EtusivuHae koulutuksiaPractical Machine Learning

Practical Machine Learning

An introduction to R and Python programming languages, plus a deep coverage of the mathematics, algorithms and technology of Machine learning.
This 5-day course is designed for people who are already working with Big Data and analysing large statistical sets. By attending this course you'll learn what an effective Machine Learning approach looks like in an organisation.
You'll learn to understand different models of Machine Learning and how to implement them. Also covered is how to validate the statistical quality and the metrics attached to that data and how to implement them practically using Python and R.

Koulutusmuoto
Remote

Kesto
5 päivää

Hinta
5262 €

This 5-day course is designed for people who are already working on basic data science problems and starting the statistical analysis of data with python. By attending this course you'll learn what an effective Machine Learning approach looks like in an organisation.

You'll learn how to implement different Machine learning models, validate their quality and how to implement them practically.

Target Audience

This course is aimed at fledgling data scientists and analysts who wish to gain more in-depth knowledge of Machine Learning.

Learning outcomes

  • Explore and prepare data
  • Develop ML models
  • How to pick ML algorithms for a given task
  • Understand techniques and metrics used to determine the quality of ML models

Prerequisites

  • GCSE Mathematics or above. Must be comfortable with analytical and mathematical thinking.
  • Familiar with basic python programming: variables, control flow, scope, data structures and functions. Must be comfortable with algorithmic thinking.
  • Familiar with basics of data analysis including databases, descriptive statistics, and typical business use cases.

Course Content

  • Introduction to Data Science
  • Python for Data Science
  • Fundamentals of Statistics
  • Exploratory Data Analysis
  • Data Preparation
  • Choosing ML Algorithms
  • Building ML Models
  • Evaluating ML algorithms and Model Selection
  • Deploying ML Models