All businesses introduce new products for various reasons. The new products poses challenge for the planners and marketing executives to estimate the demand for them for merchandise and supply planning purposes. The primary reason being the lack of historical data that can be used for forecasting. These techniques are'By Analogy' and'Bass Diffusion' including a live demonstration using a planning software. While Analogy is the more popular technique, the issue most planners face in this technique is in choosing the right analogue product.

Learn how to select features and build simpler, faster and more reliable machine learning models. This is the most comprehensive, yet easy to follow, course for feature selection available online. Throughout this course you will learn a variety of techniques used worldwide for variable selection, gathered from data competition websites and white papers, blogs and forums, and from the instructor's experience as a Data Scientist. You will have at your fingertips, altogether in one place, multiple methods that you can apply to select features from your data set. The course starts describing simple and fast methods to quickly screen the data set and remove redundant and irrelevant features.

Machine learning worries a lot of developers when it comes to analyzing complex statistical problems. Knowing that statistics helps you build strong machine learning models that optimizes a given problem statement. This Learning Path will teach you all it takes to perform complex statistical computations required for machine learning. So, if you are a developer with little or no background in statistics and want to implement machine learning in their systems, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This video will teach you all it takes to perform the complex statistical computations required for Machine Learning. You will gain information on statistics behind unsupervised learning, reinforcement learning, and more. You'll master real-world examples that discuss the statistical side of Machine Learning.

If you're looking to gain a solid foundation in Machine Learning to further your career goals, in a way that allows you to study on your own schedule at a fraction of the cost it would take at a traditional university, this online course is for you. If you're a working professional needing a refresher on machine learning or a complete beginner who needs to learn Machine Learning for the first time, this online course is for you. Why you should take this online course: You need to refresh your knowledge of machine learning for your career to earn a higher salary. You need to learn machine learning because it is a required mathematical subject for your chosen career field such as data science or artificial intelligence. You intend to pursue a masters degree or PhD, and machine learning is a required or recommended subject.

Spark lets you apply machine learning techniques to data in real time, giving users immediate machine-learning based insights based on what's happening right now. Using Spark, we can create machine learning models and programs that are distributed and much faster compared to standard machine learning toolkits such as R or Python. In this course, you'll learn how to use the Spark MLlib. You'll find out about the supervised and unsupervised ML algorithms. You'll build classifications models, extracting proper futures from text using Word2Vect to achieve this.

Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This video will teach you all it takes to perform complex statistical computations required for Machine Learning. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. We will discuss the application of frequently used algorithms on various domain problems, using both Python and R programming.

Learn how to engineer features and build more powerful machine learning models. This is the most comprehensive, yet easy to follow, course for feature engineering available online. Throughout this course you will learn a variety of techniques used worldwide for data cleaning and feature transformation, gathered from data competition websites and white papers, blogs and forums, and from the instructor's experience as a Data Scientist. You will have at your fingertips, altogether in one place, a variety of techniques that you can apply to capture as much insight as you possibly can with the features of your data set. The course starts describing the most simple and widely used methods for feature engineering, and then describes more advanced and innovative techniques that automatically capture insight from your variables.