INTRODUCTION:Machine Learning is a subfield of Artificial Intelligence that enables machines to improve at a given task with experience. It is important to note that all machine learning technique are classified as Artificial Intelligence ones. However, not all Artificial Intelligence could count as Machine Learning since some basics. Rule-based engines could be classified as AI but they do not learn from experience therefore they do not belong to the machine learning category.
Humans acquire knowledge through experience either directly or shared by others. Machines acquire knowledge by following the instructions by the users(humans). It can be process millions of instructions in a second and return the result.
MACHINE LEARNING PROCESS:
1.Define the Problem:
The Machine Learning process starts with defining a business problem defining is very important. To define it we will divide into Task(T), Experience(E) and Performance(P).
2.Collect the data:
After defining the problem, data collection process starts. There are different ways to collect the data. I f we want to associate review with ratings, we start by scraping the website. For analyzing twitter data and associate it with sentiment, we start by APIs provided by twitter and start collecting the data for a tag or which associated with a company. Marketing researcher create different survey form and put it on the website to collect the data.
3.Prepare the data:
After data collection you need to focus on data preparation. Once you collect the data, you need to prepare it in the format used by machine learning algorithm. After identifying data on higher level basis, we need to transform or preprocess it to make it useful for machine learning algorithms. These are some of the process involves in preprocessing of the data.
- Cleaning
- Formatting
- Sampling
- Decomposition
- Scaling
4.Split data in training and testing:
The goal of any machine learning algorithm is to predict well on unseen new data. We use training data to build the model. In training data, we move the algorithm in the direction which reduces training error. But we cannot consider accuracy on training data as the generalized accuracy. The reason is that the algorithm may memorize the instances and classify the points accordingly. Testing data is hidden to the algorithm at the time training. Our general method is to use 60-80% data in training and rest of it as testing.
5.Algorithm Selection:
We start with our set of Machine learning algorithm and apply the feature engineered training data. Algorithm selection depends upon on the problem definition. Types of Algorithm are Classification(Decision Tree, Naïve Bayes, Neural Networks etc.), Regression(Linear Regression, Kernel Regression, etc.) and Clustering.
6.Training the Algorithm:
After algorithm selection we start with training the model. Training is done on training dataset. most of the algorithm start with random assignment of weights/ parameters and improve them in each iteration. In training algorithm, steps run several times on the training dataset to produce results.
7.Evaluation on Test data:
After creating best algorithm on training data, we evaluate performance of test dataset. Test dataset is not available to algorithm during training. So, algorithm decisions are not biased by test dataset points.
8.Parameter Tuning:
After selecting right algorithm for our problem, we start it and try to improve it for better performances. Each algorithm has different types of setting which we can configured and change the performance. This is called Parameter tuning. For example, for learning rate of algorithm, we can change the rate of learning and improve the performance. These parameters are called Hyper Parameters.
9.Start Using your Model:
After completing all the above steps, you are ready with the model which you trained and evaluated on your test dataset. Now you can use this model to start predicting the values for the data points. Whenever you get the data, you start by looking all the above steps to improve your performance.
So, in machine learning we start by our problem and finally finish with a prediction algorithm to solve the problem.
APPLICATION OF MACHINE LEARNING:
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