by Shovon Sengupta | Released March 2019 | ISBN: 9781789800289

Master the top 7 powerful and advanced algorithms and excel in Machine LearningAbout This VideoUnderstand 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 algorithmsIn DetailAre 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-DaysDownloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you. Show and hide more

1. Chapter 1 : Linear Models
   • The Course Overview 00:03:37
   • Introduction to Linear Regression 00:08:58
   • Various concepts around Linear Regression 00:07:12
   • Using Linear Regression for prediction 00:14:59
   • Advantages and Limitations of Linear Regression 00:03:44
   • Case Study – Linear Regression 00:19:54
   • Introduction to Logistic Regression 00:07:16
   • Various Concepts around Logistic Regression 00:07:30
   • How Logistic Regression Can Be Used for Multi-Class Classification 00:22:14
   • Advantages and Limitations of Logistic Regression 00:03:19
   • Case Study – Logistic Regression 00:18:37
   • Homework Assignment – Linear Models 00:02:04
2. Chapter 2 : Decision Tree Algorithm
   • Introduction to Decision Tree 00:04:58
   • Concepts - Various Decision Tree Algorithms 00:05:37
   • Various Components of Decision Tree 00:04:00
   • Advantages and Disadvantages of Decision Tree Algorithm 00:04:21
   • Case Study – IBM’s HR Attrition Data 00:24:47
   • Homework Assignment – Decision Tree Algorithm 00:01:46
3. Chapter 3 : Random Forest Algorithm
   • Introduction to Random Forest Algorithm 00:05:03
   • Concepts of Random Forest Algorithm 00:05:27
   • Various components of Random Forest Algorithm 00:05:40
   • Advantages and Disadvantages of Random Forest Algorithm 00:04:03
   • Case Study - IBM's HR Attrition Data 00:12:16
   • Homework Assignment – Random Forest Algorithm 00:01:53
4. Chapter 4 : K-Means Clustering Algorithm
   • Introduction to K-Means Clustering 00:04:44
   • Concepts of K-Means Clustering Algorithm 00:06:18
   • Different Clustering Methods 00:04:32
   • Advantages and Disadvantages of K-Means Clustering Algorithm 00:01:04
   • Case Study – Iris Dataset 00:11:46
   • Homework Assignment - K-Means Clustering Algorithm 00:01:18
5. Chapter 5 : K-Nearest Neighbors Algorithm
   • Introduction to KNN Algorithm 00:04:06
   • Concepts of KNN Algorithm 00:05:52
   • Advantages and Limitations of KNN Algorithm 00:02:37
   • Case Study – Income Census Dataset 00:15:36
   • Homework Assignment – KNN Algorithm 00:01:53
6. Chapter 6 : Naïve Bayes Algorithm
   • Introduction to Naïve Bayes Algorithm 00:03:04
   • Concepts of Naïve Bayes Algorithm 00:05:42
   • Advantages and Limitations of Naïve Bayes Algorithm 00:03:03
   • Case Study – Bank Marketing Dataset 00:14:16
   • Homework Assignment - Naïve Bayes Algorithm 00:01:19
7. Chapter 7 : Time Series Analysis
   • Introduction to Time Series Analysis 00:06:32
   • Various Concepts around Time Series Model 00:04:38
   • Full overview of ARIMA/ SARIMA Model 00:08:28
   • Forecast Accuracy Measure – Time Series Analysis 00:03:24
   • Case Study – CPI Inflation Dataset 00:18:43
   • Homework Assignment - Time Series Analysis 00:03:59
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