Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
The importance of machine learning can be understood when we realize how easily machine learning techniques can be used to solve problems that seem really hard eg., face recognition, you would realize that ML algorithms can tackle many seemingly difficult problems as long as there is enough data.
Here are few steps to learn Machine Learning:
1. Programming Skills: There are multiple languages which provide machine learning capabilities. Also, there is development work happening at a rapid pace across several languages. Currently "R" and "Python" are the most commonly used languages and there is enough support or community available for both.
2. Learn basic Descriptive and Inferential Statistics: It is good to have understanding about the descriptive and inferential statistics before you start serious machine learning development.
Descriptive statistics give information that describes the data in some manner. For example, suppose a pet shop sells cats, dogs, birds and fish. If 100 pets are sold, and 40 out of the 100 were dogs, then one description of the data on the pets sold would be that 40% were dogs. This same pet shop may conduct a study on the number of fish sold each day for one month and determine that an average of 10 fish were sold each day. The average is an example of descriptive statistics.
Inferential statistics takes data from a sample and makes inferences about the larger population from which the sample was drawn. Because the goal of inferential statistics is to draw conclusions from a sample and generalize them to a population, we need to have confidence that our sample accurately reflects the population.
3. Data Exploration, Cleaning and Preparation: What differentiates a good machine learning professional from an average one is the quality of feature engineering and data cleaning which happens on the original data. The more quality time you spend here, the better it is. This step also takes the bulk of your time and hence it helps to put a structure around it.
4. Introduction to Machine Learning: There are various resources available to start with Machine learning techniques. I would suggest you to pick one of the following two ways depending on your style of learning:
First option has to be learning through books. There are many books available which are excellent to start with.
Now a days there are many courses available and these are the best way to kick start your machine learning journey. Both students and professionals will have an edge over all other applicants if they leverage these degree or a certification on the same. I would personally recommend Intro to Machine Learning - GL4L because the tutorial is absolutely free and very easy to understand for a beginner. Therefore it is advisable to take advantage of this course and understand the basics of machine learning as it will give you a very good idea about the whole machine learning concept. Things are taught step by step, it is all hands-on and not theoretical. It is easy to get lost and waste time learning many different aspects. I like this course because it covers only what is needed and no more and it is well explained.
5. Advanced Machine Learning: This step will be mostly covered if you choose the certification courses but if you are learning by books then these are few extra topics you will have to study thoroughly. These topics include:
Deep learning, a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a nonlinear approach. A traditional approach to detecting fraud or money laundering might rely on the amount of transaction that ensues, while a deep learning non-linear technique would include time, geographic location, IP address, type of retailer and any other feature that is likely to point to a fraudulent activity.
Ensemble Modelling is a powerful way to improve the performance of your model. It usually pays off to apply ensemble learning over and above various models you might be building. Learning this is where an expert can be differentiated from an average professional.
Machine Learning with Big Data As you know that the size of data is increasing at an exponential rate but raw data is not useful till you start getting insights from it. Machine learning is nothing but learning from data, generate insight or identifying pattern in the available data set. There are various application of machine learning.
6. Gain Experience and Work On Real Projects: Once you've got a solid grasp on all the theoretical aspects of Machine Learning, it's time to get down to the field. Expose yourself to the industry and try to find real data science projects on the Internet. g algorithms like "spam detection", "web document classification", "fraud detection", "recommendation system" and many others.