How Long Does It Take To Become A Machine Learning Engineer

By Author: Lokesh Kumar
Total Articles: 5
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I am listing down some important points which would help the readers to estimate the time required to master in it. Becoming an expert in any particular field requires considerable investment of time and perseverance. It would also benefit the readers a lot by understanding where they stand with respect to machine learning skill currently, so that the path is clearer.

The following 7 steps can help in your journey to coming close the becoming an expert in machine learning

1. Understand the basics of machine learning(ML)

2. Learning the statistics related to machine learning(ML)

3. Learn either Python or R for data analysis

4. Complete an exploratory analysis of a project

5. Create supervised learning models

6. Create unsupervised learning models

7. Exploring deep learning models

Alternately, you can opt some of the online/classroom training institutes which can help you in bringing about the discipline required to go through the above steps.

I am trying to explain the above steps and the things which needs to be covered. This information will help you ...
... get some clarity about the time needed by each and every individual. On a personal note, I would recommend 8-11 months to cover these topics in depth.

You need to spare some time to make yourself aware about the field of machine learning. You may already have ideas and some sort of understanding about what the field is, but if you want to become an expert, you need to understand the finer details to a point where you can explain it in simple terms to just about anyone. Understanding of the below points can help.

• What is Analytics?

• What is Data Science?

• What is Big Data?

• What is Machine Learning?

• What is Artificial Intelligence?

• How are the above domains different from each other and related to each other?

• How are all of the above domains being applied in the real world?

Don't ignore the statistical concepts while trying to understand machine learning. The below concepts in statistics would become very helpful when you try to understand the theory behind machine learning techniques.

• Data structures, variables and summaries

• Sampling

• The basic principles of probability

• Distributions of random variables

• Inference for numerical and categorical data

• Linear, multiple and logistic regression

Programming is very easier to learn, more fun in case you have some background in coding. While mastering a programming language could be an eternal quest, at this stage, you need to get familiar with the process of learning a language and that is not too difficult.

Python and R are very popular and mastering one can make it quite easy to learn the other. One can start with Python as it is much more in demand and than gradually progress on to add more tools in their arsenal.

Suggested topics to master in programming world could be

• Supported data structures

• Read, import or export data

• Data quality analysis

• Data cleaning and preparation

• Data manipulation

• Data visualization

exploratory data analysis is about studying data to understand the story that is hidden beneath it, and then sharing the story with everyone. Topics to cover in exploratory data analysis could be but not limited to

• Single variable explorations

• Pair-wise and multi-variable explorations

• Visualization, dashboard and storytelling in Tableau

Create unsupervised learning models

Below topics could be a good starting point

• K-means clustering

• Association rules

Create supervised learning models

Below topics could be a good starting point

• Logistic regression

• Classification trees

• Ensemble models like Bagging and Random Forest

• Supervised Vector Machines

Data engineering and architecture is a field of specialization in itself, but every machine learning expert must know how to deal with big data systems, irrespective of their specialization within the industry.

Understanding how large amounts of data can be stored, accessed and processed efficiently is important to be able to create solutions that can be implemented in practice and are not just theoretical exercises. Topics to cover could include

• Big data overview and eco-system

• Hadoop – HDFS, Map Reduce, Pig and Hive

• Spark

Machines are able to see, listen, read, write and speak thanks to deep learning models that are going to transform the world in many ways, including significantly changing the skills required for people to be useful to organizations.

Getting involved the exercises like with creating a model that can tell the image of a flower from a fruit will certainly help you start seeing the path to getting there.

Topics to cover:

• Artificial Neural Networks

• Natural Language Processing

• Convolutional Neural Networks

• TensorFlow

• Open CV

Undertake and Complete a Data Project

After completing the above steps, any learner should almost ready to unleash oneself to the world as a machine learning professional, but you need to showcase all that you have learned before anyone else will be willing to agree with you. You might like to create a Github repository which could be a good placeholder to assemble all the work done in the area of machine learning/data science

The internet presents glorious opportunities to find such projects. If you have been diligent about the previous eight steps, chances are that you would already know how to find a project that will excite you, be useful to someone, as well as help demonstrate your knowledge and skills.

Topics could include

• Data collection, quality check, cleaning and preparation

• Exploratory data analysis

• Model creation and selection

• Project report

You can find more detailed information on certificate course in machine learning

Thanks for reading..