Advanced Python Tutorial 4 | Learn Advanced Python Concepts | Python Programming | All In One Code смотреть онлайн

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About the Course

All in one code’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. 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 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
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in the future, living the present

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Why learn Machine Learning with Python?

Data Science is a set of techniques that enable 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.