In reinforcement learning, an artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. This is the most complete Reinforcement Learning course on Udemy. In it you will learn the basics of Reinforcement Learning, one of the three paradigms of modern artificial intelligence. You will implement from scratch adaptive algorithms that solve control tasks based on experience.

TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.

In this project, we'll predict Forest Cover supported various attributes (cartographic variables) of the Forest. Hence, this is often a classification problem. Given is the attribute name, attribute type, the measurement unit, and a brief description. The forest cover type is the classification problem. Welcome to this project on predict Forest Cover in Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free of cost on their server just by registering through email id.

Recurrent neural networks (RNN) are a class of neural networks that is powerful for modeling sequence data such as time series or natural language. GRU layers enable you to quickly build recurrent models without having to make difficult configuration choices. Recurrent Neural Networks (RNNs), a class of neural networks, are essential in processing sequences such as sensor measurements, daily stock prices, etc. In fact, most of the sequence modelling problems on images and videos are still hard to solve without Recurrent Neural Networks. Further, RNNs are also considered to be the general form of deep learning architecture.

Description Deep Learning: Recurrent Neural Networks in Python is a Deep Learning and Artificial Intelligence training course focusing on the development of recursive neural networks (RNNs) published by Yodemi Academy. Among the most important topics covered in this course are GRU architecture, short-term long-term memory architecture (LSTM), time series forecasting, stock price forecasting, natural language processing (NLP) with artificial intelligence, and… Cited. At the beginning of this training course, you will get acquainted with the famous deep learning architectures in a brief and at the same time practical way. Recursive neural networks, or RNNs for short, are one of the most popular classes in the development of artificial intelligence-based systems used in modeling operations sequences. Among the most important applications of the RNN network are time series forecasting of various events, stock price forecasting, natural language processing, and so on.

Mathematics for Machine Learning:Mathematics is essential to understanding the notations of machine learning. It also provides the basics to solve machine learning-related problems. This is a specialization on Coursera to develop mathematical intuition by Imperial College London named Mathematics for Machine Learning. This specialization contains three courses containing Linear Algebra, Calculus, and Principal Component Analysis. Python for everybody: The specialization Python for everybody contains five courses to learn python.

Object tracking is a subarea of Computer Vision which aims to locate an object in successive frames of a video. An example of application is a video surveillance and security system, in which suspicious actions can be detected. Other examples are the monitoring of traffic on highways and also the analysis of the movement of players in a soccer match! In this last example, it is possible to trace the complete route that the player followed during the match. To take you to this area, in this course you will learn the main object tracking algorithms using the Python language and the OpenCV library!

Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid ... Are you aspiring to become a Data Scientist or Machine Learning Engineer? In this course, you will learn about core concepts of Data Science, Exploratory Data Analysis, Statistical Methods, role of Data, Python Language, challenges of Bias, Variance and Overfitting, choosing the right Performance Metrics, Model Evaluation Techniques, Model Optmization using Hyperparameter Tuning and Grid Search Cross Validation techniques, etc. You will learn how to perform detailed Data Analysis using Pythin, Statistical Techniques, Exploratory Data Analysis, using various Predictive Modelling Techniques such as a range of Classification Algorithms, Regression Models and Clustering Models. You will learn the scenarios and use cases of deploying Predictive models. This course covers Python for Data Science and Machine Learning in great detail and is absolutely essential for the beginner in Python.

Active Position Estimation (APE) is the task of localizing one or more targets using one or more sensing platforms. APE is a key task for search and rescue missions, wildlife monitoring, source term estimation, and collaborative mobile robotics. Success in APE depends on the level of cooperation of the sensing platforms, their number, their degrees of freedom and the quality of the information gathered. APE control laws enable active sensing by satisfying either pure-exploitative or pure-explorative criteria. The former minimizes the uncertainty on position estimation; whereas the latter drives the platform closer to its task completion. In this paper, we define the main elements of APE to systematically classify and critically discuss the state of the art in this domain. We also propose a reference framework as a formalism to classify APE-related solutions. Overall, this survey explores the principal challenges and envisages the main research directions in the field of autonomous perception systems for localization tasks. It is also beneficial to promote the development of robust active sensing methods for search and tracking applications.

In few-shot imitation learning (FSIL), using behavioral cloning (BC) to solve unseen tasks with few expert demonstrations becomes a popular research direction. The following capabilities are essential in robotics applications: (1) Behaving in compound tasks that contain multiple stages. (2) Retrieving knowledge from few length-variant and misalignment demonstrations. (3) Learning from a different expert. No previous work can achieve these abilities at the same time. In this work, we conduct FSIL problem under the union of above settings and introduce a novel stage conscious attention network (SCAN) to retrieve knowledge from few demonstrations simultaneously. SCAN uses an attention module to identify each stage in length-variant demonstrations. Moreover, it is designed under demonstration-conditioned policy that learns the relationship between experts and agents. Experiment results show that SCAN can learn from different experts without fine-tuning and outperform baselines in complicated compound tasks with explainable visualization.