It is a method of data analysis that automates analytical model building. It is a process of teaching a computer system about how to make decisions when required it fetches data from a data model. Machine learning is a subset of Artificial Intelligence. It is a process in which a machine or a system gains the ability to automatically learn from its experience without being programmed explicitly. The primary idea is to allow the system to learn and improve automatically without any human interference or assistance. This is done through observation of data and the patterns in data and making decisions. This is where data science comes into the picture.
Types of Machine learning
- Supervised learning: It is the most basic type of machine learning. The data needs to be labeled accurately and then provided in the machine learning algorithm. The ML algorithm is trained on a smaller dataset which is usually a part of the bigger dataset. It serves to give the algorithm a better sense of the problem, solution, and data points to be dealt with. The algorithm then establishes a relationship between the parameters provided. At the end of the training, the algorithm understands how the data works and the relationship between the input and output or the cause and effect.
- Unsupervised learning: The machine learning algorithm works on unlabeled data. This results in the creation of hidden structures. It does not take inputs from the user and establishes an abstract relationship. The advantage of unsupervised learning is that it can adapt to the data by dynamically changing hidden structures.
- Reinforcement learning: It takes inspiration from how human beings derive information from raw data. This algorithm improves upon itself and learns using trial and error methods. In every iteration of the algorithm, the resulting output is given to the interpreter which decides if the result is favorable or not and the iteration continues till a certain level of accuracy is achieved.
There are Seven Steps of Machine Learning
- Gathering Data
- Preparing that data
- Choosing a model
- Hyperparameter Tuning
How does Machine Learning work?
The three major building blocks of a Machine Learning system are the model, the parameters, and the learner.
- Model is the system that makes predictions.
- The parameters are the factors which are considered by the model to make predictions
- The learner makes the adjustments in the parameters and the model to align the predictions with the actual results
Applications of Machine Learning in day-to-day life
Machine learning has entered our day-to-day lives and sooner our dependency will increase on these technologies including artificial intelligence. Machine learning helps companies improve the scalability of the business and enhance operations. Hence the demand for machine learning engineers is quite high. Due to this huge demand, they are also getting paid handsomely. Pursuing KnowledgeHut’s Machine learning training can also increase the chances of getting a job. Here, we discuss some of the examples of machine learning that we use commonly.
- Traffic predictions: While commuting, we get an idea of traffic congestion through GPS navigation services. The data gets updated when you are traveling and updates this on the maps and gives an approximate prediction based on daily traffic analysis. Baes on this, you can be notified of the alternative routes with less congestion and helps in traffic control. You can learn about this service here.
- Virtual personal assistants: Google Allo, Siri, and Amazon’s Alexa are the most common virtual assistants we come across in our daily lives. Machine learning plays a vital role in the functioning of these assistants. they collect and refine data based on your previous use and actions on your device and give you customized results. It can command other apps and deliver you the desired results. One can perform activities like setting an alarm, playing a song, calling a particular contact, and so much more. All of these involve machine learning.
- Online transportation services: Online transportation services have become very common. We all are familiar with apps like Uber, Ola, etc. and most of us depend on these for our daily commute. One striking feature is the surge charges, During the hours when the traffic congestion is high or demands are high, the customers are charged higher than usual fees. This is done with the help of machine learning. The traffic congestion is predicted based on the daily traffic data, and when the demand increases, mostly during the office hours, the prices are increased.
- Customer support: Many websites and apps provide an option to chat with customer care to resolve their queries. Most of the time, we don’t chat with an executive but with a chatbot. According to this website, these chatbots collect information from their websites and show you the relevant information. These chatbots have been upgrading with time to provide better customer experience. They are upgrading themselves with the use of machine learning to deliver more relevant information with minimum human interference.
- Healthcare: The contribution of machine learning in healthcare is revolutionary. We all commonly use wearable gadgets like fit bands that record data related to our health. This data can be used to predict the health progress of the patient and provide the required diagnosis and treatment. Epidemic outbreaks and the trend of rising and fall of the curve can be predicted by observing the data patterns and other factors. It can prove as a preventive measure to take the necessary measure. One can predict the difficult phases of an outbreak by observing the response to climatic changes, the availability of safety equipment, and so much more.
- Social media: There are many features on social media that use machine learning to provide better customer experience. Recommendation to tag other people in your photos is derived using image processing and using that data to estimate the probable match. ‘Friends you may know’ is another feature that uses machine learning. You get recommendations based on mutual friends, interests, and other data.
- Shopping websites and apps: We ofter get recommendations regarding the ‘products you may like’ on shopping portals. This data is derived from the items you have added in your wishlist, or products you have shopped before, or even products that you have viewed before. It enhances customer experience and gives you related data.
As is evident, machine learning has entered our daily lives and it has made our lifestyles convenient. The feature of customization has proved to give better customer experience and better profits. It is still in its evolutionary phase and there is a wide scope for research and discovery. This leads to a range of job opportunities that can be challenging and rewarding too.