Genre:eLearning |Language:English
Files Included :
1 Course Overview.mp4 (4.54 MB)
1 Overview.mp4 (1.92 MB)
2 What to Expect.mp4 (1.47 MB)
3 On Machine Learning.mp4 (5.87 MB)
4 What Is Different About Machine Learning.mp4 (4.03 MB)
5 Learning Types.mp4 (11.29 MB)
6 Machine Learning Pipeline.mp4 (9.47 MB)
7 Problem Definition.mp4 (4.73 MB)
8 Introducing Google Collaboratory.mp4 (8.3 MB)
9 Summary.mp4 (1.2 MB)
1 Overview.mp4 (1.45 MB)
2 Revisiting ML Pipeline.mp4 (1.5 MB)
3 Understanding Data Sourcing.mp4 (8.29 MB)
4 CSV Format.mp4 (1.93 MB)
5 Understanding SciPy.mp4 (6.54 MB)
6 Demo - Loading Data into Pandas.mp4 (8.88 MB)
7 Summary.mp4 (1.12 MB)
01 Overview.mp4 (1.4 MB)
02 Revisiting ML Pipeline.mp4 (2.14 MB)
03 Introducing Data Analysis.mp4 (5.5 MB)
04 Univariant Numerical Analysis.mp4 (12.54 MB)
05 Bivariant Numerical Analysis.mp4 (7.93 MB)
06 Demo - Descriptive Stats - Part One.mp4 (14.14 MB)
07 Demo - Descriptive Stats - Part Two.mp4 (10.01 MB)
08 Data Visualization.mp4 (10.63 MB)
09 Demo - Data Visualization - Part One.mp4 (11.67 MB)
10 Demo - Data Visualization - Part Two.mp4 (10.4 MB)
11 Summary.mp4 (1021.43 KB)
01 Overview.mp4 (1.88 MB)
02 Revisting ML Pipeline.mp4 (2.81 MB)
03 Data Scaling - The Problem.mp4 (11.24 MB)
04 Data Scaling - The Solution.mp4 (4.32 MB)
05 The Need for Data Segregation.mp4 (5.94 MB)
06 Train Test Split.mp4 (5.05 MB)
07 KFlod Cross Validation.mp4 (5.25 MB)
08 Welcoming scikit-learn.mp4 (3.67 MB)
09 Demo - Data Segregation Techniques.mp4 (8.48 MB)
10 Summary.mp4 (1.71 MB)
01 Overview.mp4 (1.5 MB)
02 Revisiting ML Pipeline.mp4 (1.72 MB)
03 Scoping Your Focus.mp4 (9.7 MB)
04 Introducing Derivatives.mp4 (6.81 MB)
05 Linear Regression.mp4 (5.49 MB)
06 Variance Bias Tradeoff.mp4 (8.58 MB)
07 Other Regression Algorithms.mp4 (3.19 MB)
08 Model Evaluation.mp4 (4.32 MB)
09 Demo - Deploying and Testing the Model - Part 1.mp4 (18.61 MB)
10 Demo - Deploying and Testing the Model - Part 2.mp4 (15.76 MB)
11 Summary.mp4 (2.87 MB)
1 Overview.mp4 (1.74 MB)
2 Handling Features.mp4 (2.95 MB)
3 Model Improvement.mp4 (2.14 MB)
4 Automated ML.mp4 (6.45 MB)
5 Operationalization.mp4 (2.63 MB)
6 Team Data Science Process.mp4 (4.21 MB)
7 Summary.mp4 (3.74 MB)
1 Course Overview.mp4 (3.23 MB)
01 Version Check.mp4 (552.03 KB)
02 Module Overview.mp4 (1.93 MB)
03 Prerequisites and Course Outline.mp4 (2.27 MB)
04 The Need for Data Preparation.mp4 (6.1 MB)
05 Insufficient Data.mp4 (10.02 MB)
06 Too Much Data.mp4 (6.35 MB)
07 Non-representative Data, Missing Values, Outliers, Duplicates.mp4 (3.55 MB)
08 Dealing with Missing Data.mp4 (7.56 MB)
09 Dealing with Outliers.mp4 (8.17 MB)
10 Oversampling and Undersampling to Balance Datasets.mp4 (7.13 MB)
11 Overfitting and Underfitting.mp4 (4.24 MB)
12 Module Summary.mp4 (2.14 MB)
01 Module Overview.mp4 (1.83 MB)
02 Handling Missing Values.mp4 (12.87 MB)
03 Cleaning Data.mp4 (15.02 MB)
04 Visualizing Relationships.mp4 (8.4 MB)
05 Building a Regression Model.mp4 (14.85 MB)
06 Univariate Feature Imputation Using the Simple Imputer.mp4 (14.99 MB)
07 Multivariate Feature Imputation Using the Iterative Imputer.mp4 (12.14 MB)
08 Missing Value Indicator.mp4 (3.97 MB)
09 Feature Imputation as a Part of an Machine Learning Pipeline.mp4 (7.85 MB)
10 Module Summary.mp4 (2.06 MB)
01 Module Overview.mp4 (3.97 MB)
02 Numeric Data.mp4 (8.04 MB)
03 Scaling and Standardizing Features.mp4 (9.3 MB)
04 Normalizing and Binarizing Features.mp4 (12.24 MB)
05 Categorical Data.mp4 (4.89 MB)
06 Numeric Encoding of Categorical Data.mp4 (7.27 MB)
07 Label Encoding and One-hot Encoding.mp4 (15.24 MB)
08 Discretization of Continuous Values Using Pandas Cut.mp4 (6.48 MB)
09 Discretization of Continuous Values Using the KBins Discretizer.mp4 (7.42 MB)
10 Building a Regression Model with Discretized Data.mp4 (6.72 MB)
11 Module Summary.mp4 (1.88 MB)
1 Module Overview.mp4 (1.82 MB)
2 The Curse of Dimensionality.mp4 (7.77 MB)
3 Reducing Complexity in Data.mp4 (4.73 MB)
4 Feature Selection to Reduce Dimensions.mp4 (5.56 MB)
5 Filter Methods.mp4 (6.39 MB)
6 Embedded Methods.mp4 (7.64 MB)
7 Module Summary.mp4 (2.04 MB)
1 Module Overview.mp4 (1.84 MB)
2 Feature Correlations.mp4 (17.28 MB)
3 Using the Correlation Matrix to Detect Multi-collinearity.mp4 (10.35 MB)
4 Using Variance Inflation Factor to Detect Multi-collinearity.mp4 (6.57 MB)
5 Features Selection Using Missing Values Threshold and Variance Threshold.mp4 (13.08 MB)
6 Univariate Feature Selection Using Chi2 and ANOVA.mp4 (14.09 MB)
