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Machine Learning for Aspiring Data Scientists: Zero to Hero

4.7

(18 students)
Last updated:October 30, 2024
Language:English

Machine Learning for Aspiring Data Scientists: Zero to Hero

4.7

(18 students)

Rated Excellent on

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Overview

Welcome to “Machine Learning for Aspiring Data Scientists: Zero to Hero,” a comprehensive course designed to equip beginners with the essential skills and knowledge needed to excel in the field of data science. This course takes a step-by-step approach, starting from the fundamentals of machine learning and advancing to complex algorithms and applications. You will learn through engaging lectures, hands-on projects, and real-world case studies, ensuring a practical understanding of concepts. The course covers topics including supervised and unsupervised learning, data preprocessing, model evaluation, and deployment. By the end of the course, you will not only grasp theoretical aspects but also gain hands-on experience using popular tools and frameworks such as Python, Scikit-Learn, and TensorFlow. This program is structured to foster critical thinking and problem-solving skills, preparing you for a career in data science and related fields.

Learning Outcomes

By the end of this course, you will be able to:

  • Understand fundamental machine learning concepts and techniques.
  • Implement supervised and unsupervised learning algorithms.
  • Preprocess and analyze datasets effectively.
  • Evaluate and fine-tune models for optimal performance.
  • Apply machine learning tools and libraries in real-world scenarios.

How will I get my certificate?

You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.

Who is This course for?

This course is ideal for aspiring data scientists, analysts, and anyone interested in learning machine learning from scratch. It’s perfect for students, professionals looking to transition into data science, or enthusiasts eager to expand their technical skills in a rapidly evolving field.

Requirements

No prior experience in programming or data science is needed, but a basic understanding of Python and statistics will be beneficial. Participants should have access to a computer with internet connectivity, enabling them to engage in hands-on exercises and projects throughout the course.

Career Path

  • Data Scientist
  • Machine Learning Engineer
  • Data Analyst
  • Business Intelligence Developer
  • Research Scientist

Course Curriculum

  • 15 sections
  • 190 lectures
  • 15 hours, 50 minutes total length
Expand all sections
  • video Modeling an epidemic
    00:08:00
  • video The machine learning recipe
    00:06:00
  • video The components of a machine learning model
    00:02:00
  • video Why model?
    00:03:00
  • video On assumptions and can we get rid of them?
    00:09:00
  • video The case of AlphaZero
    00:11:00
  • video Overfitting/underfitting/bias/variance
    00:11:00
  • video Why use machine learning
    00:05:00

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This course includes:

  • Duration:
    15 hours, 50 minutes
  • Access:
    Lifetime access
  • Units:
    190
  • Assignments:
    No
  • Quizzes:
    No
  • Certificate:
    Yes