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A Comprehensive Guide to Machine Learning With Python Training

If you're someone looking to build a career as a data scientist, you must have heard about Machine Learning. It is an incredibly beneficial tool that allows you to get hidden insights from large sets of data and predict future trends accurately.Technically speaking, ML is a prominent aspect of artificial intelligence (AI) domain and has been in the news for quite some time now. It allows computers to learn without being explicitly programmed. This area offers attractive opportunities for aspirants willing to make a career in this domain.Machine Learning can be broadly separated into three categories:Supervised learningHere, the machine learning program is given the input data, as well as corresponding labeling. This means that the learning data needs to be labeled by a human being beforehand.Unsupervised learningIn unsupervised learning, there are no labels provided to the learning algorithm. This means that the algorithm has to figure out the clustering of the input data.Reinforcement learningIn this type of machine learning, the computer program interacts with its environment dynamically. This means that the computer program receives positive and/or negative feedback to be able to improve its performance.Why Start Machine Learning With Python?To master Data Science and Machine Learning, it is imperative to master at least one coding language and continue using it confidently. For a satisfying and successful Machine Learning journey, Python is an ideal choice as the coding language, especially if you want to jump into the field of machine learning and data science.It is an extremely approachable, intuitive, and minimalistic language that comes with a full-featured library line that significantly reduces the time to get desired results.How Can You Learn Machine Learning With Python?Machine Learning with Python course is specifically designed to let you learn the fundamentals of machine learning using a well-known programming language, Python.The course contents are usually divided into two componentsTo learn about the purpose of Machine Learning and its applications in the real world.A general understanding of various Machine Learning topics including Machine Learning algorithms supervised vs unsupervised learning, and model evaluation.The course allows you to explore various algorithms and models as listed below:Algorithms: Classification, Clustering, Regression, and Dimensional Reduction.Models: Root Mean Squared Error, Train/Test Split, and Random Forests.Topics Covered In the Machine Learning with Python CourseBelow are the topics covered in Machine Learning with Python course:Neighbour ClassifierNeural networks:Neural Networks from Scratch (in Python).Dropout Neural Networks.Neural Network in using Numpy (in Python).Neural Networks with Scikit (in Python).Machine Learning with Scikit and Python.Naive Bayes Classifier.Introduction to Text Classification using Python and Naive Bayes.Skills You Will Acquire In Machine Learning With Python TrainingBelow are some of the essential skills you will acquire after completing this training:Setting up a Python development environment accurately.Various algorithm concepts such as regression, clustering, classification, sci-kit learn and SciPy.Applications of Machine Learning.Creation of accurate data science models.About Python libraries most suitable for Machine Learning.Importance of data analysis and its relevance in the present scenario.Learning how to predict future outcomes to make informed business decisions by using Python.How to apply predictive algorithms to data.Conceptual understanding of how Python works in the Hadoop distributed file ecosystem, PIG, and Hive.How to use Python packages for data analysis applications.Who Is Eligible for Doing This Course?You can do this course even if you have little to no experience in math or programming. The only important element you require is interest in the field and motivation to learn. That being said, a course in Machine Learning with Python is ideal for anyone who is:Passionate about learning the fundamentals of machine learning algorithm with Python.People who wish to kick-start or make a transition to a career as a data scientist.EXCEL users (intermediate and advanced both) who are unable to work with large sets of data.Professionals keen on learning practical application aspects of machine learning to real-world problems.Professionals looking to learn ways to apply machine learning to their respective domain.
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A Comprehensive Guide to Machine Learning With Python Training

Susan May
Blog
10th Jun, 2019
A Comprehensive Guide to Machine Learning With Python Training

If you're someone looking to build a career as a data scientist, you must have heard about Machine Learning. It is an incredibly beneficial tool that allows you to get hidden insights from large sets of data and predict future trends accurately.

Technically speaking, ML is a prominent aspect of artificial intelligence (AI) domain and has been in the news for quite some time now. It allows computers to learn without being explicitly programmed. This area offers attractive opportunities for aspirants willing to make a career in this domain.

Machine Learning can be broadly separated into three categories:

Machine Learning categories

  • Supervised learning

Here, the machine learning program is given the input data, as well as corresponding labeling. This means that the learning data needs to be labeled by a human being beforehand.

  • Unsupervised learning

In unsupervised learning, there are no labels provided to the learning algorithm. This means that the algorithm has to figure out the clustering of the input data.

  • Reinforcement learning

In this type of machine learning, the computer program interacts with its environment dynamically. This means that the computer program receives positive and/or negative feedback to be able to improve its performance.

Why Start Machine Learning With Python?

To master Data Science and Machine Learning, it is imperative to master at least one coding language and continue using it confidently. For a satisfying and successful Machine Learning journey, Python is an ideal choice as the coding language, especially if you want to jump into the field of machine learning and data science.

It is an extremely approachable, intuitive, and minimalistic language that comes with a full-featured library line that significantly reduces the time to get desired results.

How Can You Learn Machine Learning With Python?

Machine Learning with Python course is specifically designed to let you learn the fundamentals of machine learning using a well-known programming language, Python.

The course contents are usually divided into two components

  1. To learn about the purpose of Machine Learning and its applications in the real world.
  2. A general understanding of various Machine Learning topics including Machine Learning algorithms supervised vs unsupervised learning, and model evaluation.

The course allows you to explore various algorithms and models as listed below:

  • Algorithms: Classification, Clustering, Regression, and Dimensional Reduction.
  • Models: Root Mean Squared Error, Train/Test Split, and Random Forests.

Topics Covered In the Machine Learning with Python Course

Below are the topics covered in Machine Learning with Python course:

  1. Neighbour Classifier
  2. Neural networks:
    • Neural Networks from Scratch (in Python).
    • Dropout Neural Networks.
    • Neural Network in using Numpy (in Python).
    • Neural Networks with Scikit (in Python).
  3. Machine Learning with Scikit and Python.
  4. Naive Bayes Classifier.
  5. Introduction to Text Classification using Python and Naive Bayes.

Skills You Will Acquire In Machine Learning With Python Training

Below are some of the essential skills you will acquire after completing this training:

  • Setting up a Python development environment accurately.
  • Various algorithm concepts such as regression, clustering, classification, sci-kit learn and SciPy.
  • Applications of Machine Learning.
  • Creation of accurate data science models.
  • About Python libraries most suitable for Machine Learning.
  • Importance of data analysis and its relevance in the present scenario.
  • Learning how to predict future outcomes to make informed business decisions by using Python.
  • How to apply predictive algorithms to data.
  • Conceptual understanding of how Python works in the Hadoop distributed file ecosystem, PIG, and Hive.
  • How to use Python packages for data analysis applications.

Who Is Eligible for Doing This Course?Who Is Eligible for Doing Machine Learning with Python Course

You can do this course even if you have little to no experience in math or programming. The only important element you require is interest in the field and motivation to learn. That being said, a course in Machine Learning with Python is ideal for anyone who is:

  • Passionate about learning the fundamentals of machine learning algorithm with Python.
  • People who wish to kick-start or make a transition to a career as a data scientist.
  • EXCEL users (intermediate and advanced both) who are unable to work with large sets of data.
  • Professionals keen on learning practical application aspects of machine learning to real-world problems.
  • Professionals looking to learn ways to apply machine learning to their respective domain.
Susan

Susan May

Writer, Developer, Explorer

Susan is a gamer, internet scholar and an entrepreneur, specialising in Big Data, Hadoop, Web Development and many other technologies. She is the author of several articles published on Zeolearn and KnowledgeHut blogs. She has gained a lot of experience by working as a freelancer and is now working as a trainer. As a developer, she has spoken at various international tech conferences around the globe about Big Data.


Website : https://www.zeolearn.com

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