This course is about the fundamental concepts of algorithmic problems, focusing on backtracking and dynamic programming. As far as I am concerned these techniques are very important nowadays, algorithms can be used (and have several applications) in several fields from software engineering to investment banking or research&development. In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. The first chapter is about backtracking: we will talk about problems such as N-queens problem or hamiltonian cycles and coloring problem. In the second chapter we will talk about dynamic programming, theory first then the concrete examples one by one: fibonacci sequence problem and knapsack problem.

Are You an Ecologist or Conservationist Interested in Learning GIS and Machine Learning in R? Then this course is for you! I will take you on an adventure into the amazing of field Machine Learning and GIS for ecological modelling. You will learn how to implement species distribution modelling/map suitable habitats for species in R. My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial data from different sources and producing publications for international peer reviewed journals.

A large number of E-Commerce and tech companies rely on real time training and predictions for their products. Google predicts real time click-through rates for their ads. This is used as an input to their auction mechanism, apart from a bid from the advertiser to decide which ads to show to the user. Stackoverflow uses real time predictions to automatically tag a question with the correct programming language so that they reach the right asker. An election management team might want to predict real time sentiment using Twitter to assess the impact of their campaign.

The field of robotics is growing at an incredible rate, and demand for software engineers with the right skills far exceeds the current supply. This makes this an ideal time to enter this field, and this groundbreaking program represents a unique opportunity to develop these in-demand skills. Expert instructors, personalized project reviews, and exclusive hiring opportunities are hallmarks of this program, and in collaboration with the NVIDIA Deep Learning Institute--one of the most exciting and innovative companies in the world--we have built an unrivalled curriculum that offers the most cutting-edge learning experience currently available. You will graduate from this program having completed several hands-on robotics projects in simulation that will serve as portfolio pieces demonstrating the skills you've acquired. This will enable you to pursue a rewarding career in the robotics field.

The real power and value proposition of Apache Spark is its speed and platform to execute data processing and data science tasks. Let's see how easy it is! 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. Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools that are equally useful for application developers as well as data scientists.

The MOOC Replication Framework (MORF) is a novel software system for feature extraction, model training/testing, and evaluation of predictive dropout models in Massive Open Online Courses (MOOCs). MORF makes large-scale replication of complex machine-learned models tractable and accessible for researchers, and enables public research on privacy-protected data. It does so by focusing on the high-level operations of an extract-train-test-evaluate workflow, and enables researchers to encapsulate their implementations in portable, fully reproducible software containers which are executed on data with a known schema. MORF's workflow allows researchers to use data in analysis without providing them access to the underlying data directly, preserving privacy and data security. During execution, containers are sandboxed for security and data leakage and parallelized for efficiency, allowing researchers to create and test new models rapidly, on large-scale multi-institutional datasets that were previously inaccessible to most researchers. MORF is provided both as a Python API (the MORF Software), for institutions to use on their own MOOC data) or in a platform-as-a-service (PaaS) model with a web API and a high-performance computing environment (the MORF Platform).

The course is broad and pretty decent introductory course, but there is a number of presentation and course design flaws. First, while I'm not sure whether it is solely a Coursera's typical marketing approach to prevent users from refusing the course just because of the minimum amount of time required, or authors' unintended misestimations, but the actual time needed to complete the course is a way more than listed at the course home page, especially assignments. Often the time needed only to run an assignment training with no coding exceeds the given estimate. To get the value from the course one should be prepared to allocate much more time (2x-3x in total). Second, the course is too broad to be called an introductory one but too shallow in terms of math/practical/reasoning details to be named a deep one.

In this course, you'll learn about clustering and dimension reduction, the two fundamental techniques of unsupervised learning and you'll learn to apply them using Python 3 and industry standard, freely available software libraries like scikit-learn and SciPy. You're going to learn to use the fundamental tools of unsupervised learning that professional data scientists use everyday. So who is this course for? Perhaps you're an IT professional, an analyst, a scientist or an academic, and you're looking to make the transition to data science, or you're a student, and you want to learn what data science is all about. In this course I'm going to share with you not only what I learnt but also the joy and the fascination of discovering patterns in data - the wonder of finding hidden structure in datasets that seemed at first too large and too complex.

About this course: This one-week accelerated on-demand course provides participants a a hands-on introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and productionizing ML models. OBJECTIVES This course teaches participants the following skills: Identify use cases for machine learning Build an ML model using TensorFlow Build scalable, deployable ML models using Cloud ML Know the importance of preprocessing and combining features Incorporate advanced ML concepts into their models Productionize trained ML models PREREQUISITES To get the most of out of this course, participants should have: Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python Familiarity with Machine Learning and/or statistics Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google services are currently unavailable in China).