The report lists the leading competitors and also covers the insights strategic industry analysis of the key factors influencing the global Machine Learning …

In this article, I have shared a 3-month plan to learn mathematics for machine learning. As we know, almost all machine learning algorithms make use of concepts of Linear Algebra, Calculus, Probability & Statistics, etc. Some advanced algorithms and techniques also make use of subjects such as Measure Theory(a superset of probability theory), convex and non-convex optimization, and much more. To understand the machine learning algorithms and conduct research in machine learning and its related fields, the knowledge of mathematics becomes a requirement. The plan that I have shared in this article can be used to prepare for data science interviews, to strengthen mathematical concepts, or to start researching in machine learning. The plan will not only help in understanding the intuition behind machine learning but can also be used in many other advanced fields such as statistical signal processing, computational electrodynamics, etc.

George Konidaris still remembers his disheartening introduction to robotics. "When you're a young student and you want to program a robot, the first thing that hits you is this immense disappointment at how much you can't do with that robot," he says. Most new roboticists want to program their robots to solve interesting, complex tasks -- but it turns out that just moving them through space without colliding with objects is more difficult than it sounds. Fortunately, Konidaris is hopeful that future roboticists will have a more exciting start in the field. That's because roughly four years ago, he co-founded Realtime Robotics, a startup that's solving the "motion planning problem" for robots.

Online Courses Udemy - Start Python from the basics and learn how to create 10 amazing and professional Python programs used in the real world! Created by Ardit Sulce, English, Italian [Auto-generated] PREVIEW THIS COURSE - GET COUPON CODE What you'll learn Go from a total beginner to a confident Python programmer Create 10 real-world Python programs (no toy programs) Solidify your skills with bonus practice activities throughout the course Create an app that translates English words Create a web-mapping app on the browser Create a portfolio website and publish it on a real server Create a desktop app for storing data for books Create a webcam video app that detects moving objects Create a web scraper Create a data visualization app Create a database app Create a geocoding web app Create a website blocker Send automated emails Analyze and visualize data Use Python to schedule programs based on computer events. No prior knowledge of Python is required. No previous programming experience needed. Description The Python Mega Course is one of the top online courses to learn Python programming and it has over 130,000 enrolled students.

Sanjeev is a recipient of the 40 Under 40 Data Scientists award, at the Machine Learning Dev Summit for his research in autonomous tech.

Coursera's Machine Learning for Everyone (free access) fulfills two different kinds of unmet learner needs. It's a conceptually-complete, end-to-end course series – its three courses amount to the equivalent of a college or graduate-level course – that covers both the technology side and the business side. While fully accessible and understandable to business-level learners, it's also also vital to data scientists and budding technical practitioners, since it covers:

Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG), where entities are composed of free-form text. However, most existing CKG completion methods focus on the setting where all the entities are presented at training time. Although this setting is standard for conventional KG completion, it has limitations for CKG completion. At test time, entities in CKGs can be unseen because they may have unseen text/names and entities may be disconnected from the training graph, since CKGs are generally very sparse. Here, we propose to study the inductive learning setting for CKG completion where unseen entities may present at test time. We develop a novel learning framework named InductivE. Different from previous approaches, InductiveE ensures the inductive learning capability by directly computing entity embeddings from raw entity attributes/text. InductiveE consists of a free-text encoder, a graph encoder, and a KG completion decoder. Specifically, the free-text encoder first extracts the textual representation of each entity based on the pre-trained language model and word embedding. The graph encoder is a gated relational graph convolutional neural network that learns from a densified graph for more informative entity representation learning. We develop a method that densifies CKGs by adding edges among semantic-related entities and provide more supportive information for unseen entities, leading to better generalization ability of entity embedding for unseen entities. Finally, inductiveE employs Conv-TransE as the CKG completion decoder. Experimental results show that InductiveE significantly outperforms state-of-the-art baselines in both standard and inductive settings on ATOMIC and ConceptNet benchmarks. InductivE performs especially well on inductive scenarios where it achieves above 48% improvement over present methods.

Commonsense question answering (QA) requires background knowledge which is not explicitly stated in a given context. Prior works use commonsense knowledge graphs (KGs) to obtain this knowledge for reasoning. However, relying entirely on these KGs may not suffice, considering their limited coverage and the contextual dependence of their knowledge. In this paper, we augment a general commonsense QA framework with a knowledgeable path generator. By extrapolating over existing paths in a KG with a state-of-the-art language model, our generator learns to connect a pair of entities in text with a dynamic, and potentially novel, multi-hop relational path. Such paths can provide structured evidence for solving commonsense questions without fine-tuning the path generator. Experiments on two datasets show the superiority of our method over previous works which fully rely on knowledge from KGs (with up to 6% improvement in accuracy), across various amounts of training data. Further evaluation suggests that the generated paths are typically interpretable, novel, and relevant to the task.

We propose two algorithms for interpretation and boosting of tree-based ensemble methods. Both algorithms make use of mathematical programming models that are constructed with a set of rules extracted from an ensemble of decision trees. The objective is to obtain the minimum total impurity with the least number of rules that cover all the samples. The first algorithm uses the collection of decision trees obtained from a trained random forest model. Our numerical results show that the proposed rule covering approach selects only a few rules that could be used for interpreting the random forest model. Moreover, the resulting set of rules closely matches the accuracy level of the random forest model. Inspired by the column generation algorithm in linear programming, our second algorithm uses a rule generation scheme for boosting decision trees. We use the dual optimal solutions of the linear programming models as sample weights to obtain only those rules that would improve the accuracy. With a computational study, we observe that our second algorithm performs competitively with the other well-known boosting methods. Our implementations also demonstrate that both algorithms can be trivially coupled with the existing random forest and decision tree packages.

Artificial intelligence is related with human intelligence with similar characteristics such as understanding, reasoning, , learning, problem solving and …