A baby learns to crawl, walk, and then run. We are in the crawling stage when it comes to applying Machine Learning.

Dave Waters

Indeed, we’re in the crawling stage. There’s so much to explore in this wide field of Machine Learning and to adopt it in our daily lives.

But what exactly is Machine Learning?

Machine Learning (ML) refers to the group of techniques used by Data Science professionals that allows machines/systems to learn from data.

What does a Machine Learning engineer do?

ML engineers develop algorithms that help machines to identify patterns in their data and to self-train for comprehending commands. Models used by Data Scientists are fed with data by the help of ML engineers.

Here are the essential Machine Learning algorithms based on my research (to help you cross any hurdle in your ML career path):

  1. Decision Tree Algorithms
  2. Deep Learning
  3. Clustering Algorithms
  4. Logistic Regression
  5. Support Vector Machines
  6. Ensemble Learning
Let’s dive deeper into these algorithms one by one.

1. Decision Tree Algorithms

It’s a widely used Supervised Machine Learning Algorithm in which data is split continuously according to a specific parameter.

It is used for both classification and regression analysis. Decision trees algorithm is used in Statistics, Data Mining, and Machine Learning.

ONLINE SHOPPING DECISION TREE

Decision Trees Types:

  • Categorical Variable
  • Continuous Variable
Recommended Book- Decision Trees and Random Forests by Chris Smith

2. Deep Learning (DL)

DL is based on Artificial Neural Networks (ANN) with representation learning. The word ‘Deep’ in Deep Learning suggests the presence of multiple layers in the network.

Deep Learning Architectures has applications in Natural Language Processing, Speech Recognition, Computer Vision, etc.

Neural Networks types in Deep Learning:

  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Artificial Neural Networks
Recommended book- Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

3. Clustering Algorithms

These algorithms compute the similarity among all pairs of samples. It comes in two variants – class (to recognize the clusters) and function (to return an array corresponding to each cluster)

Clustering Types are based on the following categories:

  • Centroid-based
  • Density-based
  • Hierarchical
  • Distribution based
Recommended Book- Data Clustering: Algorithms and Applications by Charu C. Aggarwal, Chandan K. Reddy

4. Logistic Regression

Logistic Regression is used for parameter estimation of the logistic model in regression analysis.

By estimating probabilities, it can be used to measure the relation between dependent and independent variables.

This model is analogous to Linear Regression but it has quite different assumptions.

Regression Models Types:

  • Binary Logistic
  • Multinomial Logistic
Recommended Book- Applied logistic regression by David W. Hosmer

5. Support Vector Machines

It’s a Supervised Machine Learning algorithm, invented by Vladimir N. Vapnik and Alexey Ya. Chervonenkis is used in the case of classification and/or regression analysis and offers solutions for both problems.

This model is represented with data items as points in space which are mapped so that these points are divided by a wide gap. In the same space, new points are mapped and forecasted to reside in a category that’s based on the side of that gap.

Support vector machine

Real-World Applications:

  • Image Classification
  • Text and Hypertext Categorization
  • Synthetic-aperture Radar (SAR) Classification
  • Biological Science
Recommended Book- Evolutionary Machine Learning Techniques by Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah

6. Ensemble Learning

This method provides better predictive performance as compared to other learning algorithms. The Ensemble is a Supervised Machine Learning algorithm as it can be used for training and prediction purposes.

These ensembles can show more flexibility which in turn can allow them to over-fit the training data. Some ensemble techniques can be used to minimize over-fitting.

The following are the commonly used Ensembles Types:

  • Bayes Optimal Classifier
  • Bagging
  • Boosting
  • Bayesian Model Averaging
  • Stacking

According to a study by Springer, bagging-style algorithms show poor performance relative to the boosting-style algorithm (in terms of predictive accuracy).

Recommended Book- Ensemble Methods: Foundations and Algorithms  by Zhi-Hua Zhou

Conclusion

Having a good grasp of these concepts will help you step up in your Machine Learning Career Path.

Best Wishes for your Future!

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