In this lesson, we will begin inputting the data that we will use for our rule-based chatbot.
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This clip is from the chapter "Basics of Chatbots with Machine Learning and Python" of the series "Chatbots for Beginners: A...

Let's look at another typical use case of a window function: to create a sequential integer number inside a group of rows while deciding how this group will be ordered.
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This clip is from the chapter "SQL - Window Functions"...

The second type of subquery used to filter data based on conditional logic is the subquery that may return multiple rows of data.
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This clip is from the chapter "SQL – Subqueries" of the series "Master SQL for Data...

Here, we will look at the first type of subquery used to filter data with conditional logic, the results of which can be a single row or multiple rows of subqueries, depending on how we build it.
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This clip is from the chapter...

We will explore the data-storing area called the dictionary, which stores constraints, filters, functions, lists, and so on.
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This clip is from the chapter "SQL - Retrieving Data with Queries" of the series "Master SQL for...

Here, you will learn to combine and filter data based on multiple conditions in the same query using operators.
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This clip is from the chapter "SQL - Retrieving Data with Queries" of the series "Master SQL for Data...

In this lesson, you will learn to use filtering conditions to filter data based on conditions. Here, we will explore the WHERE condition.
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This clip is from the chapter "SQL - Retrieving Data with Queries" of the series...

In this lesson, you will learn about combining datasets and tables with very little or no common factors among them.
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This clip is from the chapter "SQL - Combining Data from Multiple Tables" of the series "Master SQL for Data...

In this lesson, you will learn to create a recommendation and use a brute-force algorithm to generate the recommendation.
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This clip is from the chapter "Project Amazon Product Recommendation System" of the series "Recommender...

In this video, we will perform data visualization with the project we created and check the model’s accuracy.
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This clip is from the chapter "Project Amazon Product Recommendation System" of the series "Recommender Systems:...

After entirely developing our model, you will learn to fit and evaluate the model for functionality.
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This clip is from the chapter "Project Amazon Product Recommendation System" of the series "Recommender Systems: An Applied...

Here, we will look at the next step of training our model: the compute loss function.
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This clip is from the chapter "Project Amazon Product Recommendation System" of the series "Recommender Systems: An Applied Approach Using...

In this video, you will learn how to create a candidate tower and develop a retrieval system.
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This clip is from the chapter "Project Amazon Product Recommendation System" of the series "Recommender Systems: An Applied...

In this lecture, you will learn to develop our model and create a query tower to perform retrieval tasks.
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This clip is from the chapter "Project Amazon Product Recommendation System" of the series "Recommender Systems: An...

In this lesson, you will learn to perform the test train split, which will do the training split and then create a prediction; we will use a random dataset (80-20 ratio).
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This clip is from the chapter "Project Amazon Product...

In this video, you will learn about the next part of the project, which is rating our data.
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This clip is from the chapter "Project Amazon Product Recommendation System" of the series "Recommender Systems: An Applied Approach...

In this video, we will continue to check our dataset using a single username. We will use a part of the DataFrame to do so.
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This clip is from the chapter "Project Amazon Product Recommendation System" of the series...

In this video, you will learn how to load a dataset for the project being developed, using WordCloud, importing WordCloud, STOPWORDS, and ImageColorGenerator.
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This clip is from the chapter "Project Amazon Product...

In this video, we will understand how to vectorize the story by defining functions, using the data, and developing an index and tokenizer. We will also determine the maximum story length calculated.
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This clip is from the...

In this lesson, we will discuss deploying our chatbot using code. We will not use a built-in application such as Twilio; instead, we will use a Python library—Boto.
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This clip is from the chapter "Chatbots Development with...

In this lecture, we will understand what a session state is, basically the state of the conversation between the user and the Amazon Lex chatbot and dialog action, which determines the action that Amazon Lex should take to the Lambda...

After checking our model for accuracy, we will make predictions of our results from the model we created. We will visualize the predictions using the test data.
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This clip is from the chapter "Advanced Chatbots with Deep...

After learning to create our encoders for the input sequences and the questions, the encoders will match the data and obtain the responses.
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This clip is from the chapter "Advanced Chatbots with Deep Learning and Python" of...

We will begin to develop our deep learning model and input placeholders to store the maximum story length and question length and define the vocabulary size. We will now build our encoder using the sequential model.
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This clip...