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Data Science Algorithms in a Week

  • Course Code: Data Science - Data Science Algorithms in a Week
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants to Build strong foundation of machine learning algorithms In 7 days.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Data Science Algorithms skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Build strong foundation of machine learning algorithms In 7 days. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Machine learning applications are highly automated and self-modifying, and they continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly. Data science helps you gain new knowledge from existing data through algorithmic and statistical analysis. 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. 

Working in a hands-on learning environment, led by our Data Science Algorithms expert instructor, students will learn about and explore: 

  • Get to know seven algorithms for your data science needs in this concise, insightful guide 
  • Ensure you’re confident in the basics by learning when and where to use various data science algorithms 
  • Learn to use machine learning algorithms in a period of just 7 days 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • 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 

Audience & Pre-Requisites 

This course is designed for developers interested to Build strong foundation of machine learning algorithms In 7 days. 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Classification Using K Nearest Neighbors 
  • Classification Using K Nearest Neighbors 
  • 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 
  1. Naive Bayes 
  • Naive Bayes 
  • 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 
  1. Decision Trees 
  • Decision Trees 
  • 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 
  1. Random Forest 
  • Random Forest 
  • 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 
  1. Clustering into K Clusters 
  • Clustering into K Clusters 
  • 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 
  1. Regression 
  • Regression 
  • 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 
  1. Time Series Analysis 
  • Time Series Analysis 
  • Business profit – analysis of the trend 
  • Electronics shop’s sales – analysis of seasonality 
  1. Statistics 
  • Statistics 
  • Basic concepts 
  • Bayesian Inference 
  • Distributions 
  • Cross-validation 
  • A/B Testing 
  1. R Reference 
  • R Reference 
  • Introduction 
  • Data types 
  • Linear regression 
  1. Python Reference 
  • Python Reference 
  • Introduction 
  • Data types 
  • Flow control 
  1. Glossary of Algorithms and Methods in Data Science 
  • Glossary of Algorithms and Methods in Data Science 

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