Machine Learning is a technologically evolved tool which utilizes machine intelligence to capture the untapped areas of business models. We at GoodWorkLabs recognize Machine Learning as one of the pinnacle problem solvers for emerging and established businesses. Our prowess assist businesses in tapping the vast and unexplored reserves of unprocessed data. Be it data mining or analyzing or processing raw chunks of information, we can help you set up a formidable fortress of data supremacy.
Machine Learning is the field that studies how to make computers learn. In other words, a Machine Learning algorithm is a computer program that teaches computers how to program themselves so that we don’t have to explicitly describe how to perform the task we want to achieve. The information that a Machine Learning algorithm needs in order to write its own program to solve a particular task is a set of known examples.
For example, for the task of teaching a computer to identify animals, we will show to the computer a bunch of labeled pictures, e.g. this picture is a tiger, this pictures is a cat, etc , the same way we do it when we teach children.
1. Machine learning means learning from data; AI is a buzzword. Machine learning lives up to the hype: there are an incredible number of problems that you can solve by providing the right training data to the right learning algorithms. Call it AI if that helps you sell it, but know that AI, at least as used outside of academia, is often a buzzword that can mean whatever people want it to mean.
2. Machine learning is about data and algorithms, but mostly data. There’s a lot of excitement about advances in machine learning algorithms, and particularly about deep learning. But data is the key ingredient that makes machine learning possible. You can have machine learning without sophisticated algorithms, but not without good data.
3. Unless you have a lot of data, you should stick to simple models. Machine learning trains a model from patterns in your data, exploring a space of possible models defined by parameters. If your parameter space is too big, you’ll overfit to your training data and train a model that doesn’t generalize beyond it. A detailed explanation requires more math, but as a rule you should keep your models as simple as possible.
4. Machine learning can only be as good as the data you use to train it. The phrase “garbage in, garbage out” predates machine learning, but it aptly characterizes a key limitation of machine learning. Machine learning can only discover patterns that are present in your training data. For supervised machine learning tasks like classification, you’ll need a robust collection of correctly labeled, richly featured training data.
5. Machine learning only works if your training data is representative. Just as a fund prospectus warns that “past performance is no guarantee of future results”, machine learning should warn that it’s only guaranteed to work for data generated by the same distribution that generated its training data. Be vigilant of skews between training data and production data, and retrain your models frequently so they don’t become stale.
Machine learning helps to deliver precise and accurate outcomes because it is possible to automate and prioritize the decision making processes. When machine learning is clubbed with the Internet of Things it becomes easier to troubleshoot problems in the manufacturing process.
Machine learning outperform human mind when it comes to speed and versatility. This means data is updated on a real-time basis which is a big advantage in discovering new ideas, processes and adapting to constantly-shifting business scenarios.
“Machines learning” is all about complex algorithms that make sophisticated processes to appear simpler and easier. It is the basic component of that drives process automation resulting in improved and accurate decisions. What you get as a result is superior business models, improvised products and innovative services.
“Machine Learning” can help to eliminate errors that are commonly done by humans. The quality of deliverables is superior and with added cyber security so essential in the financial service businesses. It can help to protect sensitive data, due diligence and statutory regulations.
“Machine Learning” is exciting because it is able to process patterns in Big Data – a process that is beyond human abilities. This makes it possible to deliver actions at unprecedented speeds. Potentially “Machine Learning” can help to predict opportunities and even recommend ways to conclude business deals.
GoodWorkLabs is the industry leader in Machine Learning and has proven its mettle time and again with reputed clients. We understand Machine Learning like no one else and this makes us the only choice for all Machine Learning solutions. Apart from being reliable, our ML solutions are accurate and precise.
The scope of Machine Learning is infinite and when applied to existing business models it can dig up so much information which otherwise would seem impossible to extract and assimilate.
With the GoodWorkLabs advantage, these are the possibilities that you can extract with Machine Learning:
1. Image tagging: The Machine Learning algorithms can identify faces or specified objects in a photo based on the photos that you manually tag. This can reduce redundant tasks for manual data operators.
2. Optical Character Recognition: The algorithms learn to identify a certain image as a written character and convert a scanned text document into a digital file for various uses such as data grouping and image processing.
1. Predictions: Banking sectors can use this to analyse credit worthiness and probability of loan defaulters. Other applications include trading, building predictive models of prices and market volatility, portfolio management and risk management.
2. Anomaly detection: This technique is used for fraud detection. Companies can detect which transactions are outside the usual purchasing patterns of the user and warn them at an early stage. Also, retail and e-commerce websites can use these algorithms to block fake accounts.
1. Sentiment analysis: This method can be used to classify if the opinion expressed by the writer is positive, neutral or negative. This can be used for future marketing strategies and product development.
2. Information extraction: The algorithms can extract a particular piece of information such as names, web links, addresses, keywords etc.
3. Filtering: With Machine Learning, data can be classified as a tweet, chat, post, blog or spam posts. This can help firms which have huge inflow of social content on a daily basis.
Imagine the simplification that Machine Learning can bring to your business along with the added advantage of owning analysed data at the tip of your hands. All that data loitering in your servers and warehouses can be put to good use by processing information that could be used for your marketing strategies, brand management, sales pitches, and new innovative product development processes.
So, what are you waiting for ?
Talk to us today for a better understanding of Machine Learning and how it can change the way you look at business.