Today we have access to many offline and online resources that provide machine learning certification programs. But, we often get confused about how to start the journey to become a machine learning expert. However, for your relief, many experts in the field have shared effective tactics to help beginners reach their ultimate career goal in the ML space.
This article shares some of those tactics to assist you in gaining expertise in machine learning technology. Let’s explore:
Get the hold onto the basics
First things first, anyone who wants to learn machine learning deeply must focus on grasping its basics. In fact, the basics of any subject make you understand the finer details enabling you to learn the concepts quickly. Here is a list of fundamental concepts that you can start with:
- Analytics
- Data science
- Big data
- AI
Additionally, also ensure to explore machine learning online courses that teach real-world applications and use cases of these domains..
Gain experience in statistics
Do not worry; you don’t have to become a master in statistics to learn machine learning. But, there are indeed a few concepts that you must have a firm hold on and know when and where you can use them. Here are a few topics that we suggest you consider:
- Sampling
- Principles and fundamentals of probability
- Data structures, summaries, and variables
- Logistic regression
- Random variable distributions
Learn programming languages ( especially R or Python)
Mastering programming languages will help you to sharpen your machine learning technology expertise. We advise you to gain comfort in working with Python or R languages to conduct a few ML-related tasks. However, you can also choose both. You can perform any of these tasks:
- Data reading, importing, and exporting
- Quality analysis of data
- Data cleansing
- Data filtering, sorting, and other data manipulation activities
Participate in an exploratory project on Data Analysis
An exploratory data project refers to the analysis of data to uncover the events and story behind it. And, further, you can share the information with the concerned party. You might find machine learning certification programs that provide students to conduct these types of data analysis during the course. Here are project ideas that you can cover:
- Explorations of a single and multivariable
- Data visualization and storytelling using Tableau
Prepare unsupervised ML models
You can take up concepts like K-means clustering using three distinct datasets across different interest areas. By working on these types of unsupervised ML algorithms, you will master building ML models for different needs. In fact, there is a wide range of supervised and unsupervised learning algorithms that you must try out working with. For this, you can join up available machine learning online courses that focus on training learners to understand and conduct tasks using all kinds of algorithms in the ML space.
Learn technologies regarding Big Data
Knowing about data architecture and engineering is a separate space of specialization. But, every professional machine learning expert should know how to work with big data frameworks.
It is because it helps experts in carrying out proper data storage, access, and processing them effectively. Further, it enables you to come up with efficient solutions that you can practically execute. Hence we can say that when you decide to learn machine learning, big data becomes an important part of this journey. Here’s what you should learn in big data space:
- Ecosystem and overview of big data
- Hadoop frameworks including HDFS, Pig, MapReduce, and Hive
Get familiar with handling deep learning models
Deep learning models are actively in use across big business giants like Google and Apple to provide innovative solutions like Google Assistant and Siri. Moreover, many big automobile manufacturing companies are leveraging the technology to create self-driving cars and whatnot.
So, to stand out as a candidate, learn the workings of deep learning models aside from machine learning technology.
Although machine and deep learning models can do exceptional tasks with their algorithms, start with something simpler. We advise you to begin with building a model that can recognize objects within a picture. For example, you can build a model that can effectively differentiate between boxes and circles.
You can train the algorithm to identify your different family members for a more advanced level. Further, you can upgrade your tasks by introducing machine learning algorithms with wider data. So, while going for deep learning topics and concepts, we advise you to prioritize the following:
- Natural language processing and artificial neural networks
- TensorFlow
- Convolutional Neural Networks
- Open CV
Take part in a data project to build influential expertise
If you follow all the points mentioned above, then while you reach here, you’ll already become a pro machine learning expert. So, now all you have to focus on is presenting your knowledge in front of potential employers. Just by a few quick web searches, you’ll find a wide range of data projects that can drive your passion for machine learning technology.
Complete the projects and add them to your portfolio to showcase your skills. Here are a few ML-based sections that you can cover through your projects:
- Data collection, cleansing, building, and quality check
- Selection and creation of the model
- Preparation of comprehensive project report
Conclusion
Machine learning is a domain that is thriving in the present and has a potentially bright future ahead. In fact, the field is continuously evolving with new advancements, and experts need to keep up with these shifts to stay relevant. Though it looks a lot on the plate, its ever-evolving nature also makes the domain thrilling and interesting for techies. Due to the high demand for ML experts, there’s also a rise in machine learning certification programs. So, if you also want to develop expertise in machine learning technology and pursue a career in this field, then this is the right time.
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