At Code Ninja we help you build your dream Machine Learning product with our expert consultancy services. Our consultants help develop an effective and efficient Machine Learning strategy and roadmap by realizing business workflows and objectives by conducting comprehensive business research.
Our ML models are highly optimized to meet your business needs. Our powerful algorithms use autonomous processes for decisions and evaluations to deliver consistent results. Adaptable ML solutions with optimal data science strategies designed by Code Ninja experts help businesses maximize resource efficiency, reduce costs and carry out complex tasks.
Code Ninja is expert in creating strategy roadmaps for your next big machine learning project. Our experts are experienced in providing solutions that help attain maximum efficiency and automate your processes with high accuracy. Our recommendation of modern market tools, data collection methods and machine learning models help businesses achieve their objectives in a seamless manner.
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Machine learning allows businesses to explore a range of possibilities, but only when done right! Machine learning is complex and it requires experience, expertise and comprehensive research to comprehend business needs and create an effective model around it. When looking for consultancy in machine learning, Code Ninja can provide you with:
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A machine learning model is built through a continuous cycle of training, testing and validation. Once you create an algorithm, you need to continue with this cycle till your outcomes are validated.
Deep Learning uses several layers of ‘neural’ networks to carry out tasks. It is a subgroup of Machine learning, however it is different from ML in the way that it automatically recognizes the features it would use. Whereas, the same has to be engineered in Machine Learning.
Machine learning is divided into three types:
Data science and machine learning are tightly interlinked. While data science is the tools and processes needed to extract meaningful data, machine learning uses meaningful extracted data to learn and make decisions based on learning patterns.
Training set is data or examples given to a machine learning model for learning whereas a test set is used to validate the accuracy of the learning model. It is a standard practice to allocate 70% dataset for training set and remaining 30% for test set.
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