Machine Learning Algorithms in 7 Days [Video]
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: https://github.com/PacktPublishing/Machine-Learning-Algorithms-in-7-DaysStyle 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.
|Course Length||5 hours 40 minutes|
|Date Of Publication||30 Mar 2019|
|Introduction to Linear Regression|
|Various concepts around Linear Regression|
|Using Linear Regression for prediction|
|Advantages and Limitations of Linear Regression|
|Case Study – Linear Regression|
|Introduction to Logistic Regression|
|Various Concepts around Logistic Regression|
|How Logistic Regression Can Be Used for Multi-Class Classification|
|Advantages and Limitations of Logistic Regression|
|Case Study – Logistic Regression|
|Homework Assignment – Linear Models|
|Concepts - Various Decision Tree Algorithms|
|Various Components of Decision Tree|
|Advantages and Disadvantages of Decision Tree Algorithm|
|Case Study – IBM’s HR Attrition Data|
|Homework Assignment – Decision Tree Algorithm|
|Concepts of Random Forest Algorithm|
|Various components of Random Forest Algorithm|
|Advantages and Disadvantages of Random Forest Algorithm|
|Case Study - IBM's HR Attrition Data|
|Homework Assignment – Random Forest Algorithm|
|Concepts of Naïve Bayes Algorithm|
|Advantages and Limitations of Naïve Bayes Algorithm|
|Case Study – Bank Marketing Dataset|
|Homework Assignment - Naïve Bayes Algorithm|