These 3 machine learning algorithms will widen the career prospects of young coders

Machine learning is the field of study concerned with the design and development of algorithms and techniques that allow computers to learn. There are multiple machine learning algorithms, each different from the other and serving a specific purpose. Starting from classifying a category and predicting a continuous target or analyzing a time series target, such algorithms help in producing great results. However, they all depend on the factors at hand: The problem that is being solved, the data that is there and the resources available. Here is a list of 3 such algorithms that might help aspiring data scientists to advance their job prospects. 

LSTM (Long short-term memory) 
LSTM or long short-term memory is one of the most popular and traditional tree-based algorithms, used for predictive analysis. LSTM is mainly used for classification and time-series based problems. They are also considered essential in the field of deep learning in order to classify, process, and predict based on a series of data. Though it is considered to be a bit complicated, this algorithm is highly effective and powerful in its predictions.  

XGBoost  
This is also a popular tree-based algorithm that falls in between decision trees algorithm (algorithm of probability) and random forest algorithm (a supervised learning algorithm that helps mainly in classification). XGBoost is used by tech companies in their machine learning department. In fact, among all the popular machine learning algorithms, XGBoost has the maximum documentation. It has a high-performance rate, and is one of the most accurate and competitive out of all machine learning algorithms. The best thing about XGBoost is that it is suitable for both beginners and experts. Apart from machine learning, XGBoost is also used to solve data science problems rapidly and with great precision. 

CatBoost  
CatBoost is the latest and one of the most superior tree-based algorithms. It is widely used for machine learning, coding and programming as well as data science. It is also used for search and recommendations, as well as in weather predictions. CatBoost utilizes all the pros of XGBoost, but it is faster and more user-friendly with an accuracy rate of almost 99 per cent. It can deal with categorical features really well, and is also highly efficient.