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Machine Learning Terminology & Process For Beginners

Learn Machine Learning Terminology & Process and gain solid understanding along with hand-on labs. Master 5 Steps of ML

Course Description

In the world of Artificial Intelligence and Machine Learning with Cloud Computing and Big Data. Learn Machine Learning Terminology & Process For Beginners step-by-step while gaining solid understanding hands-on - Bringing you the latest technologies with up-to-date knowledge.

Are you new to machine learning? Are you looking to enhance you skills within the AWS ecosystem or perhaps pursue AWS certifications?Look no further - learn and acquire new skills with this Machine Learning Terminology & Process For Beginners course

Course Description

Welcome to Machine Learning Terminology & Process For Beginners - A one of its kind course! It is not only a comprehensive course, you are will not find a course similar to this.

AWS Machine learning is not free. Please note that you may incur additional costs from AWS.

In this course, you'll learn and practice:

  1. Basic machine learning terminology and process

  2. Learn how to frame a machine learning problem and when to use machine learning

  3. Prepare and develop data sets

  4. Gain solid understanding of feature engineering and data visualizations

  5. Work with model training and evaluation

  6. Learn about business goal evaluation

  7. See machine learning prediction, and much more

In this course, you will also get complete resources, and code where applicable with this course! We've built this course with our Team ClayDesk of industry recognized developers and consultants to bring you the best of everything

So, if you would like to:

  • Gain solid understanding of Machine Learning, this course is for you

  • Gain marketable skills as an IT expert and professional, this course is for you

  • This course is not designed for intermediate or advanced level students

This Machine Learning Terminology & Process For Beginners is exactly what you need, and more. You’ll even get a certification of completion

What out students say.

See what our students say “It is such a comprehensive course that I don’t need to take any other course but this one to learn all of Machine Learning processes and important terminology along with demos - Absolutely worth it” - Chavez

“This is such an awesome course. I loved every bit of it – Wonderful learning experience!” Jill Neumann.

Join thousands of other students and share valuable experience

Why take this course?

As a senior Project Manager & Web developer, managing and deploying enterprise level IT projects, along with a Microsoft Certified Systems Engineer & Trainer, my experience with Machine Learning projects has been phenomenally great. I am excited to share my knowledge and transfer skills to my students.

Enroll now in Machine Learning Terminology & Process For Beginners today and revolutionize your learning. Stay at the cutting edge of enterprise cloud computing and enjoy bigger, brighter opportunities.

See you in class

Syed & Team ClayDesk

Goals

  • Machine learning is a field of study that focuses on developing algorithms and models that allow computers to learn from data and make predictions or decisions without being explicitly programmed. For beginners in machine learning, understanding the terminology and process is essential.
  • Terminology in machine learning includes concepts like datasets, features, labels, models, training, testing, and evaluation. A dataset is a collection of examples or instances that the machine learning algorithm will learn from. Each example consists of features, which are the measurable characteristics or attributes of the data, and labels, which are the desired outputs or targets that the model should predict. Models in machine learning are mathematical representations or algorithms that map input features to output labels.
  • The process of machine learning involves several steps. The first step is data preprocessing, which includes tasks like data cleaning, handling missing values, and feature scaling. The next step is splitting the dataset into training and testing sets. The training set is used to train the model by feeding the input features and their corresponding labels. The model learns from the training data and adjusts its internal parameters to minimize the difference between the predicted labels and the actual labels.
  • After training, the model is evaluated using the testing set to assess its performance. Evaluation metrics such as accuracy, precision, recall, and F1 score are commonly used to measure the model's effectiveness. If the model performs well on the testing set, it can be deployed to make predictions on new, unseen data.
  • It's important for beginners in machine learning to familiarize themselves with these concepts and steps to build a solid foundation. Additionally, exploring different algorithms like linear regression, decision trees, and neural networks can help gain a deeper understanding of the field. As beginners gain more experience, they can delve into advanced topics such as deep learning, reinforcement learning, and natural language processing, which open up new possibilities in machine learning applications.

Prerequisites

  • Basic statistic concept are desirable but not required

  • AWS account for free tier requires a credit card

  • Machine learning with AWS is not free. Watchful eye on billing alerts must be kept

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Curriculum

  • Course Promo & Instructir Bio
    03:44
    Preview
  • Introduction - Course Agenda
    05:38
  • What Will I Learn?
    04:50
    Preview
  • When Do You use Machine Learning?
    04:34
    Preview
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Machine Learning Terminology & Process For Beginners
This Course Includes
  • 3 hours
  • 27 Lectures
  • Completion Certificate Sample Certificate
  • Lifetime Access Yes
  • Language English
  • 30-Days Money Back Guarantee
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