In this Article we will discuss Machine learning strategies and how to get started with machine learning.
What is Machine learning
Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. Machine learning uses a variety of algorithms that iteratively learn from data to improve, describe data, and predict outcomes.
Machine learning and its related topics of data science, have been mesmerizing the world by their advancement in technology and AI.
To get started with machine learning requires knowledge of programming language, math - statistics, Linear algebra and the subject area for which machine learning is being applied.
The most important thing to start with is the programming language you should be using for machine learning. There are many languages designed for machine learning Like R, Python, java and javascript. Python is known worldwide as the industry standard for machine learning and artificial intelligence. In the industry most of the work is done by using python. The availability of libraries and open source tools make it ideal choice for developing machine learning models. Python is not only used for machine learning, it is capable of doing many other things and it has most of the modules and support for artificial intelligence and machine learning. It may be one of the easier language to learn.
The next thing is that we require some knowledge about mathematics - statistics for getting started with machine learning need not be good at math to do machine learning. However if you have a good understanding of math it will be a plus point.
Lets see How to Start with machine learning Using python
Anaconda : Jupyter, Spyder, Rstudio these are the few Integrated Development Environment (IDE) used for computer programming. Jupyter, Spyder, Rstudio are included with Anaconda. Anaconda is a free open source, and makes package management deployment simpler. It is the standard platform for python, machine learning and data science. The advantage of using anaconda is that pre-installed many machine learning and data science libraries. It provides a package manager which is called conda. It makes it easy to install new packages.
Pycharm : Pycharm is an Integrated Development Environment (IDE) used for programming. It is widely used for the python programming language.
How to start build the Machine learning project
Step 1
In the machine learning project the first step is to analyze the problem and figure out what your machine learning model will do for you. such as predict something, create something or recommend the information. It is important to understand the problem, because we can use the right algorithm to tackle the problem.
Step 2
In the second step, we get to look for the data we will use for the machine learning model. Once we select the data our next step is the data cleaning process. Data cleaning is the most important part. Data cleaning is the process in which you go through all of the data within a data and either update or remove information that is incorrect, incomplete, improperly formatted, irrelevant or duplicate. Data scientists spend up to 80% of their time analyzing and cleaning the data before training the machine learning model.
Step 3
After cleaning the data we need to select the set of data to use for the machine learning project. The next step is to apply feature engineering on selected data if it is required. Feature engineering in machine learning is a process of transforming the given data into a form which is easier to interpret.
Step 4
Now here we Split the dataset into a training and testing set. We will use a training set used to train our machine learning model and the testing set is used to see how well our model performs on unseen data. We use the right machine learning algorithm to build and train our machine learning model on the training dataset and evaluate the validate and test the accuracy of the model.
Machine Learning Methods
Supervised
Unsupervised
Semi-Supervised
Reinforcement
Supervised : In supervised learning, data is composed of examples where each example has an input element that will be provided to a model and an output or target element that the model is expected to predict. Classification is an example of a supervised learning problem where the target is a label, and regression is an example of a supervised learning problem where the target is a number. Following are the supervised learning algorithms.
Un-Supervised : Unsupervised learning is a type of machine learning in which models are trained using an unlabeled dataset and are allowed to act on that data without any supervision. In unsupervised learning not use the target variable. Unsupervised learning cannot be directly applied to a regression or classification problem because unlike supervised learning, we have the input data but no corresponding output data. The goal of unsupervised learning is to find the underlying structure of the dataset, group that data according to similarities, and represent that dataset in a compressed format.
Semi-Supervised : This type of algorithm is neither fully supervised nor fully unsupervised. This type of algorithm uses a small supervised learning component i.e small amount of pre-labeled annotated data and large unsupervised learning component i.e. lots of unlabeled data for training.
Reinforcement : Reinforcement Learning is a reward based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. For each good action, the agent gets positive reward, and for each bad action, the agent gets negative reward or penalty. In Reinforcement Learning, the agent learns automatically using reward without any labeled data.
Reinforcement learning is a type of machine learning method where an intelligent agent (computer program) interacts with the environment and learns to act within that.
You can read complete article of Reinforcement : click here
Machine Learning Techniques
Classification
Regression
Clustering
Dimensionality Reduction
Classification : In supervised machine learning technique classification is used to categorize the set of data into classes on the basis of the training data. The model learns from the given dataset or observation and then the classification algorithm classifies the new observation into a number of classes like yes or no, true or false, 0 or 1 etc.
Regression : In machine learning regression is the supervised learning concept which basically predicts the output which is a continuous numerical value dependent variable based on the independent variable.
Clustering : It is a method of grouping the objects into clusters based on the object with most similarities that remains in a group and has less or no similarities with the object of the other group. clustering analysis finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities.
Dimensionality reduction : Dimensionality reduction is an unsupervised learning technique. In machine learning classification problems, there are often too many factors on the basis of which the final classification is done. These factors are basically variables called features. The higher the number of features, the harder it gets to visualize the training set and then work on it. Sometimes, most of these features are correlated, and hence redundant. This is where dimensionality reduction algorithms come into play. Dimensionality reduction is the process of reducing the number of random variables under consideration, by obtaining a set of principal variables. It can be divided into feature selection and feature extraction.
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