ml - Magnimind Academy https://magnimindacademy.com Launch a new career with our programs Fri, 27 Oct 2023 13:58:20 +0000 en-US hourly 1 https://wordpress.org/?v=6.7.2 https://magnimindacademy.com/wp-content/uploads/2023/05/Magnimind.png ml - Magnimind Academy https://magnimindacademy.com 32 32 How Do I Use Machine Learning To Gain Profit? https://magnimindacademy.com/blog/how-do-i-use-machine-learning-to-gain-profit/ Mon, 13 Sep 2021 22:39:36 +0000 https://magnimindacademy.com/?p=8464 Undoubtedly, you’ve observed the massive buzz going around machine learning since last few years. While a lot of venture investments are being made, conferences are being organized on how to leverage the power of this technology, small businesses too can get benefitted by using machine learning. In this post, we’re going to explore some of the most common ways through which machine learning helps you gain profit.

The post How Do I Use Machine Learning To Gain Profit? first appeared on Magnimind Academy.

]]> Undoubtedly, you’ve observed the massive buzz going around machine learning since last few years. While a lot of venture investments are being made, conferences are being organized on how to leverage the power of this technology, small businesses too can get benefitted by using machine learning. In this post, we’re going to explore some of the most common ways through which machine learning to gain profit.

 

Targeting the audience

 

These days, one of the biggest problems experienced by businesses is that they fail to capture the attention of common people. The problem lies in the fact that advertisements often don’t connect with the audience. If you too are experiencing this issue, implementation of machine learning can help you sail through. You can use computer speech and vision to obtain valuable insights about your audience and use that information to create more targeted ads that result in more engagements which mean more profit.

 

Personalized customer service

 

Quality of customer service can make or break a business. With the help of machine learning tools and technologies, it’s now possible to combine years of data pertaining to customer services and merge it with NLP technology. The natural language processing algorithms make interactions with customers more personalized by leveraging data. Each and every customer receives the most useful answers to their queries, which greatly increases the quality quotient of customer service. Additionally, the technology reduces the need for heavy investment that results in reduced customer servicing costs.

 

Personalize product recommendations

 

If you’re into e-commerce environment, then you probably know that the customers like to have personalized product recommendations delivered to them. For them, it improves their overall shopping experience and for you, it brings a new opportunity to sell more products. By leveraging the power of predictive analysis and machine learning, you can look beyond what the consumers searching for and try to connect those dots on what they most likely want. Matching customers to specific products or services will increase the chances of more conversions and thus, more profit.

 

Dynamic pricing

 

Change of pricing based on the level of demand or a need can bring a good opportunity to increase your revenue stream. For instance, Uber uses machine learning to create dynamic prices. It uses the technology to optimize the ride-sharing aspect and to minimize wait time. It can temporarily change pricing in an area to obtain a higher revenue stream and can lower rates where the demand is much lower. Machine learning can utilize available data to predict areas where demand may occur, which you can leverage to attract more customers, increasing your bottom line.

 

Final Thoughts

 

These days, businesses are capturing data from a huge number of sources and with the help of machine learning tools and technologies, they’re becoming able to develop a better brand exposure to obtain successful outcomes. Machine learning has already started impacting almost every part of the business domain. So, it’d be wise to integrate this technology with your existing technologies to improve profit.

The post How Do I Use Machine Learning To Gain Profit? first appeared on Magnimind Academy.

]]> What Is Generalization In Machine Learning? https://magnimindacademy.com/blog/what-is-generalization-in-machine-learning/ Mon, 13 Sep 2021 22:01:23 +0000 https://magnimindacademy.com/?p=8444 Before talking about generalization in machine learning, it’s important to first understand what supervised learning is. To answer, supervised learning in the domain of machine learning refers to a way for the model to learn and understand data. With supervised learning, a set of labeled training data is given to a model. Based on this training data, the model learns to make predictions. The more training data is made accessible to the model, the better it becomes at making predictions. When you’re working with training data, you already know the outcome. Thus, the known outcomes and the predictions from the model are compared, and the model’s parameters are altered until the two line up. The aim of the training is to develop the model’s ability to generalize successfully.

The post What Is Generalization In Machine Learning? first appeared on Magnimind Academy.

]]> Before talking about generalization in machine learning, it’s important to first understand what supervised learning is. To answer, supervised learning in the domain of machine learning refers to a way for the model to learn and understand data. With supervised learning, a set of labeled training data is given to a model. Based on this training data, the model learns to make predictions. The more training data is made accessible to the model, the better it becomes at making predictions. When you’re working with training data, you already know the outcome. Thus, the known outcomes and the predictions from the model are compared, and the model’s parameters are altered until the two line up. The aim of the training is to develop the model’s ability to generalize successfully.

 

What is generalization?

 

The term ‘generalization’ refers to the model’s capability to adapt and react properly to previously unseen, new data, which has been drawn from the same distribution as the one used to build the model. In other words, generalization examines how well a model can digest new data and make correct predictions after getting trained on a training set.

How well a model is able to generalize is the key to its success. If you train a model too well on training data, it will be incapable of generalizing. In such cases, it will end up making erroneous predictions when it’s given new data. This would make the model ineffective even though it’s capable of making correct predictions for the training data set. This is known as overfitting. The inverse (underfitting) is also true, which happens when you train a model with inadequate data. In cases of underfitting, your model would fail to make accurate predictions even with the training data. This would make the model just as useless as overfitting.

 

The ideal solution

 

You would ideally want to choose a model that stands at the sweet spot between overfitting and underfitting. To achieve this goal, you can track the performance of a machine learning algorithm over time as it’s working with a set of training data. You can plot both the skill on the training data and the skill on a test dataset that you’ve held back from the training process. As the algorithm learns over time, the level of error for the model on the training data would decrease and so would the error on the test dataset. Training the model for too long would cause a continual decrease in the performance on the training dataset due to overfitting. At the same time, due to the model’s decreasing ability for generalization, the error for the test set would start to increase again. The sweet spot is the point just before the error on the test dataset begins to rise where the model shows good skill on both the training dataset as well as the unseen test dataset.

To limit overfitting in a machine learning algorithm, two additional techniques that you can use are:

  • Using a resampling method to estimate the accuracy of the model
  • Holding back a validation dataset

So, during your machine learning training, keep an eye on generalization when estimating your model accuracy on unseen data.

The post What Is Generalization In Machine Learning? first appeared on Magnimind Academy.

]]> How Has Machine Learning And Ai Changed And Continue To Change The Finance Industry? https://magnimindacademy.com/blog/how-has-machine-learning-and-ai-changed-and-continue-to-change-the-finance-industry/ Fri, 04 Oct 2019 12:47:11 +0000 https://magnimindacademy.com/?p=6313 Artificial intelligence together with its most talked about subcategory machine learning are probably the biggest two factors impacting the entire business world and transforming it. We may not always realize how these technologies are involved in our day-to-day life, but in reality, they’re present in a lot of aspects. In a business context, almost every industry leverages the power of artificial intelligence and machine learning – from traveling industry to transportation industry to the healthcare industry and many more. In this post, we’re going to explore the impacts of these two technologies on the finance industry.

The post How Has Machine Learning And Ai Changed And Continue To Change The Finance Industry? first appeared on Magnimind Academy.

]]> Artificial intelligence together with its most talked about subcategory machine learning are probably the biggest two factors impacting the entire business world and transforming it. We may not always realize how these technologies are involved in our day-to-day life, but in reality, they’re present in a lot of aspects. In a business context, almost every industry leverages the power of artificial intelligence and machine learning – from traveling industry to transportation industry to the healthcare industry and many more. In this post, we’re going to explore the impacts of these two technologies on the finance industry.

Here’re the major ways through which the finance industry is leveraging the power of artificial intelligence and machine learning.

1- Better risk management

Probably the biggest impact of artificial intelligence and machine learning on the finance industry can be found when it comes to risk management. While traditional software applications can predict creditworthiness based on the static information obtained from financial reports and loan applications, implementation of machine learning technologies can help financial institutions to go much further. Algorithms identify the signs of probable future issues and analyze a client’s history of risk cases to help the authorities make an informed decision. They’re also able to identify present market trends together with relevant news items which can affect the ability of a client to pay.

2- Improved data security

Data security has always been at the top of the list of concerns for any financial institution. And if you consider the number of data breaches occurred during recent years, there’re reasons to be concerned. Traditional security tools aren’t capable of identifying modern sophisticated cyberattacks. To mitigate security risks, financial institutions implement advanced technologies like machine learning. Security solutions powered by machine learning are come with unique abilities to secure the financial data. The combined power of big data capabilities and intelligent pattern analysis gives machine learning security technology a robust advantage over traditional tools.

3- Enhanced customer experience

Like all other industries, the financial industry is also focusing on developing the top line by implementing advanced methods to offer custom services and better experience to customers. Many financial institutions have already introduced chatbots powered by artificial intelligence abilities that can analyze the voice of a customer and converse accordingly. With the help of machine learning and big data, these chatbots understand how to respond to the questions of customers’ – from transaction-specific questions to onboarding concerns. Additionally, technologies backed by artificial intelligence and machine learning are capable of making product recommendations and handling customer requests.

Parting Thoughts

In the finance industry, the disruption triggered by artificial intelligence and machine learning is increasing exponentially and toward greater economic impact than ever, both on the customers and the industry. By addressing all the major operational aspects and adding advanced features, these technologies are not only revolutionizing the entire industry but also improving the financial health millions of customers involved in the process. And from a business perspective, these technologies are driving a more fundamental and deeper shift in the finance industry.

To learn more about machine learning, click here and read our another article.

The post How Has Machine Learning And Ai Changed And Continue To Change The Finance Industry? first appeared on Magnimind Academy.

]]>