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A Quick Guide To Sentiment Analysis | Sentiment Analysis In Python Using Textblob | Edureka

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Machine Learning
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Published on 05/26/24 / In How-to & Learning

๐Ÿ”ฅ Post Graduate Diploma in Artificial Intelligence by E&ICT Academy
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This video on the Sentiment Analysis in Python is a quick guide for the one who is getting started with Sentiment Analysis.
Second Part: https://youtu.be/27P268Q7pE0

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#MachineLearningUsingPython #MachineLearningTraning #SentimentAnalysis #PythonEdureka

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About the Course

Edurekaโ€™s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.

After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the 'Roles' played by a Machine Learning Engineer
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Describe Machine Learning
Work with real-time data
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Discuss Machine Learning algorithms and their implementation
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Explain Time Series and itโ€™s related concepts
Gain expertise to handle business in future, living the present

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Why learn Machine Learning with Python?

Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.

Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.
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