Machine learning is one of the most promising career paths one can pursue. The career entails building systems that can act autonomously without being explicitly programmed or closely supervised. Being a machine learning expert will introduce you to a new world of opportunities. Your expertise in this field will always be in high demand. Let's say you want to learn machine learning.
Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees. Random Forest Algorithm in Machine Learning: Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.
Last October, Google Developers brought their Machine Learning Bootcamp to Jakarta, Indonesia! ML Bootcamp is a one-stop solution to learn about Google's latest machine learning offerings from both Googlers and other industry experts. The 4-day intensive bootcamp consists of instructor-led trainings, hands-on codelabs, and saw 35 companies, as well as 12 startups represented from across Indonesia. If you're an aspiring ML developer, be sure to check out the following online courses: ML crash course with TensorFlow APIs http://bit.ly/2MLUDkU
Traditional approaches to leadership development no longer meet the needs of organizations or individuals. There are three: (1) Organizations, which pay for leadership development, don't always benefit as much as individual learners do. A growing assortment of online courses, social platforms, and learning tools from both traditional providers and upstarts is helping to close the gaps. The need for leadership development has never been more urgent. Companies of all sorts realize that to survive in today's volatile, uncertain, complex, and ambiguous environment, they need leadership skills and organizational capabilities different from those that helped them succeed in the past. There is also a growing recognition that leadership development should not be restricted to the few who are in or close to the C-suite. With the proliferation of collaborative problem-solving platforms and digital "adhocracies" that emphasize individual initiative, employees across the board are increasingly expected to make consequential decisions that align with corporate strategy and culture.
There is an undeniable truth that Artificial Intelligence, which we will refer to simply as AI, is the next frontier for the healthcare industry. Several sources have already pegged the market to be worth $36.1 billion by 2025. For those of you who like simple language; the way AI works is by having it developed through machine learning, natural language processing, and deep learning. This process is controlled by programmers, who in a lot of cases are independent contractors. Regulatory frameworks will soon be created to govern this new boom, with consulting and online training courses becoming the next cash cows milking this industry for profits.
A complete step by step course to master IBM SPSS Statistics for doing advanced Research, Statistics & Data Analysis Data is the new frontier of 21st century. According to a Harvard Business Report (2012) data science is going to be the hottest job of 21st century and data analysts have a very bright career ahead. This course aims to equip learners with ability of independently carrying out in-depth data analysis with professional confidence and accuracy. It will specifically help those looking to derive business insights, understand consumer behaviour, develop objective plans for new ventures, brand study, or write a scholarly articles in high impact journals and develop high quality thesis/project work. What you'll learn Analyse any type of numerical data using SPSS with confidence Independently plan your research study from scratch.
When making a start in a new field it is common to feel overwhelmed. You may lack confidence or feel as though you are not good enough or that you are lacking some prerequisite. You will explore these issues in this post and learn that such feelings can lead to actions that can consume a lot of time and resources and leave you feeling disappointed in yourself. You will learn that there are many paths through the field of machine learning and that like programming, it is a meritocracy. Feeling like you are not good enough or that you are lacking some skill that you think you must have before you can make a start in machine learning is dangerous thinking.
In this tutorial, you will learn how to train a Keras deep learning model to predict breast cancer in breast histology images. Back 2012-2013 I was working for the National Institutes of Health (NIH) and the National Cancer Institute (NCI) to develop a suite of image processing and machine learning algorithms to automatically analyze breast histology images for cancer risk factors, a task that took trained pathologists hours to complete. Back then deep learning was not as popular and "mainstream" as it is now. For example, the ImageNet image classification challenge had only launched in 2009 and it wasn't until 2012 that Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton won the competition with the now infamous AlexNet architecture. To analyze the cellular structures in the breast histology images we were instead leveraging basic computer vision and image processing algorithms, but combining them in a novel way.
You will learn how to explore and analyse your data, build robust and accurate time series models and understand your forecasting performance. More information and registration Business Forecasting with R (9th & 10th May) This course will discuss how to forecast using R and will cover the functionality of various packages to directly test on real-world examples.
Configuring neural network models is often referred to as a "dark art." This is because there are no hard and fast rules for configuring a network for a given problem. We cannot analytically calculate the optimal model type or model configuration for a given dataset. Fortunately, there are techniques that are known to address specific issues when configuring and training a neural network that are available in modern deep learning libraries such as Keras. In this crash course, you will discover how you can confidently get better performance from your deep learning models in seven days.