Do you want to learn the most important tools of Artificial intelligence and Machine Learning? Then we've got a perfectly designed course for you. Artificial Intelligence has enabled the processing of a large number of data and its use in the domain. There are several tools, frameworks, and libraries available to data scientists and developers, but knowing when and how to use these tools is a must. This course on basic artificial intelligence tools will help you gain this knowledge practically.

Perform statistical analysis on real world datasets Understand feature engineering strategies and tools Perform one hot encoding and normalization Understand the difference between normalization and standardization Deal with missing data using pandas Change pandas DataFrame datatypes Define a function and apply it to a Pandas DataFrame column Perform Pandas operations and filtering Calculate and display correlation matrix heatmap Perform data visualization using Seaborn and Matplotlib libraries Plot single line plot, pie charts and multiple subplots using matplotlib Plot pairplot, countplot, and correlation heatmaps using Seaborn Plot distribution plot (distplot), Histograms and scatterplots Understand machine learning regression fundamentals Learn how to optimize model parameters using least sum of squares Split the data into training and testing using SK Learn Library Perform data visualization and basic exploratory data analysis Build, train and test our first regression model in Scikit-Learn Assess trained machine learning regression model performance Understand the theory and intuition behind boosting Train an XG-boost algorithm in Scikit-Learn to solve regression type problems Train several machine learning models classifier models such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier Assess trained model performance using various KPIs such as accuracy, precision, recall, F1-score, AUC and ROC. Compare the performance of the classification model using various KPIs. Apply autogluon to solve regression and classification type problems Use AutoGluon library to perform prototyping of AI/ML models using few lines of code Plot various models' performance on model leaderboard Optimize regression and classification models hyperparameters using SK-Learn Learn the difference between various hyperparameters optimization strategies such as grid search, randomized search, and Bayesian optimization. Assess trained model performance using various KPIs such as accuracy, precision, recall, F1-score, AUC and ROC. Compare the performance of the classification model using various KPIs.

I will make the argument that the disciplines of math and statistics have captured mainstream interest because of the growing availability of data, and we need math, statistics, and machine learning to make sense of it. Yes, we do have scientific tools, machine learning, and other automations that call to us like sirens. We blindly trust these "black boxes," devices, and softwares; we do not understand them but we use them anyway. While it is easy to believe computers are smarter than we are (and this idea is frequently marketed), the reality cannot be more the opposite. This disconnect can be precarious on so many levels.

In this program, you'll master deep learning fundamentals that will prepare you to launch or advance a career, and additionally pursue further advanced studies in the field of artificial intelligence. You will study cutting-edge topics such as neural, convolutional, recurrent neural, and generative adversarial networks, as well as sentiment analysis model deployment, and you will build projects in NumPy and PyTorch. You will learn from experts in the field, and gain exclusive insights from working professionals. For anyone interested in building expertise with this transformational technology, this Nanodegree program is an ideal point-of-entry. In this program, you'll master deep learning fundamentals that will prepare you to launch or advance a career, and additionally pursue further advanced studies in the field of artificial intelligence.

The artificial neural network is currently the best technique for solving image detection, sound, and natural language processing problems when a big amount of data is available. Image detection is perhaps the most interesting of those specified before, using convolutional neural networks to learn patterns from data. This is a fairly recent technique with applications in many fields, the most famous being automatic cars, and face detection. Artificial neural networks represent a computational system inspired by nature, more precisely by the functioning of biological neurons in the human brain. The fundamental idea behind neural networks is that if they work in nature, they should also work inside a computer.

Do you want to learn Data Science and build robust applications Quickly and Efficiently? Are you an absolute beginner who wants to break into Data Science and look for a course that includes all the basics you need? Are you a busy aspiring entrepreneur who wants to maximize business revenues and reduce costs with Data Science but don't have the time to get there quickly and efficiently? This course is for you if the answer is yes to any of these questions! Data Science is one of the hottest tech fields to be in now!

For decades, science fiction authors, futurists, and movie makers alike have been predicting the amazing (and sometimes catastrophic) changes that will arise with the advent of widespread artificial intelligence. So far, AI hasn't made any such crazy waves, and in many ways has quietly become ubiquitous in numerous aspects of our daily lives. From the intelligent sensors that help us take perfect pictures, to the automatic parking features in cars, to the sometimes frustrating personal assistants in smartphones, artificial intelligence of one kind of another is all around us, all the time. While we've yet to create self-aware robots like those that pepper popular movies like 2001: A Space Odyssey and Star Wars, we have made smart and often significant use of AI technology in a wide range of applications that, while not as mind-blowing as androids, still change our day-to-day lives. One place where artificial intelligence is poised to make big changes (and in some cases already is) is in education.

If anyone has questions about which course may work best for them, please feel free to contact or message me. I will teach you the real-world skills necessary to stand out from the crowd. Hardly it can be 8-10 hours.. Professionally, I am a Data Scientist having experience of 7 years in finance, E-commerce, retail and transport. From my courses you will straight away notice how I combine my own experience to deliver content in a easiest fashion. To sum up, I am absolutely passionate about Data Analytics and I am looking forward to sharing my own knowledge with you!

The workshop will be held in conjunction with SC22: The International Conference for High Performance Computing, Networking, Storage and Analysis located in Dallas, TX on November 13 - 18, 2022. The intent of this workshop is to bring together researchers, practitioners, and scientific communities to discuss methods that utilize extreme scale systems for machine learning. This workshop will focus on the greatest challenges in utilizing HPC for machine learning and methods for exploiting data parallelism, model parallelism, ensembles, and parameter search. We invite researchers and practitioners to participate in this workshop to discuss the challenges in using HPC for machine learning and to share the wide range of applications that would benefit from HPC powered machine learning.

Graph AI carries immense potential for us to explore, connect the dots and build intelligent applications using the Internet of Behaviors (IoB). Many Graph Neural Networks achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their area to the students. The purpose of this course is to unfold the basics to the cutting-edge concepts and technologies in this realm. Graphs are all around us; real-world objects are often defined in terms of their connections to other things.