Machine Learning is disrupting the market rapidly. You already know, AI is affecting the way of Production whether for Software, Auto Mobile unit, or any other sector. I am no kidding, Each Industry must have some opening for Data Scientist and Machine Learning Engineer. It’s strong evidence for the Rising of AI and Machine Learning Era. Do you also want to be future-ready for Machine Learning? I mean! Are You looking to learn the Machine Learning algorithms? Reading this article will end up your search for Top Machine Learning Algorithms.
This article will give you an overview of the Top Machine Learning Algorithms. This article is designed in such a way that It best suits to Data Science beginners and Intermediary Readers. All you need to read it until the end. Its just an overview article, So I have put very simple language with lots of Machine Learning Examples. So do not skip the article Until you finish reading it.
Presently there are three types of Machine Learning Algorithms
- Supervised Machine Learning Algorithms
- Unsupervised Machine Learning Algorithms
- Reinforcement Machine Learning Algorithms
Supervised Machine Learning Algorithms
In supervised Machine Learning Algorithms, you have a given sample with some output(labeled) and you make a machine learning model from it. When the new inputs come to this built model, it predicts the pattern for the given inputs and easily finds the output. You can think it as you have already know the output for some data points and predict the output from the new data points.
Unsupervised Machine Learning Algorithms
Unsupervised Machine learning algorithms are just the opposite of the above. Here data points are not labeled. But in fact, all the data points are grouped into one category depending on some pattern. For example, you have two balls with a red and green color and you made a basket for each. Whenever you get the red ball you put it in the red ball basket and when you get the green ball, put in the green ball basket. This algorithm allows you to make complex data looks simpler for better analysis.
Reinforcement Machine Learning Algorithms
It differs from the two upper algorithms. In these algorithms, you take the decision for specific input and after learn how much correct the decision was and make a strategy for getting the best output. You are continuously in the learning phase and changing the strategy with time to get the best reward for the output.
Top Machine Learning Algorithms :
Under the Machine Learning umbrella, There is so many algorithms paradigm. I think you should learn topic wise algorithms.
Regression algorithms are one of the common types of task which we need to perform as a data scientist. See there are so many ways to perform regression algorithmically but we will put the important form of regression –
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression
2. Classification Algorithms –
- Logistic Regression
- Naive Bayes
- Support Vector Machine (SVM)
- K-Nearest Neighbors (K-NN)
- Random Forest Classification
- Decision Tree Classification
3. Clustering Algorithms–
- K-Means Clustering
- Hierarchical Clustering
4. Association Rule Learning–
- Apriori algorithm.
- Eclat algorithm.
5. Reinforcement Learning–
- Upper Confidence Bound
- Thompson Sampling.
Why Machine Learning Algorithms are needed? –
To understand the importance of Machine Learning Algorithms, You need not to think much Technically Just ask yourself about the best way of learning anything. Do you like learning by experiences or If I provide you some set of rules. Obviously you will say learning from past experiences enhances your understanding. The same approach we apply when training Models. Rather than writing the code in so many If else block, we use some probabilistic approach. This approach dynamically chooses the best path on the basis of the past data. Frankly speaking, there are some cases where you can never write the code in just if-else block. For example Email Spam classification. Just think about it. Here how much logic will you code for? Obviously you need algorithms which make a probabilistic decision.
Anyways Before You Proceed I will recommend giving you a quick look at What is Machine Learning. It will give you a warm start with the various Algorithms. Choosing the best Algorithms can increase the accuracy and reliability of your Model. At last but not least, Apart from all this, There is also an important corner. Guess what? Best Programming Language for Machine Learning.
How To Implement a Machine Learning Algorithm –
Once you go through the logic part of these algorithms, You must find the implementation needs too much code. But the reality is completely different from it. There are so many well-designed machine learning frameworks that make our life easy. All you need to understand their predefine library functions. You need to call them when required.
We have an amazing article on Top 5 Machine Learning Libraries. I will suggest you read this article as it gives deep insight on machine learning libraries. The beauty with these frameworks is uniform syntax throughout the code. I mean all preprocessing, evaluation, cross-validation have similar syntax throughout the framework. Even while using the different models you need not change much on codes.
Conclusion –
Choosing the Right Machine Learning Algorithm is tricky. In my Data Science journey, I found people are struggling in two steps –
- In choosing Right algorithms
- Deciding the correct EVALUATION MATRIX.
In the above article, we have tried to solve your first problem. The goal of this article is to make you aware of the types and names of learning algorithms. If you have another name that could add on this list, please suggest.
Thanks
Data Science Learner Team.
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