|Course or Certification Name||Category||Location||Mode of learning|
|Complete 2020 Data Science & Machine Learning Bootcamp||Machine Learning||Online self study|
|Marketing Applications of Machine Learning and Artificial Intelligence||Machine Learning||Classroom|
|Practical Machine Learning with Tensorflow||Machine Learning||Online self study|
|Advanced Machine Learning with Deep Learning||Machine Learning||Noida , Delhi , Gurgaon , Chandigarh , Bangalore , Hyderabad , Chennai , Ernakulam||Online Classroom|
|Deep Learning Specialization||Machine Learning||Online self study|
|Edureka Machine Learning Certification Training using Python||Data Science||Noida , Delhi , Gurgaon , Chandigarh , Bangalore , Hyderabad , Chennai , Ernakulam||Online Classroom|
|Machine Learning A-Z: Hands-On Python & R In Data Science||Machine Learning||Online self study|
|Learn Machine Learning By Building Projects||Machine Learning||Online self study|
|Predictive Analytics using Machine Learning||Machine Learning||Online self study|
|AIWorkflow: Machine Learning, Visual Recognition and NLP||Machine Learning||Online self study|
|Machine Learning for Data Science and Analytics||Machine Learning||Online self study|
|IIM Kashipur Executive Development Program in Machine Learning for Managers using Python & R||Machine Learning||Noida , Delhi , Gurgaon , Chandigarh , Bangalore , Hyderabad , Chennai , Ernakulam||Online Classroom|
|The Complete Machine Learning Course with Python||Machine Learning||Online self study|
|Data Wrangling in Pandas for Machine Learning Engineers||Machine Learning||Online self study|
|Mastering Data Science and Machine Learning Fundamentals||Machine Learning||Online self study|
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 Â£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 REMEMBERÂ¦ 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 management development programme will introduce you to some of the key concepts behind machine learning and artificial intelligence, including the differences between Strong and weak AI. This MDP will explain the different approaches to AI, including machine learning, and the practical applications for new AI-enhanced technologies. With the help of business frameworks, understand how these prediction machines can be built in your business context.
This will be an applied Machine Learning Course jointly offered by Google and IIT Madras. We will cover the basics of Tensorflow and Machine Learning in the initial sessions and advanced topics in the latter part. After this course, the students will be able to build ML models using Tensorflow.
Machines have been driving our existence since the first industrial revolution to the current industry 4.0. It is, thus, imperative to be a part of this revolution by acquainting yourself with the formidable technology platforms like Machine Learning, AI, & Deep Learning | In this age of innovation and disruption, the technology landscape changes rapidly. One has to be on their toes all the time to remain updated and upgraded. In such a scenario, a course that incorporates the concepts of Advanced Machine Learning with Deep Learning in one package can be the best bet to learn and train yourself | Cognixia offers a comprehensive training package based on a case-study approach where participants are exposed to the pragmatic aspects of learning Advanced Machine Learning, AI, & Deep Learning
Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.Participants will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. Participants will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. Participants will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. Participants will master not only the theory, but also see how it is applied in industry.
EdurekaÃ¢Â€Â™s Machine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, NaÃƒÂ¯ve Bayes and Q-Learning. This Machine Learning using Python Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Throughout the Data Science Certification Course, youÃ¢Â€Â™ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR.
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 course focuses on breaking down the important concepts, algorithms, and functions of Machine Learning. The course starts at the very beginning with the building blocks of Machine Learning and then progresses onto more complicated concepts. Each project adds to the complexity of the concepts covered in the project before it.
This course will give you an overview of machine learning-based approaches for predictive modelling, including tree-based techniques, support vector machines, and neural networks using Python. These models form the basis of cutting-edge analytics tools that are used for image classification, text and sentiment analysis, and more.The course contains two case studies: forecasting customer behaviour after a marketing campaign, and flight delay and cancellation predictions.
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones | Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Out-of-the-box Watson models for natural language understanding and visual recognition will be used. There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines.
Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications | This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis.
Data Analytics & Machine learning are growing exponentially and are going to change the decision making processes in companies. Based on the prediction by IBM, there will be around 2.7 million job postings by 2020 in this domain. This course is designed to meet this requirement and has applications for various areas of management including Economics & Finance, Marketing, Operations, Strategy, Leadership, Information System (IT) and many more. Machine Learning is at the intersection of statistics, artificial intelligence and computer science. This course will help professionals develop a fundamental understanding of machine learning and derive practical solutions using predictive analytics. It introduces the concepts related to Supervised and Unsupervised machine learning from basic regression and classification to decision trees and clustering. The course will make use of R/Python for the hands-on implementation of the models.
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!
Data Science and Machine learning is not just another buzzword. So many professionals who work in different areas such as IT, security, marketing, automation, and even medicine, know that machine learning is the key to development. Without it, so many amazing things that make our lives easier â?? such as spam-filtering, Google search, relevant ads, accurate weather forecasting or sport prediction â?? would be impossible. This course is the starting point youâ??ve been waiting for. | This course is designed for students and learners who want to demystify the concepts, statistics, and math behind machine learning algorithms, and who are curious to solve real-world problems using machine learning. The course is structured to start with the basics, and then to gradually develop an understanding of the array of machine learning and data science algorithms. | This ensures that no prior knowledge is required to start learning from this course. The content of this course is specially designed to encompass all the concepts that come under the domain of data science. This course not only guides you through the problems and concepts of machine learning but also elaborates how to successfully implement those concepts.