In a nutshell, machine learning trains a computer system to make certain predictions based on the data fed to it. These predictions can be identifying a fruit as papaya or watermelon, handling a self-driving car smartly, identification of words in a sentence, whether an email is spam or not, recognizing speech and images accurately, and more. All this can be done without writing any code instructing the computer to identify the difference between papaya and watermelon. Only the data is to be given to the models and it will learn from the data itself.
Working of the supervised machine learning algorithm
Without proper and sufficient training, machine learning models are of no use. A machine learning model is trained with the help of a mathematical function which is capable of modifying the model until it predicts the correct output, in a continuous loop. The main element of training a machine learning model is to give it the correct data.
The more data you give, the more accurate the predictions you get. The data we gather is always raw. Lots of pre-processing needs to be done before feeding data to the model. This pre-processing includes data normalization, discretization, error correction, deduplication, replacing missing values, and more.
For example, data is collected from the alcohol manufacturing plant. It has three columns, namely, color, alcoholic volume, and whether it is beer or wine. The third column is known as the label. Based on the drink’s color and alcoholic volume, it is decided whether it is beer or wine. When relevant data is collected, it needs to be pre-processed as mentioned above to improve its quality.
The data must be balanced enough, meaning it should have approximately an equal number of beer and wine samples properly shuffled. The data is then split into 2 parts, one for training and another for testing. The training data must be more than the testing data. The testing data is basically used to test whether our system gives correct predictions after training or not.
Next, we choose an appropriate machine learning model and give the data as input to it. Each model is suitable for different applications. For example, some work well for image recognition, while some are capable of working with pure numerical data. The training data is input to the model. After successful training, testing is done and accuracy is calculated.
Working of neural network
Neural networks are both supervised and unsupervised machine learning algorithms. Neural networks are used where there is tons of data and lots of features given as input. A neural network is an interconnected network of neurons, mimicking the human brain. Input is given to the first layer of this network.
The input passes through several hidden layers and reaches the output layers. The output of each of the previous layer is given as the input of the next layer. Each layer is capable of recognizing one of the different features in the dataset. For Instance, if we want to recognize handwritten numbers 0 to 9, the first layer might measure the color of pixels in the input images, layer 1 might spot shapes, layer 2 might look for components of number like lines, curves, and more.
If you are interested in learning and implementing the working of various supervised machine learning algorithms and neural networks as mentioned above, with the help of real-life use cases, machine learning training in Hyderabad is the best option for you.