6 Skills Required for Machine Learning and Artificial Intelligence

So, You are Planning to become a Machine Learning Engineer?. First of all, Congratulations!. You have chosen a profitable, secure, and most demanding career. But if you want to know the skills required for machine learning and artificial intelligence, then give your few minutes to this article.

Skills Required for Machine Learning and Artificial Intelligence

Before moving to the Skills Required for Machine Learning and Artificial Intelligence, I would like to discuss What is Machine Learning?

What is Machine Learning?

As the name suggests ”Machine Learning“. That means Machines are Learning something.

Right?

Machine Learning (ML) allows machines to learn in the same way a human learns.

ML is the subpart of Artificial Intelligence. Machine Learning learns from the training data or from self-experiences.

ML is the same as a Newborn child. The newborn child learns from the instructions given by his parent and by his self-experience. He tries to walk but he falls down. And then again tries to walk. Similarly Machine Learning Works.

ML learns from training data and predicts the output. Based on the predicted output, it improves accuracy by predicting again.

I hope now you Understood What is Machine Learning. Now let's move to the skills required for machine learning and artificial intelligence-

Skills Required for Machine Learning Engineer

So these are some must-have skills for machine learning engineer-

1. Programming Language

Knowledge of Programming language is compulsory for machine learning. For Machine Learning, the most popular programming languages are Python, R, Java, and C++. As a beginner, you can start with Python, but sometimes Python is not enough for machine learning tasks. That’s why you should have knowledge of all these programming languages.

But Python and R are the most wanted languages for machine learning engineers.

R Programming language is good for statistical operations whereas to implement mappers and reducers in Hadoop, you should be familiar with Java.

Along with that, You should have a good understanding of Classes, Data Structure, algorithms, and memory management.

2. Mathematics Skill
Knowledge of Mathematics is very important in order to understand how machine learning and its algorithms work. In math, the most important topics are-

1. Probability and Statistics
2. Linear Algebra
3. Calculus

Now, let’s have a detailed look at all of them-

a). Probability and Statistics
Probability and statistics are used in- Bayes’ Theorem, Probability Distribution, Sampling, and Hypothesis Testing.

b). Linear Algebra
Linear Algebra has two important terms- Matrices and Vectors. They both used widely in Machine Learning. Matrices are used in Image Recognition.

c). Calculus
In Calculus, you have Differential Calculus and Integral Calculus. These terms help you to determine the probability of events. For example, finding the posterior probability in the Naive Bayes model.

3. Data Engineering Skills
For building a machine learning model, you need data for training and testing. That’s why knowledge of data engineering is important. Data Engineering contains 3 basic steps-

• Data pre-processing- Data pre-processing step is performed before you process the data. Data pre-processing steps are cleaning, parsing, correcting, and consolidating the data.

• ETL (Extract, Transform, and Load)- In this step, you need to perform extraction of data from the internet or local server, then transform the data into a suitable format, and after that load the data into your program. That’s why you should have knowledge of ETL so that you can perform these steps easily.

• Knowledge of Database- You should be familiar with DBMS like SQL, Oracle Database, and No SQL.

4. Machine Learning Algorithms
You should have knowledge of Machine Learning Algorithms like-

1. Supervised Learning Algorithms
2. Logistic Regression.
3. K-Nearest Neighbors(K-NN)
4. Support Vector Machine(SVM)
5. Kernel SVM.
6. Naive Bayes
7. Decision Tree Classification.
8. Random Forest Classification
9. Unsupervised Learning Algorithms
10. K-Means Clustering
11. Hierarchical Clustering.
12. Probabilistic Clustering
13. Reinforcement Learning Algorithms
14. Policy Optimization.
15. Q-Learning
16. Learn the Model
17. Given the Model.

5. Machine Learning Frameworks
Machine Learning Frameworks make the life of developers and machine learning engineers a whole lot easier. ML Frameworks remove the complex part of machine learning and make it available for everyone who wants to use it.

These are some widely used Machine Learning Frameworks-

1. TensorFlow.
2. Theano.
3. scikit learn.
4. PyTorch.
5. Keras.
6. DL4J.
7. Caffe.
8. Microsoft Cognitive Toolkit.

6. Deep Learning Algorithms
Deep learning is the subpart of machine learning. And it is much more powerful than machine learning. Deep learning is getting more interesting nowadays. That’s why you should be familiar with Deep Learning Algorithms.

The most used Deep Learning Algorithms are-

1. Feedforward Neural Network.
2. Backpropagation.
3. Convolutional Neural Network.
4. Recurrent Neural Network.
5. Generative Adversarial Networks (GAN).

So, these are 6 Skills Required for Machine Learning and Artificial Intelligence.

If you have any doubt or questions, feel free to ask me in the comment section.

Comments

[Irena Grigor]

Great list of skills! Machine learning and artificial intelligence are the future and developing these skills opens up many opportunities.