Foundations of Data Science: Machine Learning and Statistics

Foundations of Data Science: Machine Learning and Statistics

What you’ll learn

  • Python Proficiency: Master the use of Python for implementing machine learning algorithms, establishing a solid programming foundation.
  • Analytics Insight: Develop the ability to leverage analytics in machine learning, gaining valuable insights for informed decision-making.
  • Big Data Integration: Understand the challenges and opportunities in integrating Big Data with machine learning processes.
  • Statistical Fundamentals: Learn the basics of statistical sampling, data types, visualization, and probability theory crucial for data science.
  • Random Variables and Distributions: Grasp the concepts of random variables and various probability distributions essential in machine learning applications.
  • Matrix Algebra Skills: Acquire proficiency in matrix algebra and understand its significance in manipulating data for machine learning.
  • Hypothesis Testing Mastery: Develop skills in hypothesis testing, encompassing error types, critical value approaches, and P-value analysis.
  • Regression Analysis: Gain insights into covariance and regression analysis, fundamental techniques for predictive modeling in data science.
  • Practical Application: Apply learned concepts through quizzes, practical examples, and real-world scenarios for hands-on experience.
  • Emerging Trends Awareness: Stay updated on emerging trends and innovations in machine learning, ensuring relevance in a rapidly evolving field.

How to Enroll Foundations of Data Science: Machine Learning and Statistics course?

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