This tutorial provides a gentle introduction to the particle Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear state-space models together with a software implementation in the statistical programming language R. We employ a step-by-step approach to develop an implementation of the PMH algorithm (and the particle filter within) together with the reader. This final implementation is also available as the package pmhtutorial in the CRAN repository. Throughout the tutorial, we provide some intuition as to how the algorithm operates and discuss some solutions to problems that might occur in practice. To illustrate the use of PMH, we consider parameter inference in a linear Gaussian state-space model with synthetic data and a nonlinear stochastic volatility model with real-world data.

One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. The course will teach you those fundamental concepts by implementing practical exercises which are based on live examples.

One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. A DIFFERENT & MORE EFFECTIVE APPROACH TO LEARNING DATA SCIENCE: In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. WE'LL BUILD SUPERVISED MACHINE LEARNING ALGORITHMS TOGETHER: I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. LEARN BOTH THE THEORY & APPLICATION OF MACHINE LEARNING: The course will teach you those fundamental concepts by implementing practical exercises which are based on live examples.

Pandas is hands down one of the best libraries of python. It supports reading and writing excel spreadsheets, CVS's and a whole lot of manipulation. It is more like a mandatory library you need to know if you're dealing with datasets from excel files and CSV files. This is part one of Pandas tutorial. I'm not going to cover everything possible with pandas, however, I want to give you a taste of what it is and how you can get started with it.

Numpy is a math library for python. It enables us to do computation efficiently and effectively. It is better than regular python because of it's amazing capabilities. In this article I'm just going to introduce you to the basics of what is mostly required for machine learning and datascience. I'm not going to cover everything that's possible with numpy library.

In this post, I explore strategies to switch to Data Science mid-career. This switch is not easy, but based on the experience of many who I have taught/mentored/recruited – it is possible. Most people consider PhD/MooC etc for switching their career to Data Science. But here, I will explore some non-traditional/unorthodox ways of switching to Data Science. Also, most algorithms improve previous benchmarks – but the task itself remains the same. For example, Churn prevention / Fraud detection etc are well defined industry problems.

Would you like to take a course on Keras and deep learning in Python? Consider taking DataCamp's Deep Learning in Python course! Also, don't miss our Keras cheat sheet, which shows you the six steps that you need to go through to build neural networks in Python with code examples! Before going deeper into Keras and how you can use it to get started with deep learning in Python, you should probably know a thing or two about neural networks. As you briefly read in the previous section, neural networks found their inspiration and biology, where the term "neural network" can also be used for neurons.

In this step-by-step Keras tutorial, you'll learn how to build a convolutional neural network in Python! In fact, we'll be training a classifier for handwritten digits that boasts over 99% accuracy on the famous MNIST dataset. Before we begin, we should note that this guide is geared toward beginners who are interested in applied deep learning. Our goal is to introduce you to one of the most popular and powerful libraries for building neural networks in Python. That means we'll brush over much of the theory and math, but we'll also point you to great resources for learning those.

Machine Learning (ML) and Artificial Intelligence (AI) are the most difficult domains in terms of developing and implementing. There are a lot of pre and post processes involved while implementing a machine learning based solution to solve even the simplest problem. In general, machine learning implementation involves the following steps. The above four steps seem very simple in theory, however, when you try to implement these, a lot of resources and limitations catch your attention to achieve the desired outcome. This is, however, the part where we are simply trying to implement our solution to achieve the desired accuracy and we have not even yet touched the application part of what our solution is capable of offering.

Follow leaders in ML on twitter to see what research papers/blog posts/etc. This is a very effective but highly under-rated way to get good at ML. Having seen a lot of new Stanford PhD students grow to become great researchers, I can say confidently that replicating others' results (not just reading the papers) is one of the most effective ways to see and make sure you understand the details of the latest algorithms. Many people jump too quickly into trying to invent something new, which is also worth doing, but is actually a slower way to learn and build up your foundation of knowledge. When you do build something new, publish it in a paper or blog post and consider open-sourcing your code, and share it back out with the community! Hopefully this will help you get more feedback from the community, and further accelerate your learning.