In this blog I have summarized about the term Machine Learning and all the algorithm used in real world applications. Anyone who wants to dive into the field of ML or just wants to get an overview about Machine Learning, this blog will help you.
Enjoy reading!
Machine Learning
Machine Learning (ML) is a discipline of Artificial Intelligence (AI) that provides machine the ability to automatically learn from data and past experiences to identify patterns and make predictions with minimal human interventions.
Machine Learning vs Artificial Intelligence
This is one of the most common question that comes to our mind whenever we get to hear something about ML. So in simple terms-
Artificial Intelligence is a discipline that focusses on creating machines that can perform tasks which require typical human intelligence. It involves development of algorithms and system that can reason, learn and make predictions based on input data.
On the other hand, Machine Learning is a sub-field of AI, that involves teaching machines to learn from data. ML algorithms can identify patterns and trends and use them to make predictions. ML is used to build predictive models, classify data and recognize patterns. ML is an essential tool for AI.
What is Deep Learning?
Talking about Machine Learning, we often come across with another familiar term 'Deep Learning'. Deep learning is a subset of machine learning that uses artificial neural networks to process and analyze information to mimic the learning process of the human brain.
Machine Learning Algorithms
There are different types of algorithms that are used to train the machines, so that they can predict the outputs for the new inputs. There are 2 types of Machine Learning algorithms that are used most in the real world applications. They are-
Supervised Learning
Unsupervised Learning
Supervised Learning
Supervised Machine Learning is an algorithm in which the machine is trained with inputs (X) and it's corresponding right outputs (Y). Then they can take a brand new input and produce the possible output.
The Supervised Learning algorithm further can be classified into 2 types -
Regression - This algorithm is used when we need the machine to predict an output (mostly a number) from infinitely many possible outputs.
For example - predicting the price of house etc.
Classification - This algorithm is used to predict categories i.e predicts an output from finite limited number of outputs.
For example - predicting whether the image is of a cat or not, predicting whether the student will pass or fail etc.
Unsupervised Learning
Unsupervised Learning is an algorithm where the machine is trained with inputs (X) without corresponding outputs (Y). The machine tries to find patterns and relationships in the data on its own. This type of learning is used for clustering and association problems, such as grouping similar items together or finding associations between items.
There are 3 types of unsupervised algorithms mostly used in real world applications-
Clustering - This algorithm is used to group similar data points together
For example - Clustering is used in Google News
Dimensionality reduction - This algorithm used to reduce the number of input variables in a dataset while preserving its essential information.
It is used in speech recognition, signal processing etc.
Anomaly detection - This algorithm is used to identify unusual patterns in the data points.
It is used in finance for fraud detection, used in manufacturing to identify defects etc.
There are two more advanced Machine Learning Algorithms also used in industries -
Recommender Systems - A recommendation system is a type of machine learning system that provides personalized recommendations to users based on their past behaviors, preferences, and patterns.
Reinforcement Learning - It is a Machine Learning technique that trains software to make decisions to obtain most optimal results. It is similar to the trial and error learning process humans use.
It is used to teach computer to play video games and can also be seen in ChatGPT as it uses Reinforcement Learning from human feedbacks.
Conclusion
Understanding machine learning and its diverse set of algorithms is crucial for anyone looking to explore the field of AI and data science and for tackling a wide range of problems. From linear regression to neural networks, each algorithm offers unique insights and solutions. Keep learning and experimenting to master these concepts. I hope this overview has provided a solid foundation for your journey into machine learning. Happy learning!
If you have any questions or need further clarification on any of the topics discussed, feel free to leave a comment below or reach out to me directly. Let's learn and grow together!
To further explore the world of machine learning, here are some recommended resources:
Coursera: Machine Learning by Andrew Ng-https://www.coursera.org/learn/machine-learning
Towards Data Science - https://towardsdatascience.com/