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Analyze and parse HTML responses, programmatically scrape web data, and utilize Pandas DataFrames to store, transform, and merge tables.
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This course examines the Monte Carlo methods and its types and solves the frozen lake problem with Monte Carlo methods.
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Hotspot-Analysis Public
We will visualize the results of hotspot analysis and use kernel density estimation, which is the most popular algorithm for building distributions using a collection of observations. By the end o…
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Generate and paraphrase text using different models for use in Python. Understand the applications and challenges of text summarization models.
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This module will look at how to build different variants of DRQN including DARQN to solve the problem of Atari game
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The module covers the theory behind reinforcement learning and introduces Markov chains and Markov Decision Processes
Repositories
- Machine-Learning-and-Developing-a-Text-Classifier Public
Understand how supervised and unsupervised machine learning methods can be used to construct and implement a text classifier in Python.
- Pre-Processing-Data-and-Feature-Extraction Public
Learn how to collect, clean and categorize data for your projects. Then extract the key textual features and present them visually.
- Understanding-Word-and-Document-Vectors Public
Understand what vectors are, and how they can be used to compare the frequencies of words and similar documents, and group them accordingly.
- Natural-Language-Processing-Fundamentals Public
Discover the basic tools and techniques required to preprocess data to use in an NLP project.
- Performing-Sentiment-Analysis-with-NLP Public
Explore the libraries and frameworks used to perform sentiment analysis on textual data.
- Using-and-Comparing-Topic-Modeling-Algorithms Public
Learn about various Topic Modeling algorithms, and how to apply them to datasets. Compare the strengths of different algorithms with some practical challenges.
- Using-Text-Generators-and-Summarization-models Public
Generate and paraphrase text using different models for use in Python. Understand the applications and challenges of text summarization models.
- Extracting-and-Analyzing-Web-Data Public
Collect data by scraping web pages, then analyze your findings. Learn how to use APIs to retrieve real-time data from Twitter.
- Deploying-Deep-Learning-Models-as-Web-Applications Public
This module covers handling new data and creating a model that can learn continuously from the patterns and help make better predictions.
- Introduction-to-Neural-Networks-and-Deep-Learning Public
This module covers the basics components of a neural network and its essential operations. It also explores a trained neural network created using TensorFlow
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