infyni

Data Science & Introduction to Machine Learning

A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.

Live Course

Live Class: Wednesday, 24 Feb

Duration: 30 Hours

Enrolled: 1

Offered by: infyni

Live Course
$375 20% off

$300

About Course

Data Science is the study of the extraction of knowledge from data. Being a data scientist requires an integrated skill set spanning over applied mathematics & statistics, machine learning, and other branches of computer science along with a good understanding of the craft of problem formulation to engineer effective solutions. This course will introduce students to this rapidly growing field and equip them with basic principles and tools as well as its general mindset. Students will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modelling, descriptive modelling, evaluation, and effective communication.

Skills You Will Gain

Logistic Regression Artificial Neural Network Machine Learning (ML) Algorithms Machine Learning

Course Offerings

  • Instructor-led interactive classes
  • Clarify your doubts during class
  • Access recordings of the class
  • Attend on mobile or tablet
  • Live projects to practice
  • Case studies to learn from
  • Lifetime mentorship support
  • Industry specific curriculum
  • Certificate of completion
  • Employability opportunity
  • Topics
  • Instructor (1)
  • What is Data Science?: Big Data and Data Science hype and getting past the hype Why now?
  • Statistical Inference
  • Populations and samples
  • Statistical modeling
  • probability distributions
  • Bias Variance Tradeoff
  • Exploratory Data Analysis and the Data Science Process
  • Basic tools (plots, graphs and summary statistics) of EDA
  • Philosophy of EDA
  • The Data Science Process
  • Case Study: Kaggle
  • Parametric and Non
  • parametric methods
  • supervised
  • semi-supervised and unsupervised learning.
  • Case Study on Regression using kaggle data set.

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