Explain the Steps in Machine Learning.

There are several steps in Machine Learning which are must for each project.

Gathering Data:

To collect data that can help us to solve our problem. For example, if you want to predict the prices of the houses, we need an appropriate dataset that contains all the information about past house sales and then form a tabular structure.

We can collect data using web scraping, using surveys, from public domains, also particular type o data from organizations.

For this we can use libraries like BeautifulSoap, Scrapy, Regular expression, and some scraping tools etc.

Preparing that data :

Once we have the data, we need to bring it in proper format and preprocess it. There are various steps involved in pre-processing such as data cleaning, Fill missing values, drop non effective columns, visualizing data for better understanding.

For this we can use numpy, pandas,SciPy, Keras,  PyTorch,TensorFlow, matplotlib, Seaborn, Bokesh, Plotly, etc.

Choosing a model:

A model is the output of a machine learning algorithm run on data. In simple terms when we implement the algorithm on all our data, we get an output which contains all the rules, numbers, and any other algorithm-specific data structures required to make predictions. For example, after implementing Linear Regression on our data we get an equation of the best fit line and this equation is termed as a model.The next step is usually training the model incase we don’t want to tune hyperparameters and select the default ones.

Libraries for providing implemented models like Scikit-learn, TensorFlow, PyTorch, Keras, NLTK, etc.

Hyperparameter Tuning:

A hyperparameter is a parameter whose value is set before the learning process begins.

Hyperparameters are crucial as they control the overall behaviour of a machine learning model. The ultimate goal is to find an optimal combination of hyperparameters that gives us the best results.

Hyperparameter tuning methods like Grid search, Random Search, Bayesian optimization.

Evaluation:

How can you know if the model is performing good or bad.What better way than testing the model on some data. This data is known as testing data and it must not be a subset of the data(training data) on which we trained the algorithm. The objective of training the model is not for it to learn all the values in the training dataset but to identify the underlying pattern in data and based on that make predictions on data it has never seen before. There are various evaluation methods such as K-fold cross-validation and many more.

Prediction:

Now that our model has performed well on the testing set as well, we can use it in real-world and hope it is going to perform well on real-world data.