Welcome to the Complete Computer Vision Bootcamp Course With OpenCv Python 2020. Highest Rated Course This Course is will teach you Computer Vision and Image Processing Techniques From Basic to Advance Level. This Course Provide all high quality content to learn and become Industry level Expert. We worked Really hard to explain the concepts of Computer Vision and Image Processing and the necessary mathematics behind each concept. You will get a Clear Idea about how computer understand and work with images and video Data. We will Start with a Short Python course where you will learn to code in python and will have clear understanding of python syntax and some advance concepts like python generators along with Object Oriented Programming. So Even if Your are a complete Beginner, you are going to learn everything provided in this course. After python Crash Course we will start with numpy and images basics, there we will learn how to read images as numpy array and to manipulate images with numpy. Then we will move on to Image basics with openCV, there you willl learn how to open, create and to draw on the created blank image. After that you will learn Image Processing Techniques Using OpenCv like: Color Mapping, Image Blending, Image Thresholding, Morphology, Real Time Edge Detection Using Webcam and OpenCv in Python. Then We will Make a Project which is in demand and you can directly put it in your resume. Overall After Completing This Course You will be Expert in Computer Vision and Image Processing.

JOIN OTHER 40,000 SUCCESSFUL STUDENTS WHO HAVE ALREADY ENROLLED & MASTERED PYTHON & PANDAS SKILLS (DATA ANALYSIS LIBRARY) WITH ONE OF MY BEST SELLING, TOP RATED COURSE. Student Testimonial : Great going, ankit is good at explanation of data processing stuff. i bought many of his course related to python and machine learning. - Jay Every concept is clearly explained and the tutor of this course replies to every question asked in Q&A section . - Mukka Akshay It was very good session. The instructor has enough knowledge and able to make me understand clearly. Thank you Ankit! - Bibek Baniya This is an amazing course if you want to understand the extent of the power of Pandas . - Venkat Raj It's one of the best course !!! Most of the topics has been covered and explained up to the expectation - Ankur SIngh it is a good match with what i was looking for, the instructor is quite knowledgeable . - Shivi Dhir This class is not too fast or too slow, the way he teaches is perfect. - Frankie Y It is excellent -  Rakhshee Misbah good experience - Weiting ----------------------------------------------------------------------------------------------------------- Update : New section on Data visualization library Matplotlib and Seaborn added. Update : New section on Numpy Library get added. ----------------------------------------------------------------------------------------------------------- If you want to master most in-demand data analysis library pandas, carry on reading. Hi, I am Ankit, one of the Best Selling author on Udemy, taught various courses on Data Science, Python, Pandas, PySpark, Model Deployment. By the end of this course, you will able to apply all majority of Data analysis function on various different datasets with built in function available in pandas. Analysis techniques like exploratory data analysis, data transformation, data wrangling, time series data analysis, analysis through visualization and many more. Carry on reading to know more about course. The era of Microsoft Excel is going to be over, so would you like to learn the next generation one of the most powerful data processing tool and in deman d skill required for data analyst, data scientist and data engineer. Then this course is for you, welcome to the course on data analysis with python's most powerful data processing library Pandas . Why this course? Data scientist is one of the hottest skill of 21st century and many organisation are switching their project from Excel to Pandas the advanced Data analysis tool . This course is basically design to get you started with Pandas library  at beginner level ,  covering majority of important concepts of data processing data analysis and a Pandas library and make you feel confident about data processing task with Pandas at advanced level . What is this course? This course covers Basics of Pandas library Python crash course for any of you want refresh basic concept of python Python anaconda and Pandas installation Detail understanding about two important data structure available in a Pandas library Series data type Data frame data type How you can group the data for better analysis How to use Pandas for text processing How to visualize the data with Pandas inbuilt visualization tool Multilevel index in Pandas. Time series analysis Numerical Python : NumPy Library Matplotlib and Seaborn for Data visualization Machine Learning Theoretical background Complete end to end Machine Learning Model implementation with Scikit-learn API (from Importing Data to Splitting data, Fitting data and Evaluating Data) & How to Improve Machine Learning Model Importing Data from various different kind of file You will get following after enrolling in this course. 150+ HD quality video lecture 16+ hours of content Discussion forum to resolve your query. quizzes to to test your understanding This course is still in a draft mode. I am still adding more and more content, quiz, projects related to data processing with different functionalities of Pandas. So stay tuned and enroll now. Regards Ankit Mistry

In recent years, we've seen a resurgence in AI , or artificial intelligence , and machine learning . Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error. Google famously announced that they are now "machine learning first", meaning that machine learning is going to get a lot more attention now, and this is what's going to drive innovation in the coming years. It's embedded into all sorts of different products. Machine learning is used in many industries, like finance, online advertising, medicine, and robotics. It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good. Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world? In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It’s extremely simple and intuitive, and it’s a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we’ll look at some ways in which KNN can fail. It’s important to know both the advantages and disadvantages of each algorithm we look at. Next we’ll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability. We’ll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations. Next we’ll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we’ll study, and most courses you’ll look at won’t implement them. We will, since I believe implementation is good practice. The last algorithm we’ll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning , so they are important to study in the context of machine learning. One we’ve studied these algorithms, we’ll move to more practical machine learning topics. Hyperparameters , cross-validation , feature extraction , feature selection , and multiclass classification. We’ll do a comparison with deep learning so you understand the pros and cons of each approach. We’ll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work. We’ll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from. All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac . This course focuses on " how to build and understand ", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation . It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: calculus (for some parts) probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule) Python coding: if/else, loops, lists, dicts, sets Numpy, Scipy, Matplotlib WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

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.

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.

Do You Want to Gain an Edge by Gleaning Novel Insights from Social Media? Do You Want to Harness the Power of Unstructured Text and Social Media to Predict Trends? Over the past decade there has been an explosion in social media sites and now sites like Facebook and Twitter are used for everything from sharing information to distributing news. Social media both captures and sets trends. Mining unstructured text data and social media is the latest frontier of machine learning and data science. LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Unlike other courses out there, which focus on theory and outdated methods, this course will teach you practical techniques to harness the power of both text data and social media to build powerful predictive models . We will cover web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. Additionally you will learn to apply both exploratory data analysis and machine learning techniques to gain actionable insights from text and social media data . TAKE YOUR DATA SCIENCE CAREER TO THE NEXT LEVEL BECOME AN EXPERT IN TEXT  MINING & NATURAL LANGUAGE PROCESSING : My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common social media mining and natural language processing packages to extract insights from text data.   I will even introduce you to some very important practical case studies - such as identifying important words in a text and predicting movie sentiments based on textual  reviews. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful  course, you’ll know it all:  extracting text data from websites, extracting data from social media sites and carrying out analysis of these using visualization, stats, machine learning, and deep learning! Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects. HERE IS WHAT YOU WILL GET: Data Structures and Reading in R, including CSV, Excel, JSON, HTML data. Web-Scraping using R Extracting text data from Twitter and Facebook using APIs Extract and clean data from the FourSquare app Exploratory data analysis of textual data Common Natural Language Processing techniques such as sentiment analysis and topic modelling Implement machine learning techniques such as clustering, regression and classification on textual data Network analysis Plus you will apply your newly gained skills and complete a practical text analysis assignment We will spend some time dealing with some of the theoretical concepts. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE. JOIN THE COURSE NOW!