A Machine Learning Course can prepare you for a promising career in the field. Machine Learning is one of the fastest-growing fields today. ML engineers build algorithms to automate complex machine functions, which can improve productivity, accuracy, and safety for the stakeholders. With rising demand, a career in machine learning offers a safe and promising future for today’s graduates.
The best way to enter the ML field is to enroll in a suitable Machine Learning course after your graduation. Before that, you must ensure that you are aware of what machine learning is and what all ML algorithms are used in organizations. Here are some most widely used ML algorithms that you must know if you aspire to become an ML engineer:
Linear regression is one of the most widely known algorithms in machine learning. It deals with the relationship between dependent variables (Y) and independent variables (X). The value of variable Y is predicted based on the value of variable X. If there is a single independent variable, the regression is called simple linear regression. On the other hand, when you deal with more than one input variable, it is called multiple linear regression.
Linear Regression is used to establish a relationship between the input and output variables in the form of a straight line. This line is called the regression line and is represented by the equation Y = aX + b, where ‘a’ is the slope and ‘b’ is the intercept. The regression line can either show a positive linear relationship or a negative linear relationship.
Logistic Regression estimates the binary values, or discrete values, using an estimated set of data. ML professionals use it to predict the probability of an event. The possible number of outputs is limited. You get results like Yes or No, 0 or 1, etc.
‘1’ denotes the occurrence of an event, and ‘0’ is its non-occurrence. You don’t get the exact value of 0 or 1, but the regression gives you probabilistic values between 0 and 1.
You cannot fit a regression line into a logical function with 0 & 1 possibilities. But you can fit an ‘S-shaped’ logistic function. The logistics function predicts two maximum values (0 or 1). Logistic regression becomes useful when you need to find more logic behind a prediction. It works better when you remove unrelated and correlated attributes. It is a fast method to learn and solve binary classification problems.
It is a powerful algorithm in machine learning that is used for both binary and multi-class classification problems. A Naive Bayes considers all the features in a class independent of each other, even if they are related.
In other words, it assumes that a particular property remains unaffected by the presence of other features. An ML model based on Naive Bayes is easy to build and can be used for huge datasets. It is simple but can outperform even the top sophisticated classification models.
An SVM (Support Vector Machine) algorithm is used for both classification and regression models. It allows you to plot raw data in an n-dimensional space, where n denotes the number of features in a class. If there are only ‘2’ features, your hyperplane will be a line. If there are ‘3’ features, the hyperplane is a 2-D plane.
However, the hyperplane becomes difficult to imagine when there are more than ‘3’ features. After plotting the data, you must tie the value of each feature with a coordinate to make data classification easier.Also Read: An easy guide to help you decide which Machine Learning algorithm to use for your business problem
As the name suggests, a decision tree uses a tree-like flowchart model to predict results from a series of features. Decision trees work well for both classification and regression problems. It can be used to classify continuous as well as categorical dependent variables. A decision tree starts with a root node and ends with a leaf node that gives the final decision. Here are some important terminologies related to decision trees:
K-Means is an unsupervised ML algorithm that is used to solve clustering problems. The unlabeled data sets are classified into different clusters and the number of clusters is denoted by K. The data sets are grouped into clusters in such a way that all the data points in a cluster are homogeneous. Moreover, these data points should be heterogeneous from the data present in other clusters.
The K-means algorithm takes unlabeled data sets as input, divides them into K number of clusters, and keeps repeating the process until it finds the best clusters. Here are the major tasks that a K-Means algorithm performs:
KNN (K-Nearest Neighbors) is a simple but effective algorithm in machine learning. The entire available data set is used to represent a KNN model. Under this, you must make predictions for a new data point. You can do this by searching for the entire training set for K-similar instances and summarizing the output variable for those instances. It may be the mean output variable for regression problems and the mode class value for classification problems.
You can find the similarity between data instances using the Euclidean distance. Euclidean distance is a number that you can find based on the distance between two input variables. KNN will need a lot of storage space to contain all this data. However, it performs calculations only when predictions are needed.
A random forest refers to a collection of decision trees. It is a supervised machine learning algorithm that works for classification as well as regression models. It takes different samples to build decision trees on them and takes majority votes in case of classification problems. For regression problems, it takes the average.The random forest algorithm works as follows:
If the number of input variables is M, you specify a number m < M. Then at each node, you select m variables randomly out of M. (the value of m should be constant throughout the process)
When you have massive amounts of data to handle and make highly accurate predictions, Gradient boosting and AdaBoosting algorithms are used. Boosting is a machine learning algorithm that combines multiple predictors to build a strong predictor. These algorithms are used along with R and Python for the most accurate outcomes.
LVQ (Learning Vector Quantization) is an artificial neural network that also takes inspiration from biological neural systems. It is a supervised ML algorithm that works for classification problems. Under this, you can classify different patterns where each output represents a different class. If KNN gives good results on a particular dataset, you can use LVQ to reduce the requirements of memory to store the entire data.
Edvancer is one of the top career-oriented learning platforms in India. You can find the following Machine Learning courses at Edvancer:
All these courses are online and give you the flexibility to learn anytime and from anywhere in the world. You get comprehensive coverage of all the required topics in Machine Learning. Moreover, the course allows you to develop your practical skills by working on real industry projects. Edvancer also gives you the option to choose one of the two learning styles (self-paced learning or live online classes) as per your comfort level.
Different types of Machine Learning are – Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforcement Machine Learning.2. What tools are used in ML?
Some widely used Machine Learning tools include PyTorch, TensorFlow, Amazon Machine Learning, Google Cloud ML Engine, NET, etc.3. Which programming language is used for ML?
Five majorly used programming languages for ML are Python, R, Java, Scala, and Julia.4. What is the ML lifecycle?
A Machine Learning lifecycle consists of different stages – Data gathering, Data Preparation, Data Wrangling, Analyzing Data, Training the Model, Testing the Model, and Deployment.Share this on