Demystifying Machine Learning

Ethelemmons

Businesses worldwide embrace ML (machine learning) to gain valuable and actionable insights from various technologies. These can be everything from the voice-activated devices we use as consumers to how we interact with custom car innovations. The feedback loop of ML creates a personalized environment parallel to the latest and most outstanding customer service.

This topic has become incredibly popular recently because the application use case potential has exploded. You will likely see ML applications in farming, finance, security, retail, fashion, social media, healthcare, entertainment, sports, and even how MLB teams decide what players to pick for the upcoming season.

Let’s dive deep into what machine learning is and demystify the stigma that this is some unreachable technology. You need to integrate ML applications into your business operations to remain competitive in today’s marketplace.

What is Machine Learning?

Machine learning, at its most fundamental core, is a type of AI (artificial intelligence) that allows computers and frameworks to learn and improve based on consumer input, experience, and need – all without additional programming. These customized algorithms can analyze massive amounts of data, carefully interpreting their meanings to identify patterns that lead to predictive analyses and improvements.

An excellent example of this is the systems in e-commerce security plugins. These ML technologies learn from fraudulent historical transactions to predict if a current consumer engages in friendly fraud, chargeback fraud, or malicious intent.

ML differs greatly from traditional programming because predefined rules or programmed instructions are not relied on (remember the good old days of “if-then-else”). Instead, everything is based on statistical models that allow data to improve accuracy and predictions over time. That is why many businesses are experiencing a massive benefit due to how ML handles large amounts of data or complex problems.

Applications of Machine Learning

ML is used to improve and enhance today’s technologies in several ways. All these applications can be stretched across practically any industry you can imagine and may include:

• Image Recognition:?Image recognition helps identify objects, faces, and other features within a picture as well as improves image-to-text extraction.
• Natural Language Processing:?NLP helps ML devices understand and analyze human language – improving tasks like language translation, sentiment analysis, and chatbot interactions.
• Fraud Detection:?By analyzing financial data, these tools identify suspicious patterns or behaviors that may indicate fraudulent activity.
• Personalized Recommendations:?This technology creates a more rewarding experience for consumers based on recommendations for products, services, or content from a user's past behavior or preferences.
• Sentiment Analysis:?People’s sentiment is one of the determining factors of stock market fluctuations regarding stock price predictions. NLP (natural language processing) is used to perform sentiment analysis from various unstructured data sources, such as social media, news, among others, along with classification and clustering algorithms to classify a stock as negative, positive, or neutral.?
• Financial Advisory and Portfolio Management: Robo-advisors help the customers to manage their budgets and expenses, customize their portfolio, determine the spending patterns, and provide recommendations on improved savings and investments. ?
• Cybersecurity:?This includes email monitoring, anomaly detection, defense against spam and bots, and drive-by download attack detection.
• Autonomous Vehicles:?Machine learning is used in autonomous vehicles to help them perceive their surroundings, make decisions, and navigate safely.
• Predictive Maintenance:?With the help of sensor data from equipment, ML can predict when maintenance or repairs may be needed, which can help reduce downtime and costs.
• Customer Journey Optimization:?The ultimate goal is to optimize the customer acquisition cost to a specific conversion point. This cannot happen without insights from customer data. Customer journey paths are determined by ML algorithms and provide a score for each path. This approach considers the customer acquisition costs and customer lifetime value as the factors.?It predicts the next touchpoint to enhance the possibility of a specific outcome.
• And many more!

Many businesses are jumping on the chance to engage and integrate ML tools. These improvements allow for higher optimization in supply chain operations, better detection of errors or potential fraud, and the ability to create a more welcoming and personalized customer support interaction.

Getting Started with Machine Learning

Unfortunately, there is no quick and easy way to jump into machine learning development. Some drag-and-drop no-code/low-code frameworks are available online, but for the most part, you will need to become familiar with various programming languages. This is why there is such high demand for innovative code workers in the job market – they are crafting tomorrow's ML tools.

However, there are some steps you can take to ensure you are aware enough of ML attributes before investing. These steps help demystify the stigma that ML is out of reach from everyday consumers and include:

1. Familiarize yourself with the basics: Before you dive into machine language, you must gain experience with the underlying concepts, such as data mining, statistical modeling, and neural networks.?
2. Choose a language or platform: If you cannot hire a team to do the work for you, you should jump into a language and get to work. Many different programming languages and platforms can be used for a machine language, including Python, R, TensorFlow, and Keras. Choose a language or platform that aligns with your interests and goals.
3. Practice with real-world examples: To truly master machine language, you'll need to get hands-on experience. This is best achieved through online datasets and available challenges that create scenarios where you can learn more and innovate with ML concepts.

Here are the best online courses to get you started with both theories and hands-on practice:

a.?????Machine Learning Specialization ?by DeepLearningAI

b.?????Machine Learning Collection ?by Andrew Ng

c.?????Microsoft Azure AI Fundamentals

d.?????AWS Machine Learning

e.?????IBM Applied AI

The most crucial way to demystify machine learning is to keep updated with the latest developments in the industry. Subscribe to blogs or podcasts and visit trade shows where new technologies are unveiled. Try to filter these by the industry or niche market you hope to serve.

With applications in machine learning rapidly evolving, it's essential to stay updated with the latest trends and technologies. Attend conferences and meetups, read industry publications, and engage with other machine learning professionals enthusiasts to stay in the know.

The Future of Machine Learning

We expect to see even more powerful applications emerge as machine language evolves. Some of the most promising areas of development include autonomous vehicles, personalized medicine, and augmented reality.

Go to any big box store today, and you will find products ranging from Oculus Rift AR technology to call-and-respond toys for kids using ML applications. Once you get acquainted with how and why ML tools are used, you cannot go anywhere without seeing them applied in some way or fashion.

Conclusion

Machine learning is a fascinating field with tremendous potential for businesses and individuals. By demystifying the basics and providing a roadmap for acclimatizing yourself to this technology, you will use ML tools in all areas of your personal and professional life.