Machine Learning is a science to make the machine capable of learning from past experience and provide output according to its experience. It is an algorithm that aims mainly to give an accurate prediction for the future outcome without being programmed. The basic concept of Machine Learning is to use statistical analysis that exploits the received input data to predict the output and update it with new data. Hence, it is a kind of adjusting program action according to input data patterns.
In this post, we are going to discuss the main types of machine learning, some approaches and examples, and the skills needed to pursue the career.
Types of Machine Learning – How it works
- Supervised learning algorithm which relies on provided both input and desired output and furnished feedback about the accuracy of predictions. In other words, bunch of training examples and their correct answers (labels) are provided. After enough training, the system will be able to identify the new data spontaneously.
Skillful ML data scientists are required to provide enough supervised learning to apply it to the new data in the future. Supervised learning can be demonstrated by the Google self-driving car that is trained by letting it watch human drivers and emulate their behavior (correct answer or label).
This SL technique means that you simply have some inputs that are related to each other and their expected outputs are given. The target is to predict the new data samples accurately after being trained sufficiently. A great example of regression is the price of houses (output) according to the given data about each house such as: number of rooms, color, location, and quality of furniture.
Here, the training includes bunch of examples that are classified into categories or groups and the goal is to gain sufficient experience to be able to classify future data precisely.
- Unsupervised learning algorithm depends mainly on reviewing data and building conclusions. In contrast to supervised learning, no labels or correct answers are provided. Instead, it identifies correlations and discovers hidden patterns between different variables after combining through several training data.
Once trained, it can understand the new data using its firm grasp of gained associations. A common method of unsupervised learning is clustering. A good example of USL is the Facebook news feed, where it begins to show old or unread data earlier in the feed.
- Reinforcement learning
It is an algorithm or program that learns by its own using the feedback from the environment in order to reach its intended goal or target. An example of this approach is learning to play football by playing against another player. Another instance is to learn to cook by interacting with a chef.
Some Machine learning approaches
It is a kind of mapping the observation of an item to its intended target goal.
Support Vector Machines (SVM)
It is a training algorithm that establishes a model in order to predict the category of the new input data or example.
Artificial Neural Networks (ANN)
The idea of this algorithm came mainly from the biological neural networks. As in the human’s optic nerves, the network can be described as interconnected tiers of processors. The raw input data is received by the first tier and then its output becomes the input to the successive tiers and so on. Broadly speaking, each tier receives the output from its preceding tier and the final system output is produced by the last tier.
Several subsequent runs and training stimulate the network to modify and increase its information about the world.
Real Applications – Examples
It is one of the most common ML applications. The measurements represent the output of each image pixel. For the black and white image, the single measurement describes the intensity of each pixel whereas in colored images, the single pixel characterizes the intensity of three intensities of the main color components (RGB).
It is the translation of spoken words into text. The speech signal is represented by a set of numbers and segmented into different unique portions; each has its distinct words. For each segment, the speech measurement is done by identifying the intensity or the energy in diverse time-frequency bands.
Skills needed to pursue
1. Programming Fundamentals
Machine Learning engineers should have a sufficient background in data structures, algorithms like sorting and optimization, computability and complexity (P vs. NP, big-O notation), and computer architecture. Your duty is to adapt and implement or address them effectively.
2. Probability and Statistics
The heart of machine learning is the probability and its derived techniques. The different algorithms of machine learning also depend mainly on statistics, analysis methods, and distributions.
3. Data Modeling and Evaluation
A qualified machine learning engineer should have the skill of data modeling such that he/she can evaluate a given model accurately by realizing hidden patterns and predicting expected outputs for new inputs. This can be achieved by choosing suitable error measure and evaluation strategy.
4. ML Algorithms and Libraries
Machine learning algorithms can be implemented through bank of libraries and packages such as scikit-learn, Spark MLlib, TensorFlow. However, the important task is to choose the optimum model (decision tree, nearest neighbor, SVM, NNs) and best learning procedure to fit the data (linear regression, gradient descent, etc.).
5. System Design and Software Engineering
After choosing the appropriate ML algorithm, it is necessary to manage and monitor it carefully using effective system design to get rid from bottlenecks and let the algorithm deal well with increased data. Furthermore, it is invaluable to have considerable software engineering skills to contribute in the quality, productivity, and maintainability of the system.