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Practical GraphQL - Become a GraphQL Ninja - Introduction to GraphQL
Introduction: Introduction to GraphQL This clip is from the chapter "Introduction" of the series "Practical GraphQL - Become a GraphQL Ninja".This section gives an introduction to GraphQL and creates the application shell.
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Selenium Python Automation Testing from Scratch and Frameworks - Implementing Data-Driven Mechanism
This video explains how to implement data-driven mechanism. This clip is from the chapter "Part III-Developing End-to-End Selenium Python Framework from Scratch" of the series "Selenium Python Automation Testing from Scratch and...
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PySpark and AWS: Master Big Data with PySpark and AWS - Finding Average-1
In this session, we will try to calculate the average of a given dataset. We will start by writing a transformation flow that will take the CSV data file as input and provide us with the average of the movie ratings from the dataset....
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pandas for Python - A Quick Guide - Handling Missing Values and Duplicates
During the data analysis, significant time is spent on data cleaning and transformation. Pandas provides the tool to handle such data. In this video, you will learn about handling missing data and duplicates. You will learn to eliminate...
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No-Code Machine Learning Using Amazon AWS SageMaker Canvas - Predicting Data and Validating Accuracy
In this video, you will learn how to predict data and validate the accuracy. This clip is from the chapter "Project 2 - Spam SMS Detection" of the series "No-Code Machine Learning Using Amazon AWS SageMaker Canvas".In this section, we...
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No-Code Machine Learning Using Amazon AWS SageMaker Canvas - Set Up Data in S3 Buckets for Use in SageMaker
In this video, you will learn how to set up data in S3 Buckets for use in SageMaker. This clip is from the chapter "Setup" of the series "No-Code Machine Learning Using Amazon AWS SageMaker Canvas".In this section, you will learn how to...
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Probability Statistics - The Foundations of Machine Learning - Spam Detection - Implementation Issues
In this video, we will cover spam detection - implementation issues. This clip is from the chapter "Applications and Rules for Probability" of the series "Probability / Statistics - The Foundations of Machine Learning".In this section,...
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Deep Learning CNN Convolutional Neural Networks with Python - Focus of the Course
This video provides an extension and focus areas about this course in detail. This clip is from the chapter "Introduction to the Course" of the series "Deep Learning CNN: Convolutional Neural Networks with Python".This section provides...
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Deep Learning CNN Convolutional Neural Networks with Python - Face Verification Activity
This is an activity video on face verification. This clip is from the chapter "Face Verification" of the series "Deep Learning CNN: Convolutional Neural Networks with Python".This section focuses on building the face verification app.
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Python for Deep Learning - Build Neural Networks in Python - Introduction - Implementation of ANN in Python
In this video, we will have a quick Introduction to this section. This clip is from the chapter "Implementation of ANN in Python" of the series "Python for Deep Learning — Build Neural Networks in Python".In this section, you will...
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Python for Deep Learning - Build Neural Networks in Python - Building the CNN Model
In this video, you will learn how to build the Convolutional Neural Networks (CNN) model. This clip is from the chapter "Implementation of CNN in Python" of the series "Python for Deep Learning — Build Neural Networks in Python".In...
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Practical Data Science using Python - Unsupervised Learning - K-Means Clustering
This video introduces you to K-Means clustering. This clip is from the chapter "Unsupervised Learning - K-Means Clustering" of the series "Practical Data Science Using Python".This section explains unsupervised learning - K-Means...
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Practical Data Science using Python - Support Vector Machine Project 1
This video explains the Support Vector Machine project. This clip is from the chapter "Advanced Classification Techniques – Support Vector Machine" of the series "Practical Data Science Using Python".This section explains advanced...
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Practical Data Science using Python - Support Vector Machine Metrics and Polynomial SVM
This video explains Support Vector Machine metrics and polynomial SVM. This clip is from the chapter "Advanced Classification Techniques – Support Vector Machine" of the series "Practical Data Science Using Python".This section explains...
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Practical Data Science using Python - Principal Component Analysis - Computations 2
This video explains Eigenvalues and Eigenvectors. This clip is from the chapter "Dimensionality Reduction Using PCA" of the series "Practical Data Science Using Python".This section explains dimensionality reduction using PCA.
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Practical Data Science using Python - Principal Component Analysis - Computations 1
This video explains Principal Component Analysis – computations. This clip is from the chapter "Dimensionality Reduction Using PCA" of the series "Practical Data Science Using Python".This section explains dimensionality reduction using...
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Practical Data Science using Python - Pandas DataFrame 5
This video explains the left outer join in dataframe. This clip is from the chapter "Python for Data Science" of the series "Practical Data Science Using Python".This section explains Python for data science.
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Practical Data Science using Python - Naive Bayes Probability Model - Introduction
This video introduces you to the Naive Bayes probability model. This clip is from the chapter "Naive Bayes Probability Model" of the series "Practical Data Science Using Python".This section explains Naive Bayes probability model –...
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Practical Data Science using Python - K-Means Clustering Computation
This video explains K-Means clustering computation. This clip is from the chapter "Unsupervised Learning - K-Means Clustering" of the series "Practical Data Science Using Python".This section explains unsupervised learning - K-Means...
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Practical Data Science using Python - Decision Tree - Learning Steps
This video explains decision tree - learning steps. This clip is from the chapter "Classification using decision trees" of the series "Practical Data Science Using Python".This section explains classification using decision trees.
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Practical Data Science using Python - Hypothesis Testing
This video explains hypothesis testing.
This clip is from the chapter "Statistical Techniques" of the series "Practical Data Science Using Python".This section explains advanced visualizations using business applications.
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Practical Data Science using Python - EDA Project - 3
This video explains unordered categorical variables. This clip is from the chapter "Exploratory Data Analysis (EDA)" of the series "Practical Data Science Using Python".This section explains Exploratory Data Analysis.
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Practical Data Science using Python - EDA Project - 1
This video explains an example of lending club case study. This clip is from the chapter "Exploratory Data Analysis (EDA)" of the series "Practical Data Science Using Python".This section explains Exploratory Data Analysis.
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Practical Data Science using Python - Decision Tree - Model Optimization using Grid Search Cross Validation
This video explains decision tree - model optimization using grid search cross validation. This clip is from the chapter "Classification using decision trees" of the series "Practical Data Science Using Python".This section explains...