Machine learning is a subfield of artificial intelligence (AI). The goal of machine learning generally is
to understand the structure of data and fit that data into models that can be understood and utilized by
people.Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence,
coined the term "Machine Learning" in 1959 while at IBM.
A form of artificial intelligence, machine learning is revolutionizing the world of computing as well as
all people’s digital interactions. By making it possible to quickly, cheaply and automatically process and
analyze huge volumes of complex data, machine learning is critical to countless new and future
applications. Machine learning powers such innovative automated technologies as recommendation engines,
facial recognition, fraud protection and even self-driving cars.
This Machine Learning Program prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for internship and job competency in machine learning. The demand for machine learning skills is growing quickly. The median salary of a Machine Learning Engineer is $134,293 (USD), according to payscale.com.
t’s a fascinating field of artificial intelligence which enables computers to learn without being programmed. Instead of this machine learning algorithms are training in such a way to identify the patterns and make the predictions.
Main objective of machine learning is to understand the basic fundamentals, algorithms, understand the mathematics and static underlying. This will help in solving the real world problems, looking up the ethical factors, help to stay updated with advancements.
Average salary expectation of a machine learning expert is 7, 77,030/year
In the early career stage they work as Machine Learning Engineers, Data Analysts, or Research Assistants. In the mid-career stage they will become Team Lead, Senior Machine Learning Engineer, or Research Scientist. In the late career, they will become Head of AI, Chief Data Officer, or CTO
Machine learning is nothing but a explosive that changes all our lives through making the technology much more advanced in doing the activities.
Building and deploying the models of MI, Analysis of the data, ensuring the seamless interaction with the system. Exploring new algorithms, analysis of existing algorithms etc
Technovalley is a leader in the industry of certification and training programs. As a multi-national corporation headquartered in Kochi, India with a pre-valuation of 235 million USD, Technovalley has expanded to Africa and the Middle East and has certified over thousands of interns. With multiple divisions including Technovalley-AKS, Technovalley-ACS, and Technovalley-ATS, and partnerships with 18 global major companies, you can trust that you are in good hands when choosing Technovalley. One of the key advantages of choosing Technovalley is its focus on hands-on, practical training. Our consultants are experienced in the industry and certified in their respective fields. We consider our students as interns, providing them with training on real-world situations. With this approach, you will be well-prepared for the workforce and ready for the job opportunities that await you. Plus, with our programs covering areas such as Cyber Security Stack, IT Infrastructure Stack, and Emerging Technologies Stack, you can be sure that you are learning the skills that are in high demand. In addition to providing top-notch training, Technovalley also offers a range of support and resources to our students. We have state-of-the-art infrastructure, a modern software lab, and certified and experienced consultants to help you succeed. Our students have gone on to achieve great things, such as identifying security weaknesses in global companies like Apple and Google, creating multiple websites based on learned technology, and being empanelled by the Kerala Startup Mission to train more than 6500 startups across Kerala.
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Subfield of AI where algorithms learn from data to improve their performance over time, without being explicitly programmed.
Supervised learning: Algorithms learn from labeled data (input-output pairs) to make predictions on new data. Examples include classification and regression.
Unsupervised learning: Algorithms find patterns and structure in unlabeled data without prior knowledge. Examples include clustering and dimensionality reduction.
Reinforcement learning: Algorithms learn through trial and error in an environment, receiving rewards for desirable actions and penalties for undesirable ones.
Healthcare: Diagnosing diseases, predicting patient outcomes, and developing personalized treatment plans.
Finance: Fraud detection, algorithmic trading, and risk management.
Manufacturing: Predictive maintenance, optimizing production processes, and quality control.
Retail: Personalized recommendations, targeted advertising, and dynamic pricing.
Transportation: Self-driving cars, traffic prediction, and route optimization.
