Machine Learning Algorithms in 7 Days [Video]

More Information
  • Build awesome ML solutions for your business problems
  • Easy and fast way to learn and use ML algorithms without being bothered about theoretical jargons
  • Apply ML algorithms to design your own solution to business problems
  • The course is updated and enhanced, and fully supports Python 3. This guarantees what you're learning is quite relevant for you today
  • Get to know seven ML algorithms in this concise, insightful guide

Are you really keen to learn some cool machine learning algorithms that are making headlines these days? 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.

This course offers an easy gateway to learn about 7 key algorithms in the realm of Data Science and Machine Learning. You will learn 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 existing trends in your datasets.

This video addresses 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. 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 course, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem. You will be able to easily and confidently build and implement data science algorithms.

All the code and supporting files for this course are available on:

Style and Approach

This is a fast-paced course offering practical and actionable guidance with step-by-step instructions and assignments to help you master various carefully chosen machine learning algorithms based on their usability and practical applications. You'll learn about these algorithms not only theoretically but also practically: you'll build solutions via hands-on instructions using a variety of techniques, which will offer you a detailed perspective about their usage.

  • Understand which machine learning algorithm to pick for clustering, classification, or regression and which one is most suitable for your problem.
  • Address problems related to accurate and efficient data classification and prediction.
  • Easily and confidently build and implement data science algorithms
Course Length 5 hours 40 minutes
ISBN 9781789800289
Date Of Publication 30 Mar 2019
Concepts of KNN Algorithm
Advantages and Limitations of KNN Algorithm
Case Study – Income Census Dataset
Homework Assignment – KNN Algorithm
Concepts of Naïve Bayes Algorithm
Advantages and Limitations of Naïve Bayes Algorithm
Case Study – Bank Marketing Dataset
Homework Assignment - Naïve Bayes Algorithm
Various Concepts around Time Series Model
Full overview of ARIMA/ SARIMA Model
Forecast Accuracy Measure – Time Series Analysis
Case Study – CPI Inflation Dataset
Homework Assignment - Time Series Analysis


Shovon Sengupta

Shovon Sengupta is an experienced data scientist with over 10 years' experience in advanced predictive analytics, machine learning, deep learning, and reinforcement learning. He has worked extensively in designing award winning solutions for various organizations, for different business problems in the realm of Finance. Currently, he works as Senior Lead Data Scientist at one of the leading NBFCs in USA.

Shovon holds an MS in Advanced Econometrics from one of the leading universities in India. You can follow him at his LinkedIn ID: