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Machine Learning: Random Forest with Python from Scratch - Importing Data, Helper Functions
Before creating a decision tree, we will first learn to import our dataset using Pandas. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine Learning: Random Forest with Python from Scratch©".This section...
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Machine Learning: Random Forest with Python from Scratch - Quick Implementation of Random Forest Model
Let's quickly implement Random Forest using the sklearn Random Forest model to tune the model's performance according to the project. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine Learning: Random...
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Machine Learning: Random Forest with Python from Scratch - Categorical to Numeric Conversion
In a machine, it understands values in the form of numbers. You will learn how to convert non-numeric data to numeric without changing the feature of the value. This clip is from the chapter "Random Forest Step-by-Step" of the series...
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Machine Learning: Random Forest with Python from Scratch - Outliers Removal
In the second part of the data cleaning process, we will look at an outlier in detail and learn how to correct or remove the outlier. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine Learning: Random...
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Machine Learning: Random Forest with Python from Scratch - Dealing with Missing Values
Let's look at the first step involved in the data cleaning process, which is filling or removing missing values from a dataset. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine Learning: Random Forest...
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Machine Learning: Random Forest with Python from Scratch - Reading and Manipulating Dataset
Previously, you learned how to read a dataset; now, we will look at manipulating the data and using a sample dataset in our code. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine Learning: Random Forest...
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Machine Learning: Random Forest with Python from Scratch - Using Pandas for Random Forest (2)
This is a continuation of the previous lesson, and here we will look at conditionally selecting values from a dataset. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine Learning: Random Forest with Python...
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Machine Learning: Random Forest with Python from Scratch - Introduction to the Final Project
In this video, we will discuss our final project, which classifies the titanic dataset using Random Forest. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine Learning: Random Forest with Python from...
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Machine Learning: Random Forest with Python from Scratch - Pros and Cons of Random Forest
In this video, we will look at the benefits and limitations of Random Forest and the complexities involved in decision-making using Random Forest. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine...
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Machine Learning: Random Forest with Python from Scratch - How Decision Trees and Random Forest Work
We will understand what a decision tree is and create a decision tree and get a prediction result from the decision tree. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine Learning: Random Forest with...
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Modern JavaScript from the Beginning - Second Edition - Delete Ideas
In this video, we will implement the ability to delete ideas from the list. This clip is from the chapter "RandomIdeas Project - Webpack Frontend (Bonus Project)" of the series "Modern JavaScript from the Beginning".In this bonus...
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Data Science Model Deployments and Cloud Computing on GCP - Library Example with Chart.js
This video explains Chart.js, a popular charting library for JavaScript. It makes what would be a difficult combination of styling, layout, and data positioning into an easy to manage ordeal, saving developers loads of time in creating...
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Data Science Model Deployments and Cloud Computing on GCP - Lab - Assignment Implement Caching
In this lab assignment video, we will be testing your knowledge of caching in Python applications by asking you to implement a caching mechanism. This clip is from the chapter "Google App Engine - For Serverless Applications" of the...
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Data Science Model Deployments and Cloud Computing on GCP - Lab - Deploy Python - BigQuery Application
In this video, we walk you through the process of deploying a Python application that interacts with BigQuery, showing you how to set up and configure your BigQuery project, how to create a basic Flask application that integrates with...
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Fundamentals of Neural Networks - Lab 1 - RNN in Text Classification
This video demonstrates how to design a recurrent neural network or RNN. This clip is from the chapter "Recurrent Neural Networks" of the series "Fundamentals in Neural Networks".This section explains NLP, we will start with recurrent...
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Fundamentals of Neural Networks - Lab 3 - Deep CNN
This video demonstrates a deeper CNN, where you will build a much bigger number of trainable parameters. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains...
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Fundamentals of Neural Networks - Lab 2 - Introduction to CNN
This video demonstrates the architecture and how to carry out the code using TensorFlow in collab and building a convolutional neural network. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in...
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Fundamentals of Neural Networks - Lab 4 - Functional API
This video demonstrates functional API versus sequential API. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains artificial neural networks where you will...
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Fundamentals of Neural Networks - Lab 3 - Introduction to Neural Network
This video demonstrates how to use Keras TensorFlow as API to essentially design and craft the neural network architecture. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This...
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Fundamentals of Neural Networks - Backward Propagation Through Time
Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks. It can be used to train Elman networks. The algorithm was independently derived by numerous researchers. This clip...
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Fundamentals of Neural Networks - Forward Propagation in RNN
The forward propagation in an RNN makes a few assumptions: 1) We assume the hyperbolic tangent activation function for the hidden layer. 2) We assume that the output is discrete as if the RNN is used to predict words or characters. This...
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Fundamentals of Neural Networks - Language Processing
NLP is a tool for structuring data in a way that AI systems can process that deals with language. NLP uses AI to 'read' through a document and extract key information. This clip is from the chapter "Recurrent Neural Networks" of the...
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Fundamentals of Neural Networks - Padding
This video explains padding in convolutional neural networks. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you...
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Fundamentals of Neural Networks - Gradient Descent
This video explains the optimization problem using the gradient descent algorithm. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains artificial neural...