Top 5 Training Courses on Machine Learning Use Cases [with Training Material Included]

Let us begin with a small example.

Predictive maintenance in manufacturing: Machine Learning (ML) analyzes sensor data to predict equipment failures, enabling proactive maintenance scheduling, minimizing downtime, and optimizing operations. This application improves equipment reliability, reduces costs, and maximizes productivity by addressing maintenance needs before they become critical issues.

This is one of the innumerable applications of ML in up-scaling and optimizing functions being performed manually (to any degree). With this training module, we address all kinds of audience, but focus especially on the one that does not know of the prevalence of ML, even as they use it in their smartphones or computers on a daily basis.  For instance, functions like translation, face recognition, speech recognition etc use ML.

The trainer can find this Comprehensive Training Course on Machine Learning Use Cases from SlideTeam here. This PowerPoint module has the following benefits for the trainees and the trainer as well:

1. Understanding how tech impacts life: Gaining knowledge about the use cases of machine learning can facilitate individuals to comprehend how technology influences their lives. The usage of machine learning algorithms to resolve intricate issues and predict outcomes is illuminated by it. These technologies' potential and limitations can be understood by individuals through this.
2. Career Opportunities: Skilled professionals are highly in demand in the rapidly growing field of machine learning, leading to ample career opportunities. Identifying potential career opportunities is possible for individuals who learn about use-cases of machine learning. Informed decisions can be made by them regarding the pursuit of relevant education or training. Individuals can harness this knowledge to investigate employment opportunities in domains like data science, artificial intelligence, and automation.
3. Informed Decision Making: Making informed decisions in today's world involves relying more and more on machine learning algorithms, which have a growing impact on various decision-making processes such as tailored recommendations, detecting fraudulent activities and diagnosing medical conditions. Individuals have the ability to make better-informed choices regarding the products they utilize and the services they select. Machine learning-driven solutions enable them to make more knowledgeable judgments regarding the advantages and drawbacks of depending on them.

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Session I: Introduction to Automatic Language Translation using ML

You’ve used a translator before on your phone, computer, or another device. These companies don’t just provide this translation service; they use the input to train their language models or in other words you are the product (This is why the services are free). This section discusses Google Translate, Microsoft Translate, and Facebook Translator.

Access the Training Courses on Machine Learning Use Cases

The usual experience when these pieces of software provide an incorrect or inappropriate translation, they are never the intended result (even though the answers may make you laugh).

Access the Training Courses on Machine Learning Use Cases

This is where we get an opportunity to discuss some of the limitations of automatic language translators. Download this PowerPoint Deck to access this information for your presentation.

Session II: Introduction to Medical Diagnosis using ML

Some people do not or cannot go to a doctor. Maybe they live in a remote area, maybe they cannot afford it, or maybe they are just scared. The reasons are irrelevant as with ML; ultimately, everybody will have the capability to get diagnosed in the comfort of their own homes.

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In this part of the module, we discuss how ML arrives at a medical diagnoses. We also foray into a study of the objectives of such an attempt, its benefits, and possible application areas. As a fun exercise, have the audience look up the accuracy of the diagnosis made by AL/ML. The results will shock them (remember, we are not talking about a rare terminal disease, but something more on the lines of we encounter frequently with our own health or our loved-ones).

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We complete this session with a talk on organizations currently using ML for medical diagnoses, such as Google Health, Corti, and more.

Session III: Introduction to Image Recognition using ML

Each time you unlock your phone with FaceID, it gets better at recognizing you. We all remember the debacle when some iPhones could be opened via FaceID by people who were not the owners of the device.

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In this training section, we elaborate on what image recognition is, how it works, and its applications in face analysis and animal monitoring (maybe the tech gives each sheep in the herd a name!).  We also learn about some image recognition models, such as the support vector machines, the Viola-Jones algorithm, etc.

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Session IV: Introduction to Speech Recognition using ML

“Hey Siri, send a message to Mom.” This spoken sentence not only activates your Apple AI but also allows you to transcribe a message to your mother. Speech recognition is excellent and fun and gives us ads for what we discuss out loud, much like a spy or a fly on the wall. How convenient!

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But what exactly is it, and how does it work? We answer these questions in Session IV and explain other concepts, such as the key features, algorithms, and more.

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Session V: Key Takeaways and What We Have Learnt

All educators must ensure the class remembers what was taught. In the final session, we summarize the key takeaways from the training module and take a fun quiz to jog everyone’s memory, and whether the major concepts are part of the long-term memory of the trainees.

With the training done now, the trainer can open the floor for comments and questions to allow everyone to clear their doubts and bring up their experiences and conclusions.

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Let’s Learn Some Machine Learning 

The evolution of technology is an exponential process. Before you know it, that inappropriate and fun translation is perfectly correct and in context (YAY). In this blog piece, we have examined briefly what our Machine Learning PPT Presentation offers you and your audience. Access the training module for the full package.

The slides are content-ready and 100% editable, providing more flexibility and saving you time and energy. The presentation is just a single click away. Why wait?

FAQs on Machine Learning

What is machine learning? Give an example. 

The development of algorithms and models is the main focus of machine learning, which is a field within artificial intelligence. Computers can learn and make predictions or decisions without being explicitly programmed thanks to these. The process entails utilizing statistical methods to enhance the performance of machines on a particular task gradually.

One instance is the possibility of instructing a machine learning algorithm using a labeled email dataset (spam or not spam). Email spam detection can leverage this. The patterns within the data are learned from. Afterward, the expertise is utilized to assess and categorize incoming emails as either junk or legitimate.

What are the four basics of machine learning? 

The four basics of machine learning are:

1. Data: Machine learning requires a dataset that is representative of the problem or task at hand.
2. Algorithms are mathematical models and techniques that process the data and make predictions or decisions.
3. Training: The algorithm is trained on the dataset to learn patterns and relationships.
4. Evaluation: The trained model is evaluated on new data to assess its performance and accuracy.

What is machine learning with Python?

To implement and apply machine learning algorithms, Python is used along with its libraries including sci-kit-learn, TensorFlow, and Keras in the process of machine learning with Python. Using Python and its libraries, one can apply machine learning algorithms as explained in the given sentence. Data manipulation, preprocessing, model building, and evaluation are among the various areas extensively covered by Python's comprehensive ecosystem. Machine learning tasks often choose this option due to its popularity.