🔥Edureka Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training
    This Edureka video on ‘Selecting The Correct Predictive Modeling Technique’ covers the various deciding factors to choose the correct predictive modeling technique. Following are the topics discussed:
    00:00 – Introduction
    00:51 – What is Predictive Analysis?
    02:58 – Predictive Analysis Techniques
    06:37 – Choosing a Predictive Analysis Technique
    09:03 – Predictive Analysis Models

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    #PythonEdureka #Edureka #predictiveanalysis #machinelearning #pythonprojects #pythonprogramming #pythontutorial #PythonTraining #machinelearningTutorial
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    How it Works?

    Edureka’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 the python programming language. Furthermore, you will be taught of 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 the python programming language to enhance your learning experience.
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    Why Learn Machine Learning using Python?

    Data Science is a set of techniques that enables 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.

    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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    Who should go for this Machine Learning Certification Training using Python?

    Edureka’s Python Machine Learning Certification Course is a good fit for the below professionals:
    Developers aspiring to be a ‘Machine Learning Engineer’
    Analytics Managers who are leading a team of analysts
    Business Analysts who want to understand Machine Learning (ML) Techniques
    Information Architects who want to gain expertise in Predictive Analytics
    ‘Python’ professionals who want to design automatic predictive models
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    For more information, please write back to us at sales@edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll-free)

    6 Comments

    1. Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Python Machine Learning Course curriculum, Visit our Website: http://bit.ly/2OpzQWw. Use code YOUTUBE20 for exclusive discounts.

    2. I have an assignment. ..
      My Data is : rainfall level, temperature, humidity, number of cases of malaria disease by month for the last 5 years.
      My goal: forcasting the cases so I can predict the number of cases for the next 2 years.
      Problem: I don't know if there is +/- correlation between cases and rainfall levels, temp and humidity
      What is the most suitable technque and model and why?

      I think it's either time series or forcasting or both 😅 I'm confused with those 2. .

    3. Thank you, Wasim for a wonderful explanation. I am learning ML and I am completely fascinated by the impact and opportunities ML brings to different fields and applications. I am trying to choose a model that can predict the power of solar systems as a function of temperature, irradiance, cloud ceiling, season, and humidity. Watching the video, I understand that linear regression is probably the best option. But what other methods/models I can use for comparison to find the best model for solar power production. Please advise. Many thanks again.

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