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Home » Technology » Big Data & Data Science » Data Science Algorithms in a Week

Course Skill Level:

Foundational

Course Duration:

7 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    DSAIAWL21E09

Who should attend & recommended skills:

Python experienced developers

Who should attend & recommended skills

  • This course is designed for developers interested in building a strong foundation of machine learning algorithmsin7 days.
  • Python: Basic (1-2 years’ experience).

About this course

This course will address the problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on the existing trends in your datasets. This course covers algorithms such as: k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-series. On completion of the book, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem.

Skills acquired & topics covered

  • Find out how to classify using Naive Bayes, Decision Trees, and Random Forest to achieve accuracy to solve complex problems
  • Identify a data science problem correctly and devise an appropriate prediction solution using Regression and Time-series
  • See how to cluster data using the k-Means algorithm
  • Get to know how to implement the algorithms efficiently in the Python and R languages

Course breakdown / modules

  • Mary and her temperature preferences
  • Implementation of k-nearest neighbors algorithm
  • Map of Italy example – choosing the value of k
  • House ownership – data rescaling
  • Text classification – using non-Euclidean distances
  • Text classification – k-NN in higher-dimensions

  • Medical test – basic application of Bayes’ theorem
  • Proof of Bayes’ theorem and its extension
  • Playing chess – independent events
  • Implementation of naive Bayes classifier
  • Playing chess – dependent events
  • Gender classification – Bayes for continuous random variables

  • Swim preference – representing data with decision tree
  • Information theory
  • ID3 algorithm – decision tree construction
  • Classifying with a decision tree
  • Playing chess – analysis with decision tree
  • Going shopping – dealing with data inconsistency

  • Overview of random forest algorithm
  • Swim preference – analysis with random forest
  • Implementation of random forest algorithm
  • Playing chess example
  • Going shopping – overcoming data inconsistency with randomness and measuring the level of confidence

  • Household incomes – clustering into k clusters
  • Gender classification – clustering to classify
  • Implementation of the k-means clustering algorithm
  • House ownership – choosing the number of clusters
  • Document clustering – understanding the number of clusters k in a semantic context

  • Fahrenheit and Celsius conversion – linear regression on perfect data
  • Weight prediction from height – linear regression on real-world data
  • Gradient descent algorithm and its implementation
  • Flight time duration prediction from distance
  • Ballistic flight analysis – non-linear model

  • Business profit – analysis of the trend
  • Electronics shop’s sales – analysis of seasonality

  • Basic concepts
  • Bayesian Inference
  • Distributions
  • Cross-validation
  • A/B Testing

  • Introduction
  • Data types
  • Linear regression

  • Introduction
  • Data types
  • Flow control

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