Customer Analytics in Python 2020 Get udemy course coupon code Customer Analytics in Python – the place where marketing and data science meet! What will you learn in this course? We will introduce you to the relevant theory that you need to start performing customer analytics. Then we will perform cluster analysis and dimensionality reduction to help you segment your customers. What you'll learn Master beginner and advanced customer analytics Learn the most important type of analysis applied by mid and large companies Gain access to a professional team of trainers with exceptional quant skills Wow interviewers by acquiring a highly desired skill Understand the fundamental marketing modeling theory: segmentation, targeting, positioning, marketing mix, and price elasticity; Apply segmentation on your customers, starting from raw data and reaching final customer segments; Perform K-means clustering with a customer analytics focus; Apply Principal Components Analysis (PCA) on your data to preprocess your features; Combine PCA and K-means for even more professional customer segmentation; Deploy your models on a different dataset; Learn how to model purchase incidence through probability of purchase elasticity; Model brand choice by exploring own-price and cross-price elasticity; Complete the purchasing cycle by predicting purchase quantity elasticity Carry out a black box deep learning model with TensorFlow 2.0 to predict purchasing behavior with unparalleled accuracy Be able to optimize your neural networks to enhance results Description Data science and Marketing are two of the key driving forces that help companies create value and stay on top in today's fast-paced economy.
With solid roots in statistics, Machine Learning is getting one of the most intriguing and quick-paced computer science fields to work in. There's an unending supply of enterprises and applications machine learning can be applied to make them increasingly proficient and wise. Chatbots, spam filtering, ad serving, search engines, and fraud detection, are among only a couple of instances of how machine learning models support regular day to day life. Machine Learning is the thing that lets us discover patterns and make mathematical models for things that would sometimes be unthinkable for people to do. Not at all like data science courses, which contain subjects like exploratory data analysis, statistics, communication, and visualization techniques, machine learning courses concentrate on teaching just the machine learning algorithms, how they work numerically, and how to use them in a programming language.
Model evaluation involves using the available dataset to fit a model and estimate its performance when making predictions on unseen examples. It is a challenging problem as both the training dataset used to fit the model and the test set used to evaluate it must be sufficiently large and representative of the underlying problem so that the resulting estimate of model performance is not too optimistic or pessimistic. The two most common approaches used for model evaluation are the train/test split and the k-fold cross-validation procedure. Both approaches can be very effective in general, although they can result in misleading results and potentially fail when used on classification problems with a severe class imbalance. In this tutorial, you will discover how to evaluate classifier models on imbalanced datasets.
This extensive course leads you through a complete range of software skills and languages, skilling you up to be an incredibly on-demand developer. The combination of being able to create full-stack websites AND machine learning and AI models is very rare - something referred to as a unAIcorn. This is exactly what you will be able to do by the end of this course. Whether you're looking to get into a high paying job in tech, aspiring to build a portfolio so that you can land remote contracts and work from the beach, or you're looking to grow your own tech start-up, this course will be essential to set you up with the skills and knowledge to develop you into a unAIcorn. It won't matter if you're a complete beginner to software or a seasoned veteran.
This is the third instalment of Data Science Crash Course and today we're going to review mathematics needed for Data Science. Linear algebra is all about manipulations with vectors and matrices. It's both notation and useful way of manipulating object. You can perform operations on vectors like adding by adding each respective term -- they need to have the same length. You can multiply a vector by a scalar, that is a real number, by multiplying each of the entries by this real number.
Machine Learning is a fast growing, rapidly advancing field that touches nearly everyone's lives. There has recently been an explosion of successful machine learning applications - in everything from voice recognition to text analysis to deeper insights for researchers. While common and frequently talked about, most people have only a vague concept of how machine learning actually works. In this tutorial, Dr. Artemy Kolchinsky and Dr. Brendan Tracey outline exactly what it is that makes machine learning so special in an accessible way. The principles of training and generalization in machine learning are explained with ample metaphors and visual intuitions, an extended analysis of machine learning in games provides a thorough example, and a closer look at the deep neural nets that are the core of successful machine learning.
From facial recognition to self-driving vehicles, machine learning is taking over modern life as we know it. It may not be the flying cars and world-dominating robots we envisioned 2020 would hold, but it's still pretty futuristic and frightening. The good news is if you're one of the pros making these smart systems and machines, you're in good shape. And you can get your foot in the door by learning the basics with this Essential AI and Machine Learning Certification Training Bundle. This training bundle provides four comprehensive courses introducing you to the world of artificial intelligence and machine learning.
Finnish technology firm Reaktor and the University of Helsinki joined forces to educate people on AI for free. The institutions combined to develop an online course to teach the basics of AI to anyone interested in the technology. Reaktor and the University also challenged organizations to train their staff in AI, so far over 200 organisations have pledged to do so – including banks, telecoms, and healthcare organizations. Almost 90 000 students have signed up for the course since it began in May. While popular with Finns, the course is already seeing strong demand globally, attracting students from over 80 different countries.