Machine Learning Algorithms in 7 Days

Machine Learning Algorithms in 7 Days, available at $19.99, has an average rating of 4.06, with 46 lectures, based on 8 reviews, and has 68 subscribers.

You will learn about 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 This course is ideal for individuals who are This course is for aspiring data science professionals who are familiar with Python and have some background about statistics. It is ideal for developers who are currently implementing one or two data science algorithms and want to learn more to expand their skillset. This course will be a great enabler for those who aspire to master some of the most relevant and oft-used algorithms in Machine Learning. It is particularly useful for This course is for aspiring data science professionals who are familiar with Python and have some background about statistics. It is ideal for developers who are currently implementing one or two data science algorithms and want to learn more to expand their skillset. This course will be a great enabler for those who aspire to master some of the most relevant and oft-used algorithms in Machine Learning.

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Summary

Title: Machine Learning Algorithms in 7 Days

Price: $19.99

Average Rating: 4.06

Number of Lectures: 46

Number of Published Lectures: 46

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

This course is for aspiring data science professionals who are familiar with Python and have some background about statistics. It is ideal for developers who are currently implementing one or two data science algorithms and want to learn more to expand their skillset. This course will be a great enabler for those who aspire to master some of the most relevant and oft-used algorithms in Machine Learning.

Target Audiences

This course is for aspiring data science professionals who are familiar with Python and have some background about statistics. It is ideal for developers who are currently implementing one or two data science algorithms and want to learn more to expand their skillset. This course will be a great enabler for those who aspire to master some of the most relevant and oft-used algorithms in Machine Learning.

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.

About the Author

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.

Course Curriculum

Chapter 1: Linear Models

Lecture 1: The Course Overview

Lecture 2: Introduction to Linear Regression

Lecture 3: Various concepts around Linear Regression

Lecture 4: Using Linear Regression for prediction

Lecture 5: Advantages and Limitations of Linear Regression

Lecture 6: Case Study – Linear Regression

Lecture 7: Introduction to Logistic Regression

Lecture 8: Various Concepts around Logistic Regression

Lecture 9: How Logistic Regression Can Be Used for Multi-Class Classification

Lecture 10: Advantages and Limitations of Logistic Regression

Lecture 11: Case Study – Logistic Regression

Lecture 12: Homework Assignment – Linear Models

Chapter 2: Decision Tree Algorithm

Lecture 1: Introduction to Decision Tree

Lecture 2: Concepts – Various Decision Tree Algorithms

Lecture 3: Various Components of Decision Tree

Lecture 4: Advantages and Disadvantages of Decision Tree Algorithm

Lecture 5: Case Study – IBM’s HR Attrition Data

Lecture 6: Homework Assignment – Decision Tree Algorithm

Chapter 3: Random Forest Algorithm

Lecture 1: Introduction to Random Forest Algorithm

Lecture 2: Concepts of Random Forest Algorithm

Lecture 3: Various components of Random Forest Algorithm

Lecture 4: Advantages and Disadvantages of Random Forest Algorithm

Lecture 5: Case Study – IBM's HR Attrition Data

Lecture 6: Homework Assignment – Random Forest Algorithm

Chapter 4: K-Means Clustering Algorithm

Lecture 1: Introduction to K-Means Clustering

Lecture 2: Concepts of K-Means Clustering Algorithm

Lecture 3: Different Clustering Methods

Lecture 4: Advantages and Disadvantages of K-Means Clustering Algorithm

Lecture 5: Case Study – Iris Dataset

Lecture 6: Homework Assignment – K-Means Clustering Algorithm

Chapter 5: K-Nearest Neighbors Algorithm

Lecture 1: Introduction to KNN Algorithm

Lecture 2: Concepts of KNN Algorithm

Lecture 3: Advantages and Limitations of KNN Algorithm

Lecture 4: Case Study – Income Census Dataset

Lecture 5: Homework Assignment – KNN Algorithm

Chapter 6: Naïve Bayes Algorithm

Lecture 1: Introduction to Naïve Bayes Algorithm

Lecture 2: Concepts of Naïve Bayes Algorithm

Lecture 3: Advantages and Limitations of Naïve Bayes Algorithm

Lecture 4: Case Study – Bank Marketing Dataset

Lecture 5: Homework Assignment – Naïve Bayes Algorithm

Chapter 7: Time Series Analysis

Lecture 1: Introduction to Time Series Analysis

Lecture 2: Various Concepts around Time Series Model

Lecture 3: Full overview of ARIMA/ SARIMA Model

Lecture 4: Forecast Accuracy Measure – Time Series Analysis

Lecture 5: Case Study – CPI Inflation Dataset

Lecture 6: Homework Assignment – Time Series Analysis

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 0 votes
4 stars: 1 votes
5 stars: 5 votes

Frequently Asked Questions

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