Instructional Material
[FREE] Theory Of Time Series Analysis/Forecasting
In this course the student will learn the theory of time series analysis and forecasting. Time series analysis is part of artificial intelligence (AI) and is used by many companies to make predictions on sales, temperature, energy consumption, stock prices, etcetera. Time series analysis involves looking at the time series and making judgements based on the look of the time series. The time series may need to be changed in an attempt to analyse it, and these changes could involve resampling or transforming in some fashion. Time series forecasting involves making predictions on the time series.
SAS Programming for Data Science: Statistical Modelling
Do you want to learn how to use SAS programming from the beginners to validating machine learning algorithms assumptions? Are you starting your new SAS journey? Are you looking to know how to well interpret sas output? If you are that person, then you are about to enroll in the best course to guide you! Your Instructor has more than 3 years of SAS experience. Try to search for "SAS Jobs" online.
Multi-Task Learning and HydraNets with PyTorch - PyImageSearch
Today, we will learn about Multi-Task Learning and HydraNets. This is a Deep Learning technique I first introduced back in mid-2020 in an email I sent to exactly 653 people. The responses to this email were so high (engineers from everywhere around the planet told me they loved it and wanted to apply it to their company) I had to create an entire HydraNet section in my course catalog. You can learn more by visiting https://www.thinkautonomous.ai/ Not only is this technique new and exciting for the Deep Learning field, but it's also accessible to many Computer Vision Engineers.
[100%OFF] Decision Trees, Random Forests, Bagging & XGBoost: R Studio
You're looking for a complete Decision tree course that teaches you everything you need to create a Decision tree/ Random Forest/ XGBoost model in R, right? You've found the right Decision Trees and tree based advanced techniques course! How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Decision tree, Random Forest, Bagging, AdaBoost and XGBoost.
[100%OFF] Artificial Neural Networks (ANN) With Keras In Python And R
You're looking for a complete Course on Deep Learning using Keras and Tensorflow that teaches you everything you need to create a Neural Network model in Python and R, right? You've found the right Neural Networks course! How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course. If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.
[100%OFF] Linear Regression And Logistic Regression In Python
You're looking for a complete Linear Regression and Logistic Regression course that teaches you everything you need to create a Linear or Logistic Regression model in Python, right? You've found the right Linear Regression course! A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. What is covered in this course? This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.
Academic Internship at NUS
We achieved an F1 score (harmonic mean of precision and recall) of 0.68, which is quite decent considering the limited size of our dataset. My role was related to hyperparameter optimization for the LRCN model, wherein I experimented with different values of the learning rate, dropout, and regularization techniques and how they impacted the results of our model. One important take from the entire experience was how teamwork is crucial to produce an efficient output. The internship was rigorous, with early morning lectures and late night team meetings, but I learned a lot and had fun in the process!
Top 10 AI Companies in the Education Sector: Influence Of AI in Education - Channel969
The rapid rise of digitalization and technological innovation is transforming almost every industry domain, including healthcare, banking, & finance. Regarding the revolutionary impact of AI integration upon industrial growth, education is no exception. The rapid incorporation of state-of-the-art technologies like artificial intelligence into education is changing how we learn and teach. AI for education is being used by educational platforms worldwide to create highly collaborative and interactive learning environments for students. AI has many key benefits for education, including personalized learning, digital lessons that are highly immersive, 24/7 access, instant feedback, and improved engagement for students with disabilities. Emergen Research estimates that the global market for artificial intelligence in the education sector accounted for USD 1.55 billion in 2020.
Deep Learning and Computer Vision A-Z : OpenCV, SSD & GANs
Free Coupon Discount - Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs, Become a Wizard of all the latest Computer Vision tools that exist out there. Detect anything and create powerful apps. BESTSELLER, 4.4 (3,881 ratings), Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team, English [Auto-generated], Indonesian [Auto-generated], 6 more Preview this Udemy Course - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes
A Tutorial on the Spectral Theory of Markov Chains
Seabrook, Eddie, Wiskott, Laurenz
Markov chains are a class of probabilistic models that have achieved widespread application in the quantitative sciences. This is in part due to their versatility, but is compounded by the ease with which they can be probed analytically. This tutorial provides an in-depth introduction to Markov chains, and explores their connection to graphs and random walks. We utilize tools from linear algebra and graph theory to describe the transition matrices of different types of Markov chains, with a particular focus on exploring properties of the eigenvalues and eigenvectors corresponding to these matrices. The results presented are relevant to a number of methods in machine learning and data mining, which we describe at various stages. Rather than being a novel academic study in its own right, this text presents a collection of known results, together with some new concepts. Moreover, the tutorial focuses on offering intuition to readers rather than formal understanding, and only assumes basic exposure to concepts from linear algebra and probability theory. It is therefore accessible to students and researchers from a wide variety of disciplines.