" Data Science is the sexiest job of the 21st century - It has exciting work and incredible pay ". You have been hearing about this a lot. You try to get more information on this and start querying and browsing. You get so many definitions, requirements, predictions, opinions and recommendations. At the end, you are perplexed. And you ask - " What exactly is this field all about? Is it a good career option for me? " **** Please note: This is a career advice course, not a technology course. Data Science has been growing exponentially in the last 5 years. It is also a hybrid field that requires multiple skills and training. We have been training students in Data Science. A number of them committed to training without realizing what it really is. Some were happy, some adjusted their expectations and some regretted committing too soon . We felt that professionals thinking of getting into Data Science needed a primer in what this field is all about. Hence, we came up with this course. Through this course, you will learn about Data Science goals and concepts Process Flow Roles and Responsibilities Where you will fit in to a Data Science team. Building a transition plan Getting into the Data Science field involves significant investment of time. Learn about the field in order to make an informed decision.

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

Get excited! This course is designed to jump-start using Docker Containers for Data Science and Reproducible Research by reproducing several practical examples. Course will help to setup Docker Environment on any machine equipped with Docker Engine (Mac, Windows, Linux). Course will proceed with all steps to create custom and distributed development environment [RStudio] in a container. Forget about manual update of your Development Environment! Work as usual, add or develop the research document into your Container, test it and distribute in an image! Result will be reproducible independently on the R version, perhaps after several years... Same about running R programs in the container. We will demonstrate this capability including testing the container on completely different machines (Mac, Windows, Linux) Summary of ideas we will cover in this course: Reproduce and share work on a different infrastructure Be able to repeat the work after several years Use R-Studio in an isolated environment Tips to personalize work with Docker including usage of Automated Builds What is covered by this course? This course will provide several use cases on using Docker Containers for Data Science: Preparing your computer for using Docker Working pipeline to develop docker image Building Docker image to work with R-Studio in Interactive mode Building Docker images to run R programs Using Docker network to communicate between containers Building ShinyServer in Docker container Walk-though example of developing Shiny App as an R Package and deploying in Docker Container using golem framework More relevant materials may be added to this course in the future (e.g. continous integration and deployment, docker-compose) Why to take this course and not other? Added value of this course is to provide a quick overview of functionality and to provide valuable methods and templates to build on. Focus of this course is to make a learning journey as easy as possible - simply watch these videos and reuse provided code! Just Start using Docker Containers with your Data Science tools by reproducing this course!

Hi There! Welcome to my new course OpenCV Fundamentals using Python. This is the first course from my Computer Vision series. Lets see what are the interesting topics included in this course. At first we will have an overview about computer vision and the amazing OpenCV, the open-source computer vision library. After that, we are ready to proceed with preparing our computer for installing OpenCV and later will proceed with installing OpenCV itself. Then we will try a one liner code to check if everything is working fine. When I said this course is for complete beginners, I really mean it. Because even-if you are coming from a non-python background, the next few sessions and examples will help you get the basic python programming skill to proceed with the rest of the sessions. The topics include Python assignment, flow-control, functions and data structures. Now we are all set to proceed with python computer vision exercises.  But before that we need to learn the theory of how a digital image is organized. Concept of pixels, color and grey scale channels, color codes etc. Then we will write our first opencv program in which we will simply load and display an image from our computer and we will write a grey scale version of this image back to our computer itself. As you already know the basic building block of a digital image is pixels, we will use the power of opencv to manipulate the individual pixels of an image and modify it. Later in the next session, we will use a similar technique to select a collective area of pixels and manipulate it by trying to change color and also get the properties of the image. Hope you know that there are 3 color channels in a color image and a single one in greyscale image. We will try to separate and extract those color channels and later try to merge them back to form the original image. Color spaces, unlike the color channels, is the way how colors are organized in an image. In the next session, we will explore the popular color spaces and will do exercises which switches an image between different color spaces. In the next session, we will use opencv to create and draw simple geometric shapes like line, rectangle, circle, ellipse, polygon etc into an image canvas. We also will try to insert a text into the canvas. Then we will try some morphological transformations to our image which includes erosion which erodes the pixels, then dilation which will expand the pixels, Opening transformation for white noise removal and closing for black point noise removal. Then gradient transformation and finally the top hat and black hat morphological image transformations. After that we will try the geometric transformations which includes scaling or resizing the image, then translating or place shifting the image, flipping or changing sides, rotating the image by fixing an axis, and cropping the image to extract the region of interest. In the coming two sessions, we will try the basic arithmetic and logical operations between two images. We will try to do the addition operation and subtraction operation between two images. We will also try the AND, OR, XOR and NOT binary bitwise operations for two images and will check the results obtained. Later we will go ahead with Image masking, which is a technique of covering the unwanted areas of image and display only the region of interest. And after that we will try Image Smoothing techniques. At first we will use our own filter to do a custom smoothing of image and later built in filters using algorithms like Gaussian Smoothing, average smoothing, Median and finally the bilateral smoothing. Then we will see an advanced technique called thresholding which is very useful in preprocessing and preparing the image for computer vision algorithms. We will do exercises to demonstrate simple thresholding, Otsu thresholding and adaptive thresholding. Then we will check an interesting image color intensity plotting technique called as the histograms. We will plot a histogram and will learn how we can analyse the histogram to predict the nature of image. By using this histogram and adjusting the values based on it, we can enhance the contrast of dull looking images. We will explore the technique called histogram equalization. Image pyramids are different sized images generated and stacked one on top of other. We will explore how we can use opencv methods to generate image pyramids. For us humans, its an easy task to find an object in a scene and find the edges of it. For computers its not that easy. We will explore the opencv functions which enable us to find the edges using the Canny edge detection. As we know to a computer, an image is just a collection of numbers. To find the edges, gradients or the pattern of intensity change of colors should be found out. We will use gradient detection function of OpenCV to do that. Then finally we will draw contours along the different objects in an image with the help of the above mentioned techniques and try to count the number of objects available in the scene. That's all about the basics. The code and the images used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked. So that's all for now, see you soon in the class room. Happy learning and have a great time.

Welcome to the course "Complete Outlier Detection Algorithms A-Z: In Data Science" . This is the most comprehensive, yet straight-forward, course for the outlier detection on UDEMY! Are you Data Scientist or Data Analyst or Financial Analyst or maybe you are interested in anomaly detection or fraud detection? The course is designed to teach you the various techniques which can be used to identify and recognize outliers in any set of data. The process of identifying outliers has many names in Data Science and Machine learning such as outlier modeling, novelty detection, or anomaly detection. Outlier detection algorithms are useful in areas such as Machine Learning, Deep Learning, Data Science, Pattern Recognition, Data Analysis, and Statistics . I will present to you very popular algorithms used in the industry as well as advanced methods developed in recent years, coming from Data Science. You will learn algorithms for detection outliers in Univariate space, in Low-dimensional space and also learn the innovative algorithms for detection outliers in High-dimensional space . I am convinced that only those who are familiar with the details of the methodology and know all the stages of the calculation, can understand it in depth. Anyone who interested in programming, I developed all algorithms in PYTHON, so you can download and run them. List of Algorithms: Interquartile Range Method (IQR), Standard Deviation Method KNN, DBSCAN, Local Outlier Factor, Clustering Based Local Outlier Factor, Isolation Forest, Minimum Covariance Determinant, One-Class SVM, Histogram-Based Outlier Detection, Feature Bagging, Local Correlation Integral Angular Based Outlier Detection Autoencoders Why wait? Start learning today! Because Everyone, who deals with the data, needs to know ‘ Complete Outlier Detection Algorithms A-Z: In Data Science ’, a necessity to recognize fraudulent transactions in the data set. No matter what you need outlier detection for, this course brings you both theoretical and practical knowledge, starting with basic and advancing to more complex algorithms. You can even hone your programming skills because all algorithms you will learn have an implementation in PYTHON. You will learn how to examine data with the goal of detecting anomalies or abnormal instances or outlier data points. For the code explained in the tutorials, you can find a GitHub repository hyperlink. At the end of this course, you will have understood the different aspects that affect how this problem can be formulated, the techniques applicable for each formulation, and knowledge of some real-world applications in which they are most effective.

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.

This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, + other top tech companies. You will go from zero to mastery! Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know). This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want. The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don't worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)! The topics covered in this course are: - Data Exploration and Visualizations - Neural Networks and Deep Learning - Model Evaluation and Analysis - Python 3 - Tensorflow 2.0 - Numpy - Scikit-Learn - Data Science and Machine Learning Projects and Workflows - Data Visualization in Python with MatPlotLib and Seaborn - Transfer Learning - Image recognition and classification - Train/Test and cross validation - Supervised Learning: Classification, Regression and Time Series - Decision Trees and Random Forests - Ensemble Learning - Hyperparameter Tuning - Using Pandas Data Frames to solve complex tasks - Use Pandas to handle CSV Files - Deep Learning / Neural Networks with TensorFlow 2.0 and Keras - Using Kaggle and entering Machine Learning competitions - How to present your findings and impress your boss - How to clean and prepare your data for analysis - K Nearest Neighbours - Support Vector Machines - Regression analysis (Linear Regression/Polynomial Regression) - How Hadoop, Apache Spark, Kafka, and Apache Flink are used - Setting up your environment with Conda, MiniConda, and Jupyter Notebooks - Using GPUs with Google Colab By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more . By the end, you will have a stack of projects you have built that you can show off to others. Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don't really explain things well enough for you to go off on your own and solve real life machine learning problems. Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows. Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career. You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean! Click “Enroll Now” and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course! Taught By: Daniel Bourke: A self-taught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages. My experience in machine learning comes from working at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen. I've worked on machine learning and data problems across a wide range of industries including healthcare, eCommerce, finance, retail and more. Two of my favourite projects include building a machine learning model to extract information from doctors notes for one of Australia's leading medical research facilities, as well as building a natural language model to assess insurance claims for one of Australia's largest insurance groups. Due to the performance of the natural language model (a model which reads insurance claims and decides which party is at fault), the insurance company were able to reduce their daily assessment load by up to 2,500 claims. My long-term goal is to combine my knowledge of machine learning and my background in nutrition to work towards answering the question "what should I eat?". Aside from building machine learning models on my own, I love writing about and making videos on the process. My articles and videos on machine learning on Medium, personal blog and YouTube have collectively received over 5-million views. I love nothing more than a complicated topic explained in an entertaining and educative matter. I know what it's like to try and learn a new topic, online and on your own. So I pour my soul into making sure my creations are accessible as possible. My modus operandi (a fancy term for my way of doing things) is learning to create and creating to learn. If you know the Japanese word for this concept, please let me know. Questions are always welcome. -------- Andrei Neagoie: Andrei is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc... He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life. Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time. Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities. Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way. Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible. See you inside the course!

This is an introductory course in probability and statistics. This course helps to serve as a foundation for higher levels of a statistics course, particularly inferential statistics and research methods course. This course provides 85 video lectures and it also teaches you how to estimate the probability and do statistical analysis using spreadsheets. The course is structured into 10 sections: What is Statistics- Meaning of Statistics in Singular & Plural Sense, Characteristics of Stat, Nature & Scope, Types -Descriptive & Inferential, Distrust and other limitations of Statistics. Descriptive Statistics- Measures of Central Tendency, Measures of Dispersion and Measures of Shape Probability- Introduction to Probability, Fundamental Rules of Counting, Events & and Sample Space, Set & Venn Diagram, Approaches to Probability, Addition Rule, Multiplication Rule, The Law of Total Probability, Bayes' Theorem. Random Variable- Meaning, Discrete Random Variable, Continous Random Variable, Expected Value, Variance, Probability distributions- Binomial, Poisson, Normal Distribution Sampling Distribution- Population & Sample, Parameters & Statistics, Sampling Distribution of Mean, Types of Sampling, Non-Probability Sampling, Theorems of Sampling Distribution Estimation -Estimator & Estimate, Qualities of a good estimator,  Point Estimate, Interval Estimate, the concept of standard error Confidence Interval construction, Sample size determination. Hypothesis Testing- Introduction, Meaning of Null and Alternate Hypothesis, Two-tail & One-tail Tests, Types of Error, Hypothesis Testing Procedure, Hypothesis Test of a Population Mean: Large and Small Sample, Hypothesis Test of  Population Mean: Two Independent Samples, Hypothesis Test of a Population Mean: Paired t-test, Hypothesis Test of Two Population Variance: F-test. ANOVA: One-Way ANOVA, One- Way ANOVA using Excel, Two-Way ANOVA without replication using excel, Two-Way ANOVA with replication using excel, N-Way ANOVA. Correlation Analysis -Intro to Concept, Scatter Plot, Karl Pearson Coefficient of Correlation, Spearman Rank Order Correlation, Probable Error, Hypothesis Testing of Population Coefficient of Correlation. Regression Analysis- Introduction to Regression, Regression Line, Assumptions of the Classical Linear Regression Model, OLS Method, Coefficient of Determination (R Square), Standard Error of OLS estimates, Confidence Interval for alpha and beta, Hypothesis testing, Two-Tail, One -Tail, Regression Analysis Solved Example, Forecasting With Regression Model, Regression Estimation Using Excel. This course will teach you statistics in a real sense and help you to remove your all doubts relating to statistics and probability. If you want really learn probability and statistics in a simple way, you must enrol for this course.