Learn Machine learning basics, what is Machine learning, Supervised Machine Learning, Unsupervised Learning and Machine learning examples.
Introduction to Machine Learning:
Machine learning is playing a dominant role in a wide range of critical applications such as Natural Language Processing, Image Recognition, Data Mining and expert systems. ML is a subset of Artificial Intelligence. Machine learning is turning to be a most wanted career in lot of leading companies like Google, Facebook, Microsoft, IBM, Amazon etc.At present the demand of Machine learning designers is very high and the salary package is just awesome. This field has definitely a bright future. So this is the right time to learn about machine learning. Machine learning brings computer science and statistics together to predict the future outcomes. This post introduces the basics of machine learning theory and its concepts. This makes you comfortable with the techniques and the logic to follow with the topic.
So, what is Machine learning?
Machine learning is all about learning from examples. Each example has number of features and attributes to describe it. If you can pick up the right feature and it giving you the right information then you can classify new examples.Arthur Samuel in 1959 states that
“Machine Learning is a sub field of computer science that gives computers the ability to learn without being explicitly programmed.”In recent Tom Mitchell, Carnegie Mellon University stated that
“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”I know you didn't understand! I think it’s very difficult to understand through these definitions. I make it simple. Take an example, when you search something on Google search, what happens next? Yes! It shows millions of pages with the relevant keywords. But what happens inside the Google search? How it shows relevant pages with your keywords? Google handles some millions trillions of searches in a day, how it handles such large data with correct accuracy? This is where machine learning plays its role.
Showing relevant pages with keywords (task T), you can run it through a machine learning algorithm with data about past search keywords and its patterns (experience E) and if it has successfully “learned”, it will then do better at predicting future search results (Performance P).But unfortunately sometimes, even special machine learning algorithms may inaccurate in real word problems like “is this cancer or not?”, “what is the real value of this house”? Etc.
So, machine learning is a technique that analysis data and it derives actionable insights by developing algorithms or some logical rules.
Basic steps used in Machine Learning:
1. Collection of Data:It is the first step and this is the foundation upon which the entire data set. Careful planning is essential before collecting the data. The data may be in different formats like excel, xml, csv etc,
2.Presentation of Data:
The mass data collected should be presented in a suitable, concise form for further analysis. The collected data may have quality issues like missing data, outliers, Text error etc., and one should have to clear this issues using Exploratory Data analysis method.
3. Training a Model:
The cleaned perfect data is split into two types. Train data and Test Data. The train data is used for developing the model and test data used as a reference. Suitable algorithms are used.
4. Evaluating the model:
To test the accuracy, test model is used. This step determines the precision in the choice of the algorithm based on the outcome.
5. Improving the performance:
This step involves in increasing the accuracy. Choosing different model may increase the efficiency. If we spend some time in data collection and preparation we can get a better accuracy.
Types of Machine Learning:
1. Supervised Machine Learning:
Supervised learning algorithm is used to predict the outcomes based on the known data. This model gives suitable information like what needs to be learnt and how it needs to be learnt. Training data includes both the input and desired outputs.
Example: From an album of tagged photos, recognize someone in a picture. This example used by facebook. When you upload some bunch of pictures in your fb account it automatically recognize the face of person.
2. Unsupervised Learning:
The model is not provided with the correct results during the training. Without any known labels in data we have to figure out the patterns and relationships within the data.Example: Analyzing bank data for weird looking transactions and flag those for fraud. In this case we haven’t really defined what a weird looking transaction is? We don’t have any example of what that might mean.
3.Reinforcement Learning:
Machine is trained to make specific decisions to increase the performance or efficiency according to the business requirements. This type of learning has three primary components. i) The Agent: Agent is the learner or decision maker. ii) The Environment: refers the agent interacts with iii) Actions: refers what the agent can do.Example: Self driving car uses reinforcement learning to make decisions continuously.
Applications of Machine Learning:
Google, Facebook, Amazon uses machine learning extensively in their products. Some applications of Machine Learning are1. Financial Services:
To identify important insights in data and prevent fraud. It also helps investors to invest at right time period.2. Health care:
ML Used to identify diseases based on the symptoms and related past examples of patients.3. Marketing and Sales:
website like amazon uses machine learning to show relevant items to show based on your previous purchases. It increases user experiences and sales to the company.These examples are only trailer. Machine Learning has extensive applications. It used in almost every domain.
