All the courses include a series of hands-on labs and final projects that help you focus on a specific project that interests you. Become a Business Analyst and learn Business Problem Solving, Exploratory Data Analysis, Predictive Analytics and more. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. This course provides an overview of various machine learning techniques with examples of how they are used in various organizations such as retail, finance, biotechnology and social media. It also complements your learning with special topics, including Time Series Analysis and Survival Analysis. Â© 2020 Coursera Inc. All rights reserved. You will be able to derive and communicate insights from data using Exploratory Data Analysis, Supervised Learning, Unsupervised Learning, Deep Learning, Time Series Analysis, and Survival Analysis. Identify machine learning options available to solve business questions. With recent advances in deep learning technologies, Machine Learning and Artificial Intelligence is gathering momentum to be one of the key pillars of the next Industry Revolution. You will follow along and code your own projects using some of the most relevant open source frameworks and libraries. Do I need to take the courses in a specific order? SQL-Proficient Data Analyst. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing. They will learn both the mathematical theory, and get a hands-on experience of applying this theory to actual data using Python. -Use oversampling and undersampling as techniques to handle unbalanced classes in a data set Articulate why feature scaling is important and use a variety of scaling techniques This course targets aspiring data scientists interested in acquiring hands-on experienceÂ with Machine Learning and Artificial Intelligence in a business setting. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. Â Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. Use a variety of techniques for detecting and dealing with outliers This Professional Certificate is designed specifically for scientists, software developers, and business analysts who want to round their analytical skills in Data Science, AI, and Machine Learning, but is also appropriate for anyone with a passion for data and basic Math, Statistics, and programming skills. Visit the Learner Help Center. Who should take this course? After that, we donât give refunds, but you can cancel your subscription at any time. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Machine Learning. Format. Describe and use common clustering and dimensionality-reduction algorithms We start small, provide a solid theoretical background and code-along labs and demos, and build up to more complex topics. The Business Analyst in the World of Artificial Intelligence and Machine Learning
In the on-going debate over whether Artificial Intelligence, and its allied technology Machine Learning, can completely replace the human brain, many scientists are still not convinced that machines can be trained to both “think” and “feel” like humans. Use regularization regressions: Ridge, LASSO, and Elastic net What skills should you have? You will learn about training data, and how to use a set of data to discover potentially predictive relationships. -Describe and use other ensemble methods for classification Use a variety of error metrics to compare and select a linear regression model that best suits your data This course is unique in many ways: 1. To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.