Some Student Reviews: "5/5 stars to Mauricio! " (March 2018). "The implementation part is very good and up-too the mark. The explanation step by step process is very good." (February 2018). "course done very well; everything is explained in detail; really satisfied !!!" (February 2018). "Difficult topics are simply illustrated and therefore easy to understand." (January 2018). "So far the course is good, clearly Explanation. " (November 2017). ═════════════════════════════════════════════════════ ***Read the Quick FAQ for the entire course lowdown!*** ══ ═══════════════════════════════════════════════════ NEW : Final Assignment submission lecture! Send in your CNN app and I'll review it! NEW: Trophy Awards for Key Section Achievements! BONUS : Artificial Neural Networks Summary (for your Review and refreshment) ═════════════════════════════════════════════════════ Hi. As always, thanks for showing interest in this course! What makes this course special: Convolutional Neural Networks (CNN): Concepts, Visual Examples and Presentations Step-by-Step CNN Creation and Training Create your CNN application using your own Images Plus, personalized feedback and help. You ask, I answer directly! ✅ First: You'll start with the Neural Networks Review: Quickly learn/refresh all about Neural Networks (NNs): Feed-Forward Passes, Gradient Descent and Backpropagation, Refresh your memory about how NNs learn from data, After this, you will be ready and set to tackle Convolutional Neural Networks. ✅ Second: You'll start your Convolutional Neural Networks endeavor by reviewing their history and motivation: Why are they so good at prediction? What makes them so special? What were the first attempts? ✅ Third: You'll continue your Convolutional Neural Networks endeavor by going into all required concepts: How does Convolutional Neural Networks read images? What's a Convolution layer and how to interpret it? What are the main components of Convolutional Layers? Then, learn how all Neural Network concepts stack into Convolutional Layers, i.e. activations, losses, ✅ Forth : Before jumping into code, you'll see some Convolutional Neural Networks action: You'll see 2 Convolutional Neural Networks LIVE, See how they learn right in front of your eyes, You'll do exactly the same thing in the next sections! So go for it! ✅ Fifth: You'll code your first Convolutional Neural Networks application: Code using the famous MNIST dataset, Easily understand all learnt concepts applied in this section, Tweak parameters according to your criteria and get a feel about how Convolutional Neural Networks learn from images. ✅ Sixth: Now it's time for you to code your own Convolutional Neural Networks app with your own images: We'll use the hydrangea (Kaggle) image dataset competition, Learn how to "take" images from your PC for your Convolutional Neural Networks app, Modify the parameters for the best learning process. ✅ Seventh: Submit your own Convolutional Neural Networks app as the course's Final Assignment: Get comments on how to make it better Learn 100% by applying all concepts in this assignment Optimize for best results Lastly, you can post questions or doubts, and I’ll answer to you personally. I’ll see you inside, -M.A. Mauricio M.

Greetings, I am so excited to learn that you have started your path to becoming a Data Scientist with my course. Data Scientist is in-demand and most satisfying career, where you will solve the most interesting problems and challenges in the world. Not only, you will earn average salary of over $100,000 p.a., you will also see the impact of your work around your, is not is amazing? This is one of the most comprehensive course on any e-learning platform (including Udemy marketplace) which uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making. Data Science Bootcamps are costly, in thousands of dollars. However, this course is only a fraction of the cost of any such Bootcamp and includes HD lectures along with  detailed code notebooks for every lecture. The course also includes practice exercises on real data for each topic you cover, because the goal is "Learn by Doing"! For your satisfaction, I would like to mention few topics that we will be learning in this course: Basis Python programming for Data Science Data Types, Comparisons Operators, if, else, elif statement, Loops, List Comprehension, Functions, Lambda Expression, Map and Filter NumPy Arrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting & boolean masking, Arithmetic Operations & Universal Functions Pandas Pandas Data Structures - Series, DataFrame, Hierarchical Indexing, Handling Missing Data, Data Wrangling - Combining, merging, joining, Groupby, Other Useful Methods and Operations, Pandas Built-in Data Visualization Matplotlib Basic Plotting & Object Oriented Approach Seaborn Distribution & Categorical Plots, Axis Grids, Matrix Plots, Regression Plots, Controlling Figure Aesthetics Plotly and Cufflinks Interactive & Geographical plotting SciKit-Learn (one of the world's best machine learning Python library) including: Liner Regression Over fitting , Under fitting Bias Variance Trade-off, saving and loading your trained Machine Learning Models Logistic Regression Confusion Matrix, True Negatives/Positives, False Negatives/Positives, Accuracy, Misclassification Rate / Error Rate, Specificity, Precision K Nearest Neighbour (KNN) Curse of Dimensionality, Model Performance Decision Trees Tree Depth, Splitting at Nodes, Entropy, Information Gain Random Forests Bootstrap, Bagging (Bootstrap Aggregation) K Mean Clustering Elbow Method Principle Component Analysis (PCA) Support Vector Machine Recommender Systems Natural Language Processing (NLP) Tokenization, Text Normalization, Vectorization, Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Pipeline feature........and MUCH MORE ..........! Not only the hands-on practice using tens of real data project, theory lectures are also provided to make you understand the working principle behind the Machine Learning models. So, what are you waiting for, this is your opportunity to learn the real Data Science with a fraction of the cost of any of your undergraduate course.....! Brief overview of Data around us: According to IBM, we create 2.5 Quintillion bytes of data daily and 90% of the existing data in the world today, has been created in the last two years alone. Social media, transactions records, cell phones, GPS, emails, research, medical records and much more…., the data comes from everywhere which has created a big talent gap and the industry, across the globe, is experiencing shortage of experts who can answer and resolve the challenges associated with the data. Professionals are needed in the field of Data Science who are capable of handling and presenting the insights of the data to facilitate decision making. This is the time to get into this field with the knowledge and in-depth skills of data analysis and presentation. Have Fun and Good Luck!

Hello Everyone, Welcome to Brainy Data Science Course 2020 - Train Your Brain In Data Science. This course covers following topics and perfect for all Beginners in Data Science who are not sure and confused from where to start their journey in Data Science. 1. What is Data Science and It's Different Roles. 2. How to learn Basic Tools in Python, Machine Learning For Data Science. 3. Things to keep and Avoid before Starting Your Journey in Data Science. 4. How to Start Your First Project or Competition on Kaggle. 5. How to use Jupyter Notebook. 6. How to do First Project in Data Science. 7. Hands on Experience in Data Science Live Project , Exercises, and Learn How to Check and Handle missing values in DataSet. 8. Important Terms you should know before Starting your First Practical Project in Data Science. Happy Data Science.

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!

Business Intelligence domain is booming. Many BI tools are available in the market. We have MicroStrategy one of the leading and fastest growing tool. This course is will take you through basics of MicroStrategy Desktop Application right from downloading and installation to creating beautiful dashboards. Also you will learn how to get data from Excel, create various types of visualization available and convey the right message  . Different charts , filters and unique features to any BI tools like Keep Only, View data, Export would be covered. This course will help you with Training Material, MCQ's and  exercise files. So that you follow along with the videos. After completing this training, you would receive a course completion certificate.