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Image Classification using CNN | Deep Learning Tutorial | Machine Learning Project 9 | Edureka
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This Edureka video on 'Image Classification using CNN' will give you an overview of Image Classification using Machine Learning and will help you understand various important concepts that concern Image Classification with ML.
Following pointers are covered in this Image Classification using CNN:
1) Introduction
2) Tools and Frameworks
3) Project
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---------Edureka Machine Learning Projects---------
---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧---------
---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬---------
-----------------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏GD 𝐂𝐨𝐮𝐫𝐬𝐞---------------
#edureka #edurekamachinelearning #machinelearning #ImageClassificationusingCNN #machinelearningproject #machinelearningtutorial #edurekatraining
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About the Course :
Edureka’s Machine Learning Course using Python is designed to make you grasp the concepts of Machine Learning. The Machine Learning training will provide a 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 the 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.
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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.
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