|Course or Certification Name||Category||Location||Mode of learning|
|Python for Computer Vision with OpenCV and Deep Learning||Online self study|
|Artificial Intelligence: Reinforcement Learning in Python||Online self study|
|Complete Machine Learning and Data Science: Zero to Mastery||Online self study|
|Machine Learning A-Z‚Ñ¢: Hands-On Python & R In Data Science||Online self study|
|Machine Learning, Data Science and Deep Learning with Python||Online self study|
|Machine Learning with Python Offered by IBM||Machine Learning||Online self study|
|Machine Learning with Python Training||Machine Learning||Noida , Delhi , Gurgaon , Chandigarh , Bangalore , Hyderabad , Chennai , Ernakulam||Online Classroom|
|The Complete Machine Learning Course with Python||Online self study|
|2020 AWS SageMaker, AI and Machine Learning - With Python||Online self study|
|Python for Data Science and Machine Learning Bootcamp||Online self study|
|NLP - Natural Language Processing with Python||Online self study|
|Complete Python Developer in 2020: Zero to Mastery||Online self study|
|Spark and Python for Big Data with PySpark||Online self study|
|Complete 2020 Data Science & Machine Learning Bootcamp||Online self study|
|Machine Learning A-Z: Hands-On Python & R In Data Science||Machine Learning||Online self study|
Welcome to the ultimate online course on Python for Computer Vision! This course is your best resource for learning how to use the Python programming language for Computer Vision. We'll be exploring how to use Python and the OpenCV (Open Computer Vision) library to analyze images and video data. The most popular platforms in the world are generating never before seen amounts of image and video data. Every 60 seconds users upload more than 300 hours of video to Youtube, Netflix subscribers stream over 80,000 hours of video, and Instagram users like over 2 million photos! Now more than ever its necessary for developers to gain the necessary skills to work with image and video data using computer vision. Computer vision allows us to analyze and leverage image and video data, with applications in a variety of industries, including self-driving cars, social network apps, medical diagnostics, and many more. As the fastest growing language in popularity, Python is well suited to leverage the power of existing computer vision libraries to learn from all this image and video data. In this course we'll teach you everything you need to know to become an expert in computer vision! This $20 billion dollar industry will be one of the most important job markets in the years to come. We'll start the course by learning about numerical processing with the NumPy library and how to open and manipulate images with NumPy. Then will move on to using the OpenCV library to open and work with image basics. Then we'll start to understand how to process images and apply a variety of effects, including color mappings, blending, thresholds, gradients, and more. Then we'll move on to understanding video basics with OpenCV, including working with streaming video from a webcam. Afterwards we'll learn about direct video topics, such as optical flow and object detection. Including face detection and object tracking. Then we'll move on to an entire section of the course devoted to the latest deep learning topics, including image recognition and custom image classifications. We'll even cover the latest deep learning networks, including the YOLO (you only look once) deep learning network. This course covers all this and more, including the following topics: NumPy Images with NumPy Image and Video Basics with NumPy Color Mappings Blending and Pasting Images Image Thresholding Blurring and Smoothing Morphological Operators Gradients Histograms Streaming video with OpenCV Object Detection Template Matching Corner, Edge, and Grid Detection Contour Detection Feature Matching WaterShed Algorithm Face Detection Object Tracking Optical Flow Deep Learning with Keras Keras and Convolutional Networks Customized Deep Learning Networks State of the Art YOLO Networks and much more! Feel free to message me on Udemy if you have any questions about the course! Thanks for checking out the course page, and I hope to see you inside! Jose
When people talk about artificial intelligence , they usually don't mean supervised and unsupervised machine learning . These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning , a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go. We saw AIs playing video games like Doom and Super Mario. Self-driving cars have started driving on real roads with other drivers and even carrying passengers ( Uber ), all without human assistance. If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially. Learning about supervised and unsupervised machine learning is no small feat. To date I have over SIXTEEN (16!) courses just on those topics alone. And yet reinforcement learning opens up a whole new world. As You'll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other. It's led to new and amazing insights both in behavioral psychology and neuroscience. As You'll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It's the closest thing we have so far to a true general artificial intelligence. What's covered in this course? The multi-armed bandit problem and the explore-exploit dilemma Ways to calculate means and moving averages and their relationship to stochastic gradient descent Markov Decision Processes (MDPs) Dynamic Programming Monte Carlo Temporal Difference (TD) Learning (Q-Learning and SARSA) Approximation Methods (i. E. how to plug in a deep neural network or other differentiable model into your RL algorithm) Project: Apply Q-Learning to build a stock trading bot If you're ready to take on a brand new challenge, and learn about AI techniques that you've never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you. See you in class! Suggested Prerequisites: Calculus Probability Object-oriented programming Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations Linear regression Gradient descent TIPS (for getting through the course): Watch it at 2x. Take handwritten notes. This will drastically increase your ability to retain the information. Write down the equations. If you don't, I guarantee it will just look like gibberish. Ask lots of questions on the discussion board. The more the better! Realize that most exercises will take you days or weeks to complete. Write code yourself, don't just sit there and look at my code. WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)
Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 180,000+ developers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. This is a brand new Machine Learning and Data Science course just launched January 2020! Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, + other top tech companies. 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 Nowand 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: Andrei Neagoie 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!
Interested in the field of Machine Learning? Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:Part 1 - Data PreprocessingPart 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest RegressionPart 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest ClassificationPart 4 - Clustering: K-Means, Hierarchical ClusteringPart 5 - Association Rule Learning: Apriori, EclatPart 6 - Reinforcement Learning: Upper Confidence Bound, Thompson SamplingPart 7 - Natural Language Processing: Bag-of-words model and algorithms for NLPPart 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural NetworksPart 9 - Dimensionality Reduction: PCA, LDA, Kernel PCAPart 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.
New! Updated for Winter 2019 with extra content on feature engineering, regularization techniques, and tuning neural networks - as well as Tensorflow 2.0 support! Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 14 hours of video , and most topics include hands-on Python code examples you can use for reference and for practice. I‚ ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn‚. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It‚ s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned! The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras Data Visualization in Python with MatPlotLib and Seaborn Transfer Learning Sentiment analysis Image recognition and classification Regression analysis K-Means Clustering Principal Component Analysis Train/Test and cross validation Bayesian Methods Decision Trees and Random Forests Multiple Regression Multi-Level Models Support Vector Machines Reinforcement Learning Collaborative Filtering K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Term Frequency / Inverse Document Frequency Experimental Design and A/B Tests Feature Engineering Hyperparameter Tuning ... And much more! There's also an entire section on machine learning with Apache Spark , which lets you scale up these techniques to "big data" analyzed on a computing cluster. And you'll also get access to this course's Facebook Group , where you can stay in touch with your classmates. If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs. If you‚ re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry ‚ this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now! "I started doing your course in 2015... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD
This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: | First, you will be learning about the purpose of Machine Learning and where it applies to the real world. | Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.
Machine Learning with Python course discusses concepts of the Python language such as file operations, sequences, object-oriented concepts, etc. along with some of the most commonly leveraged Python libraries like Numpy, Pandas, Matplotlib, etc. The course will then move on to introduce learners to the detailed mechanisms of Machine Learning. Learners will understand in detail the significance of the implementation of Machine Learning in the Python programming language, and leverage this knowledge in their role as data scientists. | After completing the course, one would have learnt about tools to train machines based on real-world situations using Machine Learning algorithms, as well as to create complex algorithms based on concepts related to deep learning and neural networks. During the latter stage of the course, learners will be introduced to real-world use cases of Machine Learning with Python for a holistic learning experience which would prepare them to create applications efficiently.
The Complete Machine Learning Course in Python has been FULLY UPDATED for November 2019 ! With brand new sections as well as updated and improved content , you get everything you need to master Machine Learning in one course! The machine learning field is constantly evolving, and we want to make sure students have the most up-to-date information and practicesavailable to them: Brand new sections include: Foundations of Deep Learning covering topics such as the difference between classical programming and machine learning, differentiate between machine and deep learning, the building blocks of neural networks, descriptions of tensor and tensor operations, categories of machine learning and advanced concepts such as over- and underfitting, regularization, dropout, validation and testing and much more. Computer Vision in the form of Convolutional Neural Networks covering building the layers, understanding filters / kernels, to advanced topics such as transfer learning, and feature extrations. And the following sections have all been improved and added to : All the codes have been updated to work with Python 3.6 and 3.7 The codes have been refactored to work with Google Colab Deep Learning and NLP Binary and multi-class classifications with deep learning Get the most up to date machine learning information possible, and get it in a single course! * * * The average salary of a Machine Learning Engineer in the US is $166,000! By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real life problems in your business, job or personal life with Machine Learning algorithms. Come learn Machine Learning with Python this exciting course with Anthony NG, a Senior Lecturer in Singapore who has followed Rob Percival's project based" teaching style to bring you this hands-on course. With over 18 hours of content and more than fifty 5 star ratings , it's already the longest and best rated Machine Learning course on Udemy! Build Powerful Machine Learning Models to Solve Any Problem You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen. By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more! Inside the course, you'll learn how to: Set up a Python development environment correctly Gain complete machine learning tool sets to tackle most real world problems Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them. Combine multiple models with by bagging, boosting or stacking Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data Develop in Jupyter (IPython) notebook, Spyder and various IDE Communicate visually and effectively with Matplotlib and Seaborn Engineer new features to improve algorithm predictions Make use of t rain/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen data Use SVM for handwriting recognition, and classification problems in general Use decision trees to predict staff attrition Apply the association rule to retail shopping datasets And much much more! No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q&A area. Make This Investment in Yourself If you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you! Take this course and become a machine learning engineer!
Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep UPDATE JAN-2020 Timed Practice Test and additional lectures for Exam Preparation added For Practice Test, look for the section: 2020 Practice Exam - AWS Certified Machine Learning Specialty For exam overview, gap analysis and preparation strategy, look for 2020 - Overview - AWS Machine Learning Specialty Exam UPDATE DEC-2019 Third update for this month!!! AWS Certified Machine Learning Specialty Exam Overview and Preparation Strategies lectures added to the course! Timed Practice Exam is coming soon! Also added, two new lectures that gives an overview of all SageMaker Built-in Algorithms, Frameworks and Bring-Your-Own Algorithm Supports Look for lectures starting with 2020 UPDATE DEC-2019. In the Neural Network and Deep Learning section, we will look at the core concepts behind neural networks, why deep learning is popular these days, different network architectures and hands-on labs to build models using Keras , TensorFlow , Apache MxNet : 2020 Deep Learning and Neural Networks UPDATE DEC-2019. New reference architecture section with hands-on lab that demonstrates how to build a data lake solution using AWS Services and the best practices: 2020 AWS S3 Data Lake Architecture. This topic covers essential services and how they work together for a cohesive solution. Covers critical topics like S3, Athena, Glue, Kinesis, Security, Optimization, Monitoring and more. UPDATE NOV-2019. AWS Artificial Intelligence material is now live! Within a few minutes, you will learn about algorithms for sophisticated facial recognition systems, sentiment analysis, conversational interfaces with speech and text and much more. UPDATE OCT-2019. New XGBoost Lectures, Labs, do-it-yourself exercises, quizzes, Autoscaling, high availability, Monitoring, security, and lots of good stuff UPDATE MAY-2019. 1. Model endpoint integration with hands-on-labs for (Direct Client, Microservice, API Gateway). 2. Hyperparameter Tuning - Learn how to automatically tune hyperparameters UPDATE MARCH-12-2019. I came to know that new accounts are not able to use AWSML Service. AWS is asking new users to use SageMaker Service. I have restructured the course to start with SageMaker Lectures First. Machine Learning Service Lectures are still available in the later parts of the course. Newly updated sections start with 2019 prefix. All source code for SageMaker Course is now available on Github The new house keeping lectures cover all the steps for setting up code from GitHub. SageMaker Lectures - DeepAR - Time Series Forecasting, XGBoost - Gradient Boosted Tree algorithm in-depth with hands-on. XGBoost has won several competitions and is a very popular Regression and Classification Algorithm, Factorization Machine based Recommender Systems and PCA for dimensionality reduction Benefits There are several courses on Machine Learning and AI. What is unique about this course? Here are the top reasons : 1. Cloud-based machine learning keeps you focused on the current best practices. 2. In this course, you will learn the most useful algorithms. Don't waste your time sifting through mountains of techniques that are in the wild 4. Cloud-based service is straightforward to integrate with your application and has support for a wide variety of programming languages. 5. Whether you have small data or big data, the elastic nature of the AWS cloud allows you to handle them all. 6. There is also No upfront cost or commitment Pay only for what you need and use Hands-on Labs In this course, you will learn with hands-on labs and work on exciting and challenging problems What exactly will you learn in this course? Here are the things that you will learn in this course: AWS SageMaker * You will learn how to deploy a Notebook instance on the AWS Cloud. * You will gain insight into algorithms provided by SageMaker service * Learn how to train, optimize and deploy your models AI Services In the AI Services section of this course, * You will learn about a set of pre-trained services that you can directly integrate with your application. * Within a few minutes, you can build image and video analysis applications like face recognition * You can develop solutions for natural language processing, like finding sentiment, text translation, and conversational chatbots. Integration * Learning algorithms is one part of the story - You need to know how to integrate the trained models in your application. * You will learn how to host your models, scale on-demand, handle failures * Provide a clean interface for the applications using Lambda and API Gateway Data Lake * Data management is one of the most complex and time-consuming activities when working on machine learning projects. * With AWS, you have a variety of powerful tools for ingesting, cataloging, transforming, securing, visualization of your data assets. * We will build a data lake solution in this course. Machine Learning Certification * If you are planning to get AWS Machine Learning Specialty Certification, you will find all the resources that you need to pass the exam in this course. * Timed Practice Exam and Quizzes Source Code * The source code for this course available on Git and that ensures you always get the latest code Ideal Student * The ideal student for this course is willing to learn, participate in the course Q&A forum when you need help, and you need to be comfortable coding in Python. Author My name is Chandra Lingam, and I am the instructor for this course. I have over 50,000 thousand students I spend a considerable amount of time keeping myself up-to-date and teach cloud technologies from the basics. I have the following AWS Certifications: Solutions Architect, Developer, SysOps, Solutions Architect Professional, Machine Learning Specialty. I am looking forward to meeting you. Thank you!
Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy! We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:Programming with PythonNumPy with PythonUsing pandas Data Frames to solve complex tasksUse pandas to handle Excel FilesWeb scraping with pythonConnect Python to SQLUse matplotlib and seaborn for data visualizationsUse plotly for interactive visualizationsMachine Learning with SciKit Learn, including:Linear RegressionK Nearest NeighborsK Means ClusteringDecision TreesRandom ForestsNatural Language ProcessingNeural Nets and Deep LearningSupport Vector Machinesand much, much more! Enroll in the course and become a data scientist today!
Welcome to the best Natural Language Processing course on the internet! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language. In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python. We'll start off with the basics, learning how to open and work with text and PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside of text files. Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text. We'll understand fundamental NLP concepts such as stemming, lemmatization, stop words, phrase matching, tokenization and more! Next we will cover Part-of-Speech tagging, where your Python scripts will be able to automatically assign words in text to their appropriate part of speech, such as nouns, verbs and adjectives, an essential part of building intelligent language systems. We'll also learn about named entity recognition, allowing your code to automatically understand concepts like money, time, companies, products, and more simply by supplying the text information. Through state of the art visualization libraries we will be able view these relationships in real time. Then we will move on to understanding machine learning with Scikit-Learn to conduct text classification, such as automatically building machine learning systems that can determine positive versus negative movie reviews, or spam versus legitimate email messages. We will expand this knowledge to more complex unsupervised learning methods for natural language processing, such as topic modelling, where our machine learning models will detect topics and major concepts from raw text files. This course even covers advanced topics, such as sentiment analysis of text with the NLTK library, and creating semantic word vectors with the Word2Vec algorithm. Included in this course is an entire section devoted to state of the art advanced topics, such as using deep learning to build out our own chat bots! Not only do you get fantastic technical content with this course, but you will also get access to both our course related Question and Answer forums, as well as our live student chat channel, so you can team up with other students for projects, or get help on the course content from myself and the course teaching assistants. All of this comes with a 30 day money back garuantee, so you can try the course risk free. What are you waiting for? Become an expert in natural language processing today! I will see you inside the course, Jose
Become a complete Python developer! Join a live online community of over 140,000+ developers and a course taught by an industry expert that has actually worked both in Silicon Valley and Toronto. This is a brand new Python course just launched September 2019! Graduates of Andrei‚ s courses are now working at Google, Amazon, Apple, IBM, JP Morgan, Facebook, + other top tech companies. Learn Python from scratch, get hired, and have fun along the way with the most modern, up-to-date Python course on Udemy (we use the latest version of Python). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Python tutorials anymore. This comprehensive and project based course will introduce you to all of the modern skills of a Python developer (Python 3) and along the way, we will build over 12 real world projects to add to your portfolio (You will get access to all the the code from the 12+ projects we build, so that you can put them on your portfolio right away)! The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Python developer. We will start from the very beginning by teaching you Python basics and programming fundamentals, and then going into advanced topics and different career fields in Python so you can get real life practice and be ready for the real world. The topics covered in this course are: - Programming Fundamentals - Python Basics - Python Fundamentals - Data Structures - Object Oriented Programming with Python - Functional Programming with Python - Lambdas - Decorators - Generators - Testing in Python - Debugging - Error Handling - Regular Expressions - Comprehensions - Modules - Virtual Environments - Developer Environments (PyCharm, Jupyter Notebooks, VS Code, Sublime Text + more) - File Processing: Image, CSV, PDFs, Text + more - Web Development with Python - Machine Learning with Python - Data Science with Python - Automation with Python and Selenium - Scripting with Python - Web Scraping with Python and BeautifulSoup - Image Detection - Data Visualizations - Kaggle, Pandas, NumPy, scikit-learn - Email and SMS with Python - Working with APIs (Twitter Bot, Password Checker, Translator) By the end of this course, you will be a complete Python developer that can get hired at large companies. We are going to use Python to work with Email, Text Messages, CSV files, PDF files, Image Files, Data Visualizations, build our own machine learning model and perform Image detection. We are going to build a web scraper for HackerNews, build a Twitter bot, build the most secure password checker and we will also build some automation tools using Selenium. But the best part? We will build an actual portfolio website using Python that your future employer and customers can contact you at. 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 Python and do just that. They show you how to get started. But the thing is you don‚ know where to go from there or how to build your own projects. Whether you are new to programming, or want to level up your Python skills, or are coming from a different programming language, 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‚ 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 coding experience to someone that can go off, forget about me, and build their own applications and get hired. Taught By: Andrei Neagoie 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!
Learn the latest Big Data Technology - Spark! And learn to use it with one of the most popular programming languages, Python! One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Spark ! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Spark to solve their big data problems!Spark can perform up to 100x faster than Hadoop MapReduce , which has caused an explosion in demand for this skill! Because the Spark 2.0 DataFrame framework is so new, you now have the ability to quickly become one of the most knowledgeable people in the job market! This course will teach the basics with a crash course in Python, continuing on to learning how to use Spark DataFrames with the latest Spark 2.0 syntax! Once we've done that we'll go through how to use the MLlib Machine Library with the DataFrame syntax and Spark. All along the way you'll have exercises and Mock Consulting Projects that put you right into a real world situation where you need to use your new skills to solve a real problem! We also cover the latest Spark Technologies, like Spark SQL, Spark Streaming, and advanced models like Gradient Boosted Trees! After you complete this course you will feel comfortable putting Spark and PySpark on your resume! This course also has a full 30 day money back guarantee and comes with a LinkedIn Certificate of Completion! If you're ready to jump into the world of Python, Spark, and Big Data, this is the course for you!
Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science. At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery . Here's why: The course is a taught by the lead instructor at the App Brewery, London's leading in-person programming bootcamp . In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix. This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build. The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback. To date, we've taught over 200,000 students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup. You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp. We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional. The course includes over 35 hours of HD video tutorials and builds your programming knowledge while solving real-world problems. In the curriculum, we cover a large number of important data science and machine learning topics, such as: Data Cleaning and Pre-Processing Data Exploration and Visualisation Linear Regression Multivariable Regression Optimisation Algorithms and Gradient Descent Naive Bayes Classification Descriptive Statistics and Probability Theory Neural Networks and Deep Learning Model Evaluation and Analysis Serving a Tensorflow Model Throughout the course, we cover all the tools used by data scientists and machine learning experts, including: Python 3 Tensorflow Pandas Numpy Scikit Learn Keras Matplotlib Seaborn SciPy SymPy By the end of this course, you will be fluently programming in Python and be ready to tackle any data science project . We'll be covering all of these Python programming concepts: Data Types and Variables String Manipulation Functions Objects Lists, Tuples and Dictionaries Loops and Iterators Conditionals and Control Flow Generator Functions Context Managers and Name Scoping Error Handling By working through real-world projects you get to understand the entire workflow of a data scientist which is incredibly valuable to a potential employer. Sign up today, and look forward to: 178+ HD Video Lectures 30+ Code Challenges and Exercises Fully Fledged Data Science and Machine Learning Projects Programming Resources and Cheatsheets Our best selling 12 Rules to Learn to Code eBook $12,000+ data science & machine learning bootcamp course materials and curriculum Don't just take my word for it, check out what existing students have to say about my courses: One of the best courses I have taken. Everything is explained well, concepts are not glossed over. There is reinforcement in the challenges that helps solidify understanding. I'm only half way through but I feel like it is some of the best money I've ever spent.-Robert Vance I've spent £27,000 on University..... Save some money and buy any course available by Philipp! Great stuff guys.-Terry Woodward "This course is amazingly immersive and quite all-inclusive from end-to-end to develop an app! Also gives practicality to apply the lesson straight away and full of fun with bunch of sense of humor, so it's not boring to follow throughout the whole course. Keep up the good work guys!" - Marvin Septianus Great going so far. Like the idea of the quizzes to challenge us as we go along. Explanations are clear and easy to follow-Lenox James Very good explained course. The tasks and challenges are fun to do learn an do! Would recommend it a thousand times.-Andres Ariza I enjoy the step by step method they introduce the topics. Anyone with an interest in programming would be able to follow and program-Isaac Barnor I am learning so much with this course; certainly beats reading older Android Ebooks that are so far out of date; Phillippe is so easy any understandable to learn from. Great Course have recommended to a few people.-Dale Barnes This course has been amazing. Thanks for all the info. I'll definitely try to put this in use. :)-Devanshika Ghosh Great Narration and explanations. Very interactive lectures which make me keep looking forward to the next tutorial-Bimal Becks English is not my native language but in this video, Phillip has great pronunciation so I don't have problem even without subtitles :)-Dreamerx85 Clear, precise and easy to follow instructions & explanations!-Andreea Andrei An incredible course in a succinct, well-thought-out, easy to understand package. I wish I had purchased this course first.-Ian REMEMBER¦ I'm so confident that you'll love this course that we're offering a FULL money back guarantee for 30 days! So it's a complete no-brainer, sign up today with ZERO risks and EVERYTHING to gain. So what are you waiting for? Click the buy now button and join the world's best data science and machine learning course.
This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.| Part 1 - Data Preprocessing | Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression | Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification | Part 4 - Clustering: K-Means, Hierarchical Clustering | Part 5 - Association Rule Learning: Apriori, Eclat | Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling | Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP | Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks | Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA |Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost