Each innovation had its followers, which, when applied, transformed different industries. The PC changed the way we manage physical documents, CAD sent the drawing tables to the wineries; email became the default method of formal communication. All of them ended up following globally accepted standards – at least, from the perspective of the provider. The transformations in the previous digital revolution added value to geographic and alphanumeric information, which individually helped boosted modern businesses. These transformations all relied on global connectivity; that is, the “http” protocol that we still use today. The new initiatives took advantage of the information, the inter-connected world and turned them into new businesses like Uber, Airbnb, Udemy and Netflix that have become a p[art of modern culture. But today, we are at the doors of a new digital revolution, which will change all this. Nobody can guarantee the shape of the new digital landscape – industry leaders suggest a mature, pragmatic approach will stand us in good stead.. There will be opportunities for those with vision and scope to take advantage of this revolution. Governments, always mindful of re-election, may also move with an eye on the short-term. But, in the long term, it is, ironically, common users, interested in their own needs who will have the last word. And although the new environment may offer better coexistence, with free code living side by side with private coding according to sustainable standards resulting from a consensus; nobody guarantees that actors such as government and academia will live up to their role in good time. Nobody can predict how it will happen, we only know it will happen. Digital Twin - The new TCP / IP? Since we know it will happen, even if we do not perceive the gradual changes, we need to be prepared for change. We know acting with cuation will be necessary for those who understand the sensitivity of a globally-connected market where added value not only appears in the stock market indicators but also in the response of increasingly influential consumers with regards to the quality of services. Undoubtedly, the standard will play a role in ensuring balance between industry’s supply of creativity and the demands of the end users. This course offers a vision from the author (Golgi Alvarez) perspective, and includes segments of Geospatial World, Siemens, Bentley Systems and Enterprise Management as representative leaders of the Digital Twins approach.

This course is designed to make you proficient in training and evaluating deep learning based object detection models. Specifically, you will learn about Faster R-CNN, SSD and YOLO models. For each of these models, you will first learn about how they function from a high level perspective. This will help you build the intuition about how they work. After this, you will learn how to leverage the power of Tensorflow 2 to train and evaluate these models on your local machine. Finally, you will learn how to leverage the power of cloud computing to improve your training process. For this last part, you will learn how to use Google Cloud AI Platform in order to train and evaluate your models on powerful GPUs offered by google. I designed this course to help you become proficient in training and evaluating object detection models. This is done by helping you in several different ways, including : Building the necessary intuition that will help you answer most questions about object detection using deep learning, which is a very common topic in interviews for positions in the fields of computer vision and deep learning. By teaching you how to create your own models using your own custom dataset. This will enable you to create some powerful AI solutions. By teaching you how to leverage the power of Google Cloud AI Platform in order to push your model's performance by having access to powerful GPUs.

Success of any project depends highly on how well it has been planned. Data science projects are no exception. Large number of data science projects in industrial settings fail to meet the expectations due to lack of proper planning at their inception stage. This course will provide a overview of core planning activities that are critical to the success of any data science project. We will discuss the concepts underlying  - Business Problem Definition; Data Science Problem Definition; Situation Assessment; Scheduling Tasks and Deliveries. The concepts learned will help the students in: A) Framing the business problem B) Getting buy-in from the stakeholders C) Identifying appropriate data science solution that can solve the business problem D) Defining success criteria and metrics to evaluate the key project deliverables  viz;  models, data flow pipeline and documentation. E) Assessing the prevailing situation impacting the project. For e.g. availability of data and resources; risks; estimated costs and perceived benefits. F) Preparing delivery schedules that enable early and continuously incremental valuable actionable insights to the customers G) Understanding the desired team attributes and communication needs

Comprehensive Course Description: Electrification was, without a doubt, the greatest engineering marvel of the 20th century. The electric motor was invented way back in 1821, and the electrical circuit was mathematically analyzed in 1827. But factory electrification, household electrification, and railway electrification all started slowly several decades later. Fast forward to today. It’s the same story with Artificial Intelligence (AI). The field of AI was formally founded in 1956. But it’s only now—more than six decades later—that AI is expected to revolutionize the way humanity will live and work in the coming decades. Data science is a large field of study that covers data systems and processes. These systems and processes are aimed at maintaining data sets as well as getting meaning out of them. Machine Learning (ML), a branch of AI, is the concept that systems can automatically learn and adapt from experience without human intervention. ML, essentially, aims to equip machines with independent learning techniques. Data Science & Machine Learning Full Course in 90 Hours is exhaustive and covers various topics in both these fields in great detail. Data science specialists use a combination of algorithms, applications, principles, and tools to gain a real sense of random data clusters. You are probably aware that organizations worldwide are generating exponential amounts of data. So, monitoring and storing all this data becomes very difficult. This is where data science plays a vital role by focusing on data modeling and data warehousing. Both AI and ML are important to data scientists because they can work equally well in both with ease. The expertise of these skilled professionals allows them to switch roles quickly, too. And in the life cycle of a data science project, this can be a critical factor. What makes this Data Science and Machine Learning course unique? This learning by doing course provides you with not only a solid theoretical foundation but also practical hands-on training in data science and machine learning. At the end of this course, you will be equipped with the knowledge of all the essential concepts you need to excel as a Data Science professional. When you take a quick look at the different sections of this all-inclusive course, you may think of these sections as being independent. But that’s not the case. These sections are interlinked and almost sequential. While it’s true that the course is divided into multiple sections, it’s also true that each section is an independent concept, or you can view it as a course on its own. We have deliberately arranged these sections in a sequence. The reason for this is each subsequent section builds upon the sections you have completed. This framework enables you to explore more independent concepts easily. Data Science & Machine Learning Full Course in 90 HOURS is crafted to teach you the most in-demand skills in the real world. This course aims to help you understand all the data science and machine learning concepts and methodologies with regards to Python. The course is: · Comfortably paced. · Easy to understand. · Descriptive and expressive. · Exhaustive. · Practical with live coding. · Rich with the most advanced and recently discovered models and breakthroughs by the champions in the AI universe. This course is designed for beginners, but we will explore complex concepts gradually. You will find this course interesting, and you will move ahead easily, as it is a compilation of all the basics. You will make quick progress and experience more than what you have learned. At the end of every subsection, you are assigned Home Work/exercises/activities to assess / further strengthen your learning. All this assessment is based on the previous concepts and methods you have learned. Several of these assessment tasks will be coding based, as the main aim is to get you up and proceed to implementations. Data Science is doubtless a rewarding career. You get to resolve some of the most interesting data issues and earn a handsome salary package for your efforts. After you finish Data Science & Machine Learning Full Course in 90 HOURS , you will be able to easily tackle real-world problems and ensure steady career growth. Unlike other courses, this comprehensive course is not expensive. In fact, you can learn all the concepts and methodologies of Data Science and Machine Learning at a fraction of the cost. Our tutorials are divided into 700+ brief HD videos along with detailed code notebooks. Enroll in this course and start your learning journey in Data Science and Machine Learning. This course really simplifies all the complex concepts for you. You will not find an easier course that inspires you as much along your learning journey. Teaching is our passion: We work meticulously to create online tutorials with instructors who are willing to share their expertise and help you in understanding all the concepts. The aim is to create a strong basic understanding for you before you move onward to the advanced version. Detailed course notes, high-quality video content, learning assessment questions, meaningful course material, and subject-related handouts are some of the perks of this course. You are also assured of the support of a dedicated instructor every step of the way. You can approach our team in case of any queries. Course content: 1. Python for Data Science and Data Analysis a. You start with problem-solving and finish with fancy indexing and plots in Matplotlib. b. No prior knowledge in any computer science language is assumed. c. Great fun with Python language. d. Reasonable treatment of data science packages (NumPy, Pandas, Matplotlib, Seaborn, and Sklearn). e. After this course, you will be a competent Python programmer as well as a reasonable expert of data science packages (NumPy, Pandas, Matplotlib). f. This section is designed to teach you programming in general also. Therefore, shifting from this language to any other language after this section is not difficult. 2. Data Understanding and Data Visualization with Python a. This section deals with the in-depth treatment of data science packages both for data manipulation as well as data visualization. b. While Section 1 focuses more on Python language, this section focuses completely on data science packages and their efficient use. c. The packages covered in this section include NumPy, Pandas, Matplotlib, Seaborn, Bokeh, Plotly, and Folium. d. As far as we know, this is the most comprehensive section on data understanding and visualization among the available ones. e. Further, this section is designed to reduce the dependency on core Python language to be treated independently, as well. f. 2D and 3D visualizations, interactive visualizations, and geographic maps are also covered. g. Proceeding in data science with being able to effectively play with the data using famous packages makes progress much worse, and this section addresses this concern. 3. Mastering Probability and Statistics in Python a. Obviously, concepts in data science are not new. In fact, it is also believed that data science is merely a renamed version of Probability and Statistics. Well, without being biased to that extent, we will say that the practical nature of applications was uncovered earlier even though the theory traces back to Probability and Statistics. b. One way or the other, knowing Probability and Statistics makes a significant theoretical as well as practical difference. c. Most of the courses on Probability and Statistics, however, fail to link the data science practices and theory by merely focusing on the axiomatic treatment of the subject. d. We build this section by keeping the practical needs of data science in mind as well as the importance of theory. e. Wherever important, we deliberately explain and show the relationships by derivations and even through Python Code. f. This section builds a very sound basis for understanding the classical concepts in data science as well as its more recent generalizations. g. We start with the very basics of Probability, go through inference and estimations, link famous machine learning techniques with conditional probability, and finally, show that Deep Neural Networks indeed learn a probability function eventually. 4. Machine Learning Crash Course a. Although several concepts, or even all, fall under the umbrella of Probability and Statistics, it turns out that most of the concepts have made their own practical place, mostly derived through engineering, with the name of Machine Learning. For example, the term “overfitting” is now referring to the area of machine learning. b. Machine Learning brings its own set of practices to reach the demands of automation. Hence, mastering these concepts becomes inevitable. c. This section is actually a quick walkthrough of the concepts in Machine Learning and focuses on all the theoretical as well as practical concepts. d. We mostly cover applications using the Sklearn Python package and build machine learning pipelines in this section. e. We also elaborate on more advanced areas of machine learning, which we later present as separate sections. 5. Feature Engineering and Dimensionality Reduction with Python a. Knowing the sections you have covered thus far certainly brings you a huge clarity of the field. But there is still one thing that brings the improvements in the results with a reasonable margin, and that is data preprocessing or data preparation. b. Most of the data science today relies on preparing the data suitable for machine learning models. An effective way of data preparation, most of the time, becomes a game-changer. c. This section focuses on data preparation for machine learning models. d. We build this section to provide an understanding of why selecting features and transforming features are important. e. We also discuss practical issues with real data, like missing values and non-numeric data. f. We discuss the performance improvements both in terms of execution time as well as the accuracy of the models. g. We explain the required mathematical background in a simple way. h. Finally, all the concepts are made more easily understandable by coding relevant examples in Python. 6. Artificial Neural Networks with Python a. With the availability of a huge quantity of data as well as computation power, a relatively old machine learning model, Artificial Neural Network turns out to be the game-changer in data science. b. Artificial Neural Network can approximate almost any pattern in the data. Further, it has a much greater data utilization capacity as compared to the more classical methods. c. With the recent rise of ANNs, a lot of practical techniques are also discovered, particularly for ANNs. d. Also, working with a large amount of data brings its own challenges for learning algorithms. e. In this section, we address all these concerns and cover ANNs in depth. f. We also introduce another framework, “TensorFlow,” for working in ANNs. g. With this section in hand, you can now target much larger machine learning problems. 7. Convolutional Neural Networks with Python a. ANNs, in its most basic form, is not that suitable for image data and for the problems in computer vision. b. Convolutional Neural Networks (CNNs) are considered a game-changer in the field of computer vision. CNNs are not limited to images only. You’ll find them everywhere now, from audio processing to more advanced reinforcement learning (i.e., Resnets in AlphaZero). So, the understanding of CNNs becomes inevitable in all the fields of data science. Even most of the Recurrent Neural Networks (RNNs) rely on CNNs nowadays. c. In this section, you will to learn about: i. The significance of CNNs in data science. ii. The reasons to shift to CNNs from hand engineering (classical computer vision). iii. The major concepts from the absolute beginning with complete unfolding with examples in Python. iv. Practical explanation and live coding with Python. v. Evolution of CNNs — LeNet (1990s) to MobileNets (2020s). vi. Intricate details of CNNs including examples of training CNNs. vii. TensorFlow (Google’s deep learning framework). viii. The use and applications of CNNs (with implementations in framework TensorFlow) that are more recent and advanced in terms of accuracy and efficiency. ix. The use and applications of pre-trained CNNs (with implementations in framework TensorFlow) for transfer learning on your own dataset. x. Building your own applications for Human Face-Verification and Neural Style Transfer. 8. Recurrent Neural Networks with Python a. In this section, you will learn about: i. The significance of Recurrent Neural Networks (RNNs) in data science. ii. The reasons to shift to RNNs from classical sequence models. iii. The important concepts from the absolute beginning with comprehensive unfolding with examples in Python. iv. Practical explanation and live coding with Python. v. Intricate details of RNNs with examples and derivations. vi. The use and applications of RNNs (with implementations in framework TensorFlow) that are more recent and advanced. vii. Building your own applications for automatic text generation as well as for stock price prediction. 9. Reinforcement Learning a. The training data is not always available, and the learner may have to learn through experience. This, in fact, is true in most the cases, particularly when the learner is acting in an uncertain and non-stationary environment. b. The learner needs to act in real-time and, hence, has to decide its action in real- time in response to the environment (a self-driving car, for example). c. Reinforcement Learning (RL) brings its own challenges, and their solutions sometimes are much different than the solutions offered by supervised as well as unsupervised learning. d. Large scale RL problems do require the knowledge of ANNs to model the problem. e. Reinforcement Learning is considered as game-changer in the field of data science, particularly after observing the winnings of CHESS and GO against human champions. However, RL is not restricted to games only. RL is everywhere now, ranging from Recommender Systems to more advanced applications in stock prediction. So, an understanding of RL becomes inevitable in all the fields of data science. f. In this section, you will learn about: i. The importance of Reinforcement Learning (RL) in data science. ii. The key concepts from the absolute beginning with complete unfolding with examples in Python. iii. Practical explanation and live coding with Python. iv. Applications of Probability Theory. v. Markov Decision Processes. After completing this course successfully, you will be able to: · Relate the concepts, principles, and theories in Data Science & Machine Learning . · Understand the methodology of Data Science & Machine Learning using real datasets. Who this course is for: · People who want to become perfect in their data speak. · People who want to learn Data Science & Machine Learning with real datasets in Data Science. · People from a non-engineering background who want to enter the Data Science field. · People who want to enter the Machine Learning field. · Individuals who are passionate about numbers and programming. · People who want to learn Data Science & Machine Learning along with its implementation in realistic projects. · Data Scientists. · Business Analysts.

I am glad you have decided to join the tens of thousands of other students who have enjoyed millions of minutes of instruction videos from this channel. Welcome to the "Python Realtime Population Health Desktop Application" course. The objective of this course is very simple and straightforward. We shall build a modern GUI application for tracking population health from scratch. You shall master all the tools used to complete this objective by the end of the course, additionally you shall have a complete working application which you can share with your friends. This course does not assume any prior knowledge of python or GUI programming, everything shall be done from the very basics. I have no doubt you will love this course. This course also comes with a 30 days money back guarantee so give it a try and let's see how it goes.

Qlik Sense is a great tool for exploring data and empowering analysts to generate reports. It truly puts the user in control with self-serve business intelligence. This video course is your guide to getting the most out of Qlik Sense and will help you solve common problems you might come across while using it. This course contains practical recipes covering the various key tasks you can accomplish with Qlik Sense, from visualizing your data to analyzing it. The course also contains some handy recipes for troubleshooting various possible issues in Qlik SenseBI. We’ll ensure that you have the tricks of the trade to mastering Qlik Sense for effective visual analytics. By the end of the course, you will have upgraded your skills and knowledge to be able to efficiently and become confident in implementing Qlik Sense for your real-world projects. Some prior knowledge of Qlik Sense (and familiarity with Excel and SQL) is assumed for this course. About the Author Abhishek Agarwal has 12+ years' experience in developing analytical solutions. He is a seasoned business intelligence (BI) professional with expertise in multiple technologies. He has been teaching BI technologies for the past 5+ years, working in a similar domain. He uses QlikView, Power BI, Tableau, and a couple of other technologies for end-to-end analytical solution development in his current work.

Extremely Hands-On... Incredibly Practical... Unbelievably Real! This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end. In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities - you name it! This course will give you a full overview of the Data Science journey. Upon completing this course you will know: How to clean and prepare your data for analysis How to perform basic visualisation of your data How to model your data How to curve-fit your data And finally, how to present your findings and wow the audience This course will give you so much practical exercises that real world will seem like a piece of cake when you graduate this class. This course has homework exercises that are so thought provoking and challenging that you will want to cry... But you won't give up! You will crush it. In this course you will develop a good understanding of the following tools: SQL SSIS Tableau Gretl This course has pre-planned pathways. Using these pathways you can navigate the course and combine sections into YOUR OWN journey that will get you the skills that YOU need. Or you can do the whole course and set yourself up for an incredible career in Data Science. The choice is yours. Join the class and start learning today! See you inside, Sincerely, Kirill Eremenko

Learn R Programming by doing! There are lots of R courses and lectures out there. However, R has a very steep learning curve and students often get overwhelmed. This course is different! This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward. After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises. In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course! I can't wait to see you in class, Sincerely, Kirill Eremenko

Complete Data Science Fundamental Course for Beginners First of all this is complete Data Science Fundamental Course. If you looking to begin with Data Science then this the perfect choice ever. HERE IS WHY YOU SHOULD TAKE THE COURSE The course is complete for beginners. That means by completing this course I guarantee you that you will learn all the complex Data Science Components and Machine Learning Algorithms in a easy and Understandable way. In this age of big data, companies across the globe are generating lots and lots of data. This makes Data Science a trending topic. Data Science is one of the most promising technology right now. Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Most of the businesses today are using Data Science to add value to their business operations and increase customer satisfaction and retention. And, so there is substantial increase in the demand for Data Scientists who are skilled in Data Science and related technologies. And, this is the right time to start learning Data Science.

Web Scraping has become one of the hottest topics in the data science world, for getting access to data can make or break you. This is why Fortune 500 companies like Walmart, CNN, Target, and Amazon use web scraping to get ahead and stay ahead with data. It’s the original growth tool and one of their best-kept secrets …And it can easily be yours too. Welcome to Web Scraping in Python with BeautiuflSoup and Selenium! The most up to date and project-oriented course out there currently. In this course, you're going to learn how to scrape data off some of the most well-known websites which include: Twitter Airbnb Nike Google Indeed NFL MarketWatch Worldometers IMDb Carpages At the end of this course, you will understand the most important components of web scraping and be able to build your own web scrapers to obtain new data from any website, automate any task using web scraping, and more. Plus, familiarize yourself with some of the most common scraping techniques and sharpen your Python programming skills while you’re at it! First, learn the essentials of web scraping, explore the framework of a website, and get your local environment ready to take on scraping challenges with BeautifulSoup, and Selenium. Next, cover the basics of BeautifulSoup, utilize the requests library and LXML parser, and scale up to deploy a new scraping algorithm to scrape data from any table online, and from multiple pages. Third, set up Selenium to deal with JavaScript-driven webpages, and use the unique functions of Selenium to interact with pages. Combine the concepts of BeautifulSoup and Selenium to create the most effective scrapers to deal with some of the most challenging websites. Finally, learn how to make web scraping fully automatic by running your scraper at a specific time each day. What makes this course different from the others, and why you should enroll? First, this is the most updated course currently out Second, this is the most project-based course you will find, where we will scrape many of the internets most well-known websites You will have an in-depth step by step guide on how to become a professional web scraper . You will learn how to use Selenium to scrape JavaScript websites and I can assure you, you won't find any tutorials out there that teach you how to really use Selenium like I'll be doing in this course. You will learn how to create a fully automated web scraping script that runs periodically without any intervention from you. 30 days money-back guarantee by Udemy So whether you’re a data scientist, machine learning, or AI engineer who wants to access more data sources; a web developer looking to automate tasks, or a data buff with a general interest in data science and web scraping… This course delivers an in-depth presentation of web scraping basics, methodologies, and approaches that you can easily apply to your own personal projects, or out there in the real world of business. Join me now and let’s start scraping the web together. Enroll today.