Tag: machine learning certification

Reading Time: 3 minutes

 

Education has definitely moved away from the teacher facing a classroom of students all using the same textbook. Today the learning experience is internet and ML dependent for data, technology, and digital resources, No wonder the education system is deeply invested in machine learning.

Let us explore how a machine learning course of AI is going to bring its benefits to the education experience of the future. The class sizes keep increasing with compulsory education and teachers are often facing many challenges in giving attention and help to the large numbers of students. A big challenge like this has been simplified by incorporating computer programs with ML algorithms that allow each student to follow his own pace and learning curve.

The newer methods of experiential learning at educational institutions use advanced techniques of AI, machine learning and deep learning in instructing and teaching like chatbots and learning bots. A differentiated machine learning course and AI style of learning deal with the most effective style to help the student learn.

Adaptive based learning curates the learning exercises matching them to the student’s needs and knowledge gaps. Competency-based AI tests aid the students to gauge their learning levels and progress from thereon. Using all these three types of learning, ML and AI can together test how well the students adapt their learning to applications and thus promote the progress of students based on individual interests.

What is machine learning? 

The definition of ML- machine learning is that it gives the AI the ability to self-learn from data, mimicking the human brain and is based on statistical techniques. The algorithm used need not be supervised or explicitly programmed. Almost all ML applications in education work very closely with concepts that are interconnected with artificial learning, deep learning of data, neural networks based on complex self-learning algorithms and the very basic concepts of a horde of machine learning course based applications helping machines do repetitive and intuitive tasks most times more accurately and better than humans themselves.

The benefits of machine learning in education:

Here are some ways in which ML makes a difference in the educational experience of educators and students.

Aid the educators: Data mining is the basis of ML and how well it performs. Forming a single repository of the students in one database, ML can effectively study each student’s behavior versus his peers. Thus ML can help cluster similar students and pace them better throughout the learning experience with the right resources and learning materials.

Gives insight to a student’s performance: One of the huge pluses of ML is the ability to give insights and make predictions based on data of a student’s performance. The ML technology can identify gaps and weaknesses to help students stay ahead of the curve.

Capacity to test students: ML can offer both offline and online tests and guidance that helps students to revise, relearn and evaluate performances. Both educators and students can benefit from their foresight and insights. The AI and ML-based tests and multiple choice answers also test the practical application of knowledge and not just rote learning.

Fair gradation of students: ML removes any bias in grading and scoring. The objective style tests and assignment answers can now be automatically assessed with tools like Grammarly or Turn It In. Both online and offline resources, MOOCs and such can be integrated into the learning process.

Experiential and customized learning: Personalizing the experience and offering near-instantaneous feedback is a huge advantage of ML. Both students and teachers can now benefit from knowing how to fill the knowledge gaps.

Content and feedback are instantaneous: ML is excellent at organizing content, task lists, learning resources, colleges, schools information and much more, to personalize the studying for each student. This helps students grade themselves and progress up the ladder with the suggested courses.

Through identifying weaknesses, machine learning can organize content more effectively. For example, as students learn one skill, they move on to the next skill continually building upon knowledge.

Drop-out rate reduction and retention: Corrective action can be applied rapidly if knowledge gaps persist and are identified by ML. This prevents higher drop-out rates while improving retention levels.

Availability based tutoring: This means ML will facilitate the student’s needs with an available expert tutor for effective learning and tutoring.

Conclusions:

Yes, technology and ML especially will transform the educational experience with more and more algorithms being developed by the minute. If you want to learn all about how to make a career in this field then do a machine learning course at the reputed Imarticus Learning Institute. Now is the right time to jump onto the bandwagon. Why wait?

Reading Time: 4 minutes

 

In machine learning, it is important to understand the difference between data classification and data prediction (or regression) and apply the right concept when a task arises. This is because classification involves a premature decision (as in a combination of prediction and past decisions) and the decision-maker may end up making a decision based on incorrect elements. Not a good situation when error-free action is the main aim of using such a model.

As artificial intelligence and associated technologies evolve, for a program to make the right decision actively depends on how a particular task compares with a set of reference data. This set of data, in most cases, is crucial in the development of that system.

So, let’s take a closer look at these two separate concepts and then go through some of their core differences in the context of decision-making in AI and other related fields.

What is Data Classification?

In simple words, classification is a technique where a system determines what class or category a given observation falls in so that the future course of action can then be defined. Machine learning training uses a three-pronged approach to do this:

• Application of a classification algorithm that identifies shared characteristics of certain classes
• Comparison of this set of characteristics with that of the given observation
• The conclusion to classify the given observation

Let’s take the example of a real-life event to better understand this.

If a bank wants to use its AI tool to predict if a person will default on his loan repayments or not, it will need to first classify the person. For a bank, the two classes in this context will be defaulters and non-defaulters. It does not care about any more details.

The tool will execute the above-mentioned three-step process to conclude if the person will fall in the first or the second category.

While this process may seem unbiased, there is one major drawback that data classification has. This is called the black box problem. It involves a lack of identification of the specific characteristic that must have influenced the decision to classify a given observation under one class. It can be one or more characteristics but pinpointing which ones will be impossible.

What this means is that the bank cannot use it as an example to deter other applications. It has to run the tool every time to assess the person. And for all persons, this defining characteristic may differ.

How Does Data Prediction Compare?

If data classification deals with determination based on characteristics, data prediction focuses on coming up with a more polished output (e.g.: a numeric value). Such type of regression analysis is often used for numerical prediction.

In the above example of the bank loan defaulter, a data prediction model will come up with the probability of how likely a person is to default on loan repayments rather than mere classification.

What is the Difference?

As one can observe, there is a stark difference between data classification and data prediction. Although both of them are widely used in data analysis and artificial intelligence tools, they often serve separate purposes.

According to Frank Harrell, a professor of biostatistics at Vanderbilt University, classification is a forced choice. He takes the example of the decision that a marketer has to take when she has to plan how many of the total target audience should she focus on when building a marketing plan on, say, Facebook. Here, “a lift curve is used, whereby potential customers are sorted in decreasing order of estimated probability of purchasing a product.” To gain maximum ROI, the marketer targets those that are likely to buy the product. Here, classification is not required.

When to make a forced-choice and when not to totally depends on the observation being made. This is why most algorithmic models working on data analysis cannot be used for all types of results. Classification and prediction both depend on what the required output is.

Now, let’s take a look at some pointers that will further clarify the differences between these two models:

• In classification, a data group is divided into categories based on characteristics
  • In prediction, the reference dataset is used to predict a missing element
• Classification can be used when the type of desired output is already known
  • Prediction, on the other hand, is used when the type is unknown
• Classifiers are dependent on previous data sets. They require abundant information to provide better prediction
  • Numerical regression, on the other hand, can provide usable data and act as a starting point for future activities

What these differences highlight is the need to apply them cautiously. Choosing one method over another might feel like a free option, but it is much more than that. While preparing the data will involve challenges in relevance analysis and data cleaning (to chuck out as much noise as possible), one has to also consider factors such as accuracy, speed, robustness, scalability, and interpretability.

As one can assume, these two models have differing values as far as the above factors are considered. The computational cost, for example, is not an important topic at the moment in the artificial intelligence field. But once these models become a part of the everyday analysis, a discussion will surely pop up. And that’s when a more learned decision must be taken.

Finally, it is important to understand that both classification and regression (prediction of a numerical value) are types of predictive analysis. The difference is mainly in how they interact with observation as well as how the reference data set is used.

Choosing which one to go with should perfectly fit the case otherwise one will end up with the wrong choice. Building such predictive models should, therefore, be a joint project involving data scientists and business users. In the example of the bank, if loan agents can directly work with data scientists during the development of these models, it aids in removing at least the known errors out of the equation.