These sources of free AI and machine learning training can get you started on a new career path.

Learning a policy capable of moving an agent between any two states in the environment is important for many robotics problems involving navigation and manipulation. Due to the sparsity of rewards in such tasks, applying reinforcement learning in these scenarios can be challenging. Common approaches for tackling this problem include reward engineering with auxiliary rewards, requiring domain-specific knowledge or changing the objective. In this work, we introduce a method based on region-growing that allows learning in an environment with any pair of initial and goal states. Our algorithm first learns how to move between nearby states and then increases the difficulty of the start-goal transitions as the agent's performance improves. This approach creates an efficient curriculum for learning the objective behavior of reaching any goal from any initial state. In addition, we describe a method to adaptively adjust expansion of the growing region that allows automatic adjustment of the key exploration hyperparameter to environments with different requirements. We evaluate our approach on a set of simulated navigation and manipulation tasks, where we demonstrate that our algorithm can efficiently learn a policy in the presence of sparse rewards.

Firstly, Karen Baker and Martin McGovern from Udacity help organize and facilitate this AMA for the life long learners at Udacity. I am deeply thankful to Karen, Martin and Udacity for this opportunity to share the knowledge. QQ: What is the best piece of advice you've ever received in your career? VK: I have got some good advice from books as well as mentors. QQ: What suggestions do you have around building your portfolio?

Time Series Forecasting is a key area that can lead to Return On Investment (ROI) in a business. Think about this: A 10% improvement in forecast accuracy can save an organization millions of dollars.

Record and run settings a team which includes 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with large-scale data processing jobs. Relational Databases are so stuffy and old! Welcome to HBase – a database solution for a new age. HBase: Do you feel like your relational database is not giving you the flexibility you need anymore?

Discussions on artificial intelligence have been around for way too long. And waiting for all that work that brings results it was like waiting for a new Game of Thrones season, facing up to more speculations than actual, official spoilers. Now it's here, AI we mean.

We can see that there is a bleeding of ideas between fields and subfields in statistics. The machine learning practitioner must be aware of both the machine learning and statistical-based approach to the problem. This is especially important given the use of different terminology in both domains. In his course on statistics, Rob Tibshirani, a statistician who also has a foot in machine learning, provides a glossary that maps terms in statistics to terms in machine learning, reproduced below.

To gain competitive advantage from AI, leaders must consider the technology's current state prior to aligning their strategic goals with AI initiatives. Our webinar will help you do that and guide you toward getting the most out of AI's potential. Many business leaders are contemplating whether and/or how to introduce artificial intelligence into their organizations. The challenges of implementing AI are much discussed, but beyond implementation there is the far more important question: How do we generate competitive advantage from AI's application? Using industry examples and findings from the Institute's research, he offers strategies for how to get the most out of AI's potential.

The skill or prediction error of a model must be estimated, and as an estimate, it will contain error. This is made clear by distinguishing between the true error of a model and the estimated or sample error. One is the error rate of the hypothesis over the sample of data that is available. The other is the error rate of the hypothesis over the entire unknown distribution D of examples.

Given how artificial intelligence is a buzzing topic, it has sparked a slew of beginner-friendly introductory resources that clear the general concepts from this very broad topic. And for most newcomers, the most interesting topic in AI is Deep Learning. In fact, Google's Python-based Deep Learning framework Tensorflow has helped many a developer get up to speed with the technical concepts. Besides videos and free online courses, you must also have a reading list that helps you cover the math and statistics behind the algorithms. While YouTube videos remain the main learning source and a key starting point for beginners, there is a slew of resources, especially books that can help cement fundamental concepts.