|Course or Certification Name||Category||Location||Mode of learning|
|Post Graduate Program in Data Science and Machine Learning||Data Science||Blended Classroom|
|Machine Learning Engineer||Machine Learning||Online self study|
|Machine Learning Advance certification 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|
|Complete Machine Learning and Data Science: Zero to Mastery||Online self study|
|Deployment of Machine Learning Models||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 A-Z: Hands-On Python & R In Data Science||Machine Learning||Online self study|
|PG Diploma in Machine Learning and AI||Machine Learning||Online self study|
|Python for Data Science and Machine Learning Bootcamp||Online self study|
|Machine Learning In The Cloud With Azure Machine Learning||Machine Learning||Online self study|
|Applied Machine Learning With R||Machine Learning||Online self study|
Designed to give students a comprehensive analytics education with a combination of online and in-person sessions, projects and seminars by industry leaders and hands-on learning in the Jigsaw Lab, PGPDM has already made a significant impact, being ranked #2 in the ‘Top 10 Executive Data Sciences Courses in India’ by Analytics India Magazine for 2017 & 2018. | Comprehensive data science and machine learning education
As more and more companies are looking to build machine learning products, there is a growing demand for engineers who are able to deploy machine learning models to global audiences. In this program, you’ll learn how to create an end-to-end machine learning product. You’ll deploy machine learning models to a production environment, such as a web application, and evaluate and update that model according to performance metrics. This program is designed to give you the advanced skills you need to become a machine learning engineer.
This course is an advanced level training on Machine Learning application and algorithms. It will ensure you get hands on experience in multiple and highly used machine learning skills in both supervised and unsupervised learning. This machine learning training ensures you can apply machine learning algorithms like Regression, clustering, classification and recommendation.The unique case study approach ensures you are working hands on with data while you learn. Finally the course trains you in deep learning and Spark Machine learning, skills which are in great demand today.
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!
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!
Learn how to put your machine learning models into production. What is model deployment? Deployment of machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built. When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate. However, how we are going to actually use those models is often neglected. And yet this is the most important step in the machine learning pipeline. Only when a model is fully integrated with the business systems, we can extract real value from its predictions . Why take this course? This is the first and only online course where you can learn how to deploy machine learning models. In this course, you will learn every aspect of how to put your models in production. The course is comprehensive, and yet easy to follow. Throughout this course you will learn all the steps and infrastructure required to deploy machine learning models professionally. In this course, you will have at your fingertips, the sequence of steps that you need to follow to deploy a machine learning model, plus a project template with full code, that you can adapt to deploy your own models. What is the course structure? Part 1: The Research Environment The course begins from the most common starting point for the majority of data scientists: a Jupyter notebook with a machine learning model trained in it. Part 2: Understanding Machine Learning Systems An overview of key architecture and design considerations for different types of machine learning models. This part sets the theoretical foundation for the practical part of the course. Part 3: From Research to Production Code A hands-on project with complete source code, which takes you through the process of converting your notebooks into production ready code. Part 4: Deployment Tooling Continuing with the hands-on project, this section takes you through the necessary tools for real production deployments, like CI/CD, testing, model cloud storage and more. Part 5: Deployments In this section, you will deploy models to both cloud platforms (Heroku) and cloud infrastructure (AWS). Part 6: Bonus sections In addition, there are dedicated sections which discuss handling big data, deep learning and common issues encountered when deploying models to production. Important: This course will help you take the first steps towards putting your models in production. You will learn how to go from a Jupyter notebook to a fully deployed machine learning model, considering CI/CD, and deploying to cloud platforms and infrastructure. But, there is a lot more to model deployment, like model monitoring, advanced deployment orchestration with Kubernetes, and scheduled workflows with Airflow, as well as various testing paradigms such as shadow deployments that are not covered in this course. Who are the instructors? We have gathered a fantastic team to teach this course. Sole is a leading data scientist in finance and insurance, with 3+ years of experience in building and implementing machine learning models in the field, and multiple IT awards and nominations. Chris is an AI software engineer with enormous experience in building APIs and deploying machine learning models, allowing business to extract full benefit from their implementation and decisions. Who is this course for? This course is suitable for data scientists looking to deploy their first machine learning model, and software developers looking to transition into AI software engineering. Deployment of machine learning models is a very advanced topic in the data science path so the course will also be suitable for intermediate and advanced data scientists. How advanced is this course? This is an intermediate level course, and it requires you to have experience with Python programming and git. How much experience? It depends on how much time you would like to set aside to go ahead and learn those concepts that are new to you. To give you an example, we will work with Python environments, we will work with object oriented programming, we will work with the command line to run our scripts, and we will checkout code at different stages with git. You don‚ need to be an expert in all of these topics, but it will certainly help if you have heard of them, and worked with them before. For those relatively new to software engineering, the course may be challenging. We have added detailed lecture notes and references, so we do believe beginners can take the course, but keep in mind that you will need to put in the hours to read up on unfamiliar concepts. On this point, the course slowly increases in complexity, so you can see how we pass, gradually, from the familiar Jupyter notebook, to the less familiar production code, using a project-based approach which we believe is optimal for learning. It is important that you follow the code, as we build up on it. Still not sure if this is the right course for you? Here are some rough guidelines: Never written a line of code before : This course is unsuitable Never written a line of Python before : This course is unsuitable Never trained a machine learning model before : This course is unsuitable. Ideally, you have already built a few machine learning models, either at work, or for competitions or as a hobby. Have only ever operated in the research environment : This course will be challenging, but if you are ready to read up on some of the concepts we will show you, the course will offer you a great deal of value. Have a little experience writing production code : There may be some unfamiliar tools which we will show you, but generally you should get a lot from the course. Non-technical : You may get a lot from just the theoretical section (section 3) so that you get a feel for the possible architectures and challenges of ML deployments. The rest of the course will be a stretch. To sum up: With more than 50 lectures and 8 hours of video this comprehensive course covers every aspect of model deployment. Throughout the course you will use Python as your main language and other open source technologies that will allow you to host and make calls to your machine learning models. We hope you enjoy it and we look forward to seeing you on board!
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.
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
The field of Data Science is maturing rapidly and demands professionals skilled not only in Statistics, but also in advanced concepts such as Natural Language Processing and Neural Networks. Our vision is to design and deliver a quality online Post Graduate Diploma in Machine Learning/AI to produce top-notch Data Scientists and Machine Learning experts and help India capitalize the next wave of Artificial Intelligence. With upGrad, we promise to equip you with the perfect mix of business acumen and technical capabilities to help you contribute to this technological revolution.
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!
In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset.This course teaches you how to design, deploy, configure and manage your machine learning models with Azure Machine Learning. The course will start with an introduction to the Azure ML toolset and features provided by it and then dive deeper into building some machine learning models based on some real-world problems
Applied Machine Learning with R is a hands-on course Machine Learning and Artificial Intelligence course. This course covers the core concepts of machine learning, along with machine learning algorithms. You will also learn how to implement those machine learning algorithms with R and after completion of the course, you will be able to use them in your own projects.