This is the code repository for Machine Learning Algorithms in 7 Days [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
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.
- 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 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.
This course has the following requirements:
Operating system: Windows 10/ Mac OS
Browser: Mozilla/ Crome
Sublime text Editor, Latest Version
Anaconda to access the Jupiter Notebook to run the module and work on the case studies
Python 3.6