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From agriculture to health care, manufacturing to retail and banking, artificial intelligence is transforming the economy and giving businesses a competitive edge by helping them improve the products and services they deliver to customers.

And now working professionals can gain their own competitive edge by adding an artificial intelligence (AI) micro-credential through the University of Floridas Office of Professional and Workplace Development. This is the first micro-credential to be offered at UF, joining eight online or hybrid certificate programs.

Micro-credentials have emerged as an ideal way for working professionals to become proficient in a specific area through short, non-credit courses that culminate in a competency-based badge.

Earning a micro-credential helps fill knowledge gaps, especially for those in the workforce who have limited time and cannot commit to a semester or longer of learning.

The AI micro-credential program allows participants to learn skills they can leverage for career advancements, from a new job to a raise, new title or additional responsibilities and status with a current employer.

People with such skills are in high demand. There were nearly 15,000 AI-related job postings in Florida in 2021, according to the AI Index 2022 Annual Report.

UF launched its AI micro-credential program in the fall in partnership with NVIDIA, which provided funding. NVIDIA is a leading manufacturer of high-end graphics processing units and chip systems used in two-thirds of the worlds supercomputers, including UFs HiPerGator.

The AI micro-credential consists of seven non-credit courses offered in various modalities that allow people with any level of machine learning background to participate. The courses are available to everyone, from faculty and staff to the broader community.

Regina Rodriguez, provost fellow for professional education, says the program is a great way for anyone to learn skills in AI that will help them play a role in a world of growing reliance on technology.

The courses that we are launching at UF are for those that may not have any understanding of AI, she said. So really starting with foundation courses and teaching the users of AI versus the developers of AI.

To earn the micro-credential, participants complete a 15-hour foundation course that focuses on ethics in AI and a second, 15-hour fundamentals course with a focus on either engineering or STEM.

After the foundation courses are completed, participants choose a final course, designed to demonstrate how AI is used in various fields to solve real world problems, from one of four focus areas.

The current focus areas are agriculture and life sciences, applied data science, business and engineering. UF is adding specialization courses from the College of Medicine in mid-August and the College of Law in the fall. The program is also collaborating with UF Research Computing to offer short courses and access to the HiPer Gator supercomputer.

Upcoming 15-hour course offerings include Ethics of AI on Aug. 1, AI in Business on Sept. 3, AI in Agriculture and Life Sciences of Oct. 3 and AI in Applied Data Science on Nov. 1.

For people who are interested in learning about AI but do not want to commit to earning a micro-credential, there are one and four-hour asynchronous courses available apart from the 15-hour hybrid courses needed to earn a micro-credential. The one-hour course is free and provides users with basic knowledge about AI and the four-hour course costs $249 and earns participants a certificate of completion. However, the shorter courses do not provide as much faculty interaction and discussion as the $1,095 for each micro-credential course.

The courses are priced comparable to other universities professional development courses. UF faculty and staff can take the courses for free to better perfect their craft.

The micro-credential program is also beneficial for companies and executives looking to expand their knowledge. CEOs and other C-Suite business leaders working in and outside of AI have taken courses offered by the program.

Pete Martinez, the CEO of Sivotec Bioinformatics and a former IBM vice president, participated in the Ethics of AI course, which he described as intellectually stimulating.

The University of Florida Ethics of AI program provides a proactive approach for executives to engage in deep-thought through a multi-disciplinary forum on the ethical impacts of AI innovations, he said in a message to Rodriguez. What I found of great value was the involvement from industry in its development. If treated as a pure academic program, it would lose the real-life implications of policies and regulations.

The program has recently partnered with FlapMax, a conference and training program for AI startups, to provide over 80 worldwide companies in its network with webinars and info sessions on AI in agriculture. The program has also partnered with the technology company L3Harris to develop and deliver short courses for industry professionals learning about deep neural network-based solutions.

Rodriguez says the program is just getting started with artificial intelligence offerings.

You can start to become an expert in AI today, she said. It doesnt matter what stage of educational background youre in.

Emma Richards July 15, 2022

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Editorial Note: We earn a commission from partner links on Forbes Advisor. Commissions do not affect our editors' opinions or evaluations.

If you work in tech or are hoping to break into the field, you must keep your technical skills sharp to be competitive in the job market. But not everyone has the time or money to return to school for a degree. Fortunately, online learning platforms for coding are becoming more popular.

DataCamp is one such platform that helps you enhance your coding skills or deepen your knowledge of subjects like data science and machine learning. In this article, youll learn about DataCamp and how it differs from its competitors.

DataCamp is an online learning platform that teaches students new technical skills or helps them brush up on their current skill set. DataCamp is a self-paced, non-proctored approach to learning, similar to competing providers like Codecademy and CodeCamp. DataCamp teaches data science, machine learning and skills like business intelligence and SQL tools.

When you sign up with DataCamp, youll experience a hands-on approach to learning that includes regular skill assessments to track your progress. Courses include challenges and projects featuring real-world elements to help you figure out how to apply your new skills in the workplace.

Through a series of courses or career paths, DataCamp can help you learn coding languages like Python, R, SQL and Scala, along with products like Tableau, Power BI, Oracle SQL and Excel.

DataCamp has a few different paid tiers and one free offering. The free service level is relatively limited but allows you to complete six courses and provides unlimited access to DataCamps job board. Youll also get to create and maintain a professional profile on DataCamps site.

Paid membership levels are as follows:

DataCamp offers a full suite of courses and career paths to explore. Below, weve included details on several of the more popular courses offered.

Time to Completion: 4 hours

Course Format: Self-paced

Can Courses Be Completed Fully Online? Yes

Careers this Course Prepares Learners for: Data scientist

Overview of What to Expect in this Course: This course introduces students to the Python programming language and discusses how the language is used in the field of data science. Students learn to work with data in lists and how to use functions and packages. The course culminates with exposure to NumPy, which is Pythons package used for scientific and numerical computing.

Time to Completion: 4 hours

Course Format: Self-paced

Can Courses Be Completed Fully Online? Yes

Careers this Course Prepares Learners for: Data scientist

Overview of What to Expect in this Course: One of the main responsibilities of a data scientist is to convert raw data into meaningful information. This SQL course teaches data extraction and manipulation using SQL, MySQL, Oracle and PostgreSQL. The course breaks down into four chapters.

Time to Completion: 4 hours

Course Format: Self-paced

Can Courses Be Completed Fully Online? Yes

Careers this Course Prepares Learners for: Data analyst

Overview of What to Expect in this Course: This course introduces students to the open-source language R. Students learn about key concepts like vectors, factors, lists and data frames. The R course aims to help students develop the skills theyll need to do their own data analysis in R.

Time to Completion: 3 hours

Course Format: Self-paced

Can Courses Be Completed Fully Online? Yes

Careers this Course Prepares Learners for: Data Analyst

Overview of What to Expect in this Course: Power BI is a widely used business intelligence platform that allows users to create impactful data models. DataCamps Power BI course teaches students to use the drag-and-drop functionality and other methods to load and transform data using Power Query.

More in-depth and time-intensive than individual courses, DataCamps skill tracks give a more well-rounded look at popular IT areas. These tracks include programming in Python and R and data visualization. Below, we provide some details on DataCamps most popular skill tracks.

Time to Completion: 22 hours

Course Format: Self-paced

Can Courses Be Completed Fully Online? Yes

Careers this Course Prepares Learners for: Data scientist, data analyst

Overview of What to Expect in this Course: This track offers an in-depth look at programming in R and other coding languages used by data scientists. Students undergo a series of exercises to learn about common R elements, including vectors, matrices and data frames. More advanced courses in this skill track introduce concepts like conditional statements, loops and vectorized functions.

Time to Completion: 88 hours

Course Format: Self-paced

Can Courses Be Completed Fully Online? Yes

Careers this Course Prepares Learners for: Researcher, data scientist

Overview of What to Expect in this Course: This course teaches students how to use Python like a data scientist. Students learn to work with data: importing, cleaning, manipulating andmost importantlyvisualizing. A series of interactive exercises introduces learners to some of the most popular Python libraries, like pandas, NumPy and Matplotlib.

Time to Completion: 73 hours

Course Format: Self-paced

Can Courses Be Completed Fully Online? Yes

Careers this Course Prepares Learners for: Data engineer

Overview of What to Expect in this Course: In addition to Python, this in-depth skill track comprises 19 courses that introduce students to languages like Shell, SQL and Scala. Learners also gain exposure to big data tools like AWS Boto, PySpark, Spark SQL and MongoDB. Through six self-paced projects, students create their own databases and data engineering pipelines.

Time to Completion: 61 hours

Course Format: Self-paced

Can Courses Be Completed Fully Online? Yes

Careers this Course Prepares Learners for: Machine learning scientist, machine learning engineer

Overview of What to Expect in this Course: This skill track focuses on models. Comprising 15 courses, this course teaches students about creating, training and visualizing models, one of the most important tools for a machine learning engineer. Students are also introduced to Bayesian statistics, natural language processing and Spark.

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Yes, reviews indicate that DataCamp is suitable for beginners. Even the providers more in-depth course offerings are very basic in nature, presenting material in simple, easy-to-understand formats.

When it comes to employers, a certificate from DataCamp does not carry the same weight as a degree. Still, a DataCamp certificate indicates that you have invested time and energy into learning a career-related skill set.

Possibly, though a DataCamp course on its own may not be enough for you to land a job. You should also gain hands-on experience and network with professionals in your desired field.

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Dropout is an important regularization technique used with neural networks. Despite effective results in general neural network architectures, this regularization has some limitations with the convolutional neural networks. Due to this reason, it does not solve the purpose of building robust deep learning models. DropBlock is a regularization technique, which was proposed by the researchers at Google Brain, addresses the limitations of the general dropout scheme and helps in building effective deep learning models. This article will cover the DropBlock regularization methodology, which outperforms existing regularization methods significantly. Following are the topics to be covered.

By preserving the same amount of features, the regularization procedure minimizes the magnitude of the features. Lets start with the Dropout method of regularization to understand DropBlock.

Deep neural networks include several non-linear hidden layers, making them highly expressive models capable of learning extremely complex correlations between their inputs and outputs. However, with minimal training data, many of these complex associations will be the consequence of sampling noise, thus they will exist in the training set but not in the true test data, even if they are derived from the same distribution. This leads to overfitting, and several ways for decreasing it have been devised. These include halting training as soon as performance on a validation set begins to deteriorate.

There are two best ways to regularize a fixed-sized model.

Dropout is a regularization strategy that solves two difficulties. It eliminates overfitting and allows for the efficient approximation combination of exponentially many distinct neural network topologies. The word dropout refers to the removal of units (both hidden and visible) from a neural network. Dropping a unit out means removing it from the network momentarily, including with all of its incoming and outgoing connections. The units to be dropped are chosen at random.

A thinned network is sampled from a neural network by applying dropout. All the units that avoided dropout make up the thinning network. A collection of potential 2 to the power of nets thinning neural networks may be considered a neural network with a certain number of units. Each of these networks shares weights in order to keep the total number of parameters at the previous level or lower. A new thinning network is sampled and trained each time a training instance is presented. Therefore, training a neural network with dropout may be compared to training a group of 2 to the power of nets thinned networks with large weight sharing, where each thinned network is trained extremely infrequently or never.

Are you looking for a complete repository of Python libraries used in data science,check out here.

A method for enhancing neural networks is a dropout, which lowers overfitting. Standard backpropagation learning creates brittle co-adaptations that are effective for the training data but ineffective for data that has not yet been observed. These co-adaptations are disrupted by random dropout because it taints the reliability of any one concealed units existence. However, removing random characteristics is a dangerous task since it might remove anything crucial to solving the problem.

To deal with this problem DropBlock method was introduced to combat the major drawback of Dropout being dropping features randomly which proves to be an effective strategy for fully connected networks but less fruitful when it comes to convolutional layers wherein features are spatially correlated.

In a structured dropout method called DropBlock, units in a feature maps contiguous area are dropped collectively. Because activation units in convolutional layers are spatially linked, DropBlock performs better than dropout in convolutional layers. Block size and rate () are the two primary parameters for DropBlock.

Similar to dropout, the DropBlock is not applied during inference. This may be understood as assessing an averaged forecast over the ensemble of exponentially growing sub-networks. These sub-networks consist of a unique subset of sub-networks covered by dropout in which each network does not observe continuous feature map regions.

There are two main hyperparameters on which the whole algorithm works which are block size and the rate of unit drop.

The feature map will have more features to drop as every zero entry on the sample mask is increased to block size, the block size is sized 0 blocks, and so will the percentage of weights to be learned during training iteration, thus lowering overfitting. Because more semantic information is removed when a model is trained with bigger block size, the regularization is stronger.

According to the researchers, regardless of the feature maps resolution, the block size is fixed for all feature maps. When block size is 1, DropBlock resembles Dropout, and when block size encompasses the whole feature map, it resembles SpatialDropout.

The amount of characteristics that will be dropped depends on the rate parameter (). In dropout, the binary mask will be sampled using the Bernoulli distribution with a mean of 1-keep_prob, assuming that we wish to keep every activation unit with the probability of keep_prob.

We must, however, alter the rate parameter () when we sample the initial binary mask to take into account the fact that every zero entry in the mask will be extended by block size2 and the blocks will be entirely included in the feature map. DropBlocks key subtlety is that some dropped blocks will overlap, hence the mathematical equation can only be approximated.

Lets understand with an example shown in the below image, it represents the test results by researchers. The researchers applied DropBlock on the ResNet-50 model to check the effect of block size. The models are trained and evaluated with DropBlock in groups 3 and 4. So two ResNet-50 models were trained.

The first model has higher accuracy compared to the second ResNet-50 model.

The syntax provided by Keras to use DropBlock for regularizing the neural networks is shown below.

keras_cv.layers.DropBlock2D(rate, block_size, seed=None, **kwargs)

Hyperparameter:

DropBlocks resilience is demonstrated by the fact that it drops semantic information more effectively than the dropout. Convolutional layers and fully connected layers might both use it. With this article, we have understood about DropBlock and its robustness.

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Addressing the issues of dropout regularization using DropBlock - Analytics India Magazine

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