Are you looking to enhance your skillset by delving into
machine learning algorithms? If so, you’ve come to the right place! Data
Science has become an integral part of the tech industry and understanding the
different algorithms is essential for mastering the art. In this article, we’ll
discuss what Machine Learning is, types of algorithms, how to tune models,
hyperparameters, choosing the right algorithm and more.
What is Machine Learning? It’s a branch of Artificial
Intelligence that enables computers to learn from data rather than relying on
explicit programming — making machines smarter. Essentially, it’s all about
prediction – predicting outcomes based on patterns in the data.
There are broadly two types of algorithms used in machine
learning – supervised and unsupervised learning. In supervised learning,
algorithms use labeled data sets to learn from while in unsupervised learning
algorithms make decisions without any human input or labels.
Data Science requires one to model real-world situations
using predictive analytics and make decisions based on those outcomes. To do
so, one needs to understand how ML models are built, trained and tuned for
optimal performance. Training and tuning models involve optimizing a set of
parameters called hyperparameters which affect the model's behavior. These need
to be optimized through cycles of experimentation known as “hyperparameter
optimization” in order to find an optimal combination that leads to an accurate
prediction result.
Choosing the right algorithm can be tricky as there are many
options available like linear regression or random forest classifier etc each
having their own pros & cons depending on usage scenarios &
optimization goals. Additionally, there is also a risk of overfitting or
underfitting which can create issues with accuracy & performance.
Source: Best Data Science Course in Pune
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