflow
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65
Machine Learning Model Deployment both on Flask App server and AWS EC2
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66
Machine Learning Model deployment on AWS EC2
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67
Computer Vision
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68
Convolutional Neural Network
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69
implementation of convolutional neural network in Python
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