The Machine Learning
As the name suggests, Machine Learning or ML is the study in which a computer or a machine has the ability to perform it’s own task without it being explicitly programmed by a person. Machine Learning (ML) allows machines to learn in the same way as humans
ML is the subpart of Artificial Intelligence. Machine Learning learns from the training data or from self experiences. And has Deep Learning and Neural Networks, as its sub-portion which are often interchangeably used. 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, predict the output. Based on the predicted output, it improves accuracy by predicting again.
Who is a Machine Learning Engineer?
A Machine Learning Engineer is a programmer who builds machines and systems that can learn and react similarly to the human being. The goal of a Machine Learning Engineer is to achieve Artificial Intelligence.
A Machine Learning Engineer typically works with the following steps-
Data Collection.
Data Preprocessing.
Choose a Machine Learning Algorithm.
Training the Model.
Testing the Model.
Tuning the Model.
Machine Learning Engineers create a Machine Learning model that can work properly with the best performance. Machine Learning Engineers have to choose the right algorithms as per model compatibility and requirement.
They have to extract ideas from the data science team, choose appropriate tools and ecosystems, Use machine Learning frameworks, and stay up to date with the latest development.
What is the Salary of a Machine Learning Engineer?
According to Indeed, the average salary of Machine Learning Engineer is $142,858.67(US)
1. Entry-Level Machine Learning Engineers’ salary
Entry-level ML Engineers means someone with 0–4 year of experience, and who is college pass-out or someone who switch their job and landed into a Machine Learning Field.
So, the average salary (US)of an Entry-Level ML Engineer is $97,090 but after adding bonus and profit-sharing, the pay can become $130,000 or even more
2. Mid-Level Machine Learning Engineers’ salary
Mid-level Machine Learning Engineer means someone with 5–9 years of experience. So, according to the PayScale, the average salary (US) of a mid-level machine learning engineer is $112,095 and after adding bonus, the pay can become $153,000 or even more
What Qualification does ML Engineers posses?
Most of the Machine Learning Engineer jobs require a Master’s or Ph.D. in Computer Science, Information Science, Software engineering, Statistics, and other related fields. Machine Learning Engineer is not a Graduate level job.
It requires years of experience in data science and software engineering, as well as an advanced college degree.
Becoming a Machine Learning Engineer is easy if you are a Software Engineer and want to switch your job or you have completed your Master’s or Ph.D. in a related field.
So, these are the required qualifications for ML Engineers, but only having a degree is not enough. You should have some required skills in order to become a Machine Learning Engineer.
Skills Required for a Machine Learning Engineer
1. Programming-
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 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 -
Knowledge of Mathematics is very important in order to understand how machine learning and its algorithms work. In math, the most important topics are-
Probability and Statistics
Linear Algebra
Calculus
Now, let’s have a details 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-
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 is performed before you process the data. Data pre-processing steps are cleaning, parsing, correcting, and consolidating the data.
ETL (Extraction, Transformation, and Loading)- 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-
Some of the most common Machine Learning Algorithms are as follows
a. Supervised Learning Algorithms:-
Simple Linear Regression
Multiple Linear Regression
Non Linear Regression
Polynomial Regression
Decision Trees
Logical Regression
K-Nearest Neighbours(KNN)
Support Vector Machines
Naive Bayes
b. Unsupervised Learning Algorithms:-
K-means Clustering
Hierarchical Clustering
Density-based Clustering
Recommendation Systems
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-
Tensor Flow
Keras
Pytorch
Scikit Learn
Sources-
Thank You!
Know Your Author
Nithin Narla is a SQL Developer at Accenture, India. He likes to visualize data and create insightful stories.
