The technical world is afire with the possibilities offered by Artificial Intelligence. The promise of automating every mundane part of our lives (including driving) is too tempting for scientists, visionaries, and futurists to resist. And these days, the AI-related field of Machine Learning is gaining in popularity.
The International Data Corporation (IDC) predicted that spending on AI & ML will grow by 5x from $12 billion in 2017 to $57.6 billion by 2021. The technology and the finance industries will take the biggest slice of the cake. 64% and 52% of the companies belonging respectively to these industries will have adopted machine learning processes in the future.
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At present, the demand for machine learning experts is constantly rising as this graph clearly illustrates:
Source: Indeed.com | Credits: Ann Saphir, Data Visualization Engineer, Reuters
At the heart of it, machine stems from one question: how can we program this system to automatically improve and learn with experience? Learn here refers to the act of drawing conclusions from data and making intelligent decisions. Machine learning develops algorithms for this that glean knowledge from specific data and experience, based on statistical and computational principles.
The above paragraph would have indicated how challenging machine learning would be. It is, but it is also learnable. If you are ready to become a machine learning engineer now without waiting for a traditional university to validate your knowledge, follow & repeat the 7 steps given below, read the requirements mentioned below –
Step 1: Level up your Python & Software skills
A high-level, easy-to-use language, Python is the language of choice for AI specialists, data scientists, and machine learning engineers.
Python’s syntax is easy to learn, and it has tonnes of already built-in libraries. You’ll need to watch out for the whitespaces, though, since they can mess with the execution of the code. It also includes support for all types of programming paradigms like functional programming and object-oriented programming.
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Another important thing to get super familiar with is Github. You’ll be working in a team to build code for time-sensitive applications. Get into the habit of writing thorough unit tests for your code using frameworks such as the nose. Test your APIs using tools like Postman.
Read some books or articles to get an idea of the tools you’ll need to run Python on datasets.
Step 2: Look into machine learning algorithms
After you are familiar & comfortable with Python, you can start looking at machine learning algorithms. Be sure to read up on the theory related to each algorithm so you can implement models with ease.
A Tour of the Top Ten Algorithms for Machine Learning Newbies will help to bring you up to date. Remember that no 1 algorithm will be the perfect solution. You’ll need to implement a variety of them. Hence, study each one thoroughly.
upGrad’s course ‘Masters in Data Science’ will help you get a head start on marrying Python with Data Science through tools like Panda, NumPy etc.
Step 3: Work on mini projects
Now that your initiation into the realms of Python and machine learning is complete (both individually and combinedly), it’s time to take all that knowledge and start implementing it in projects.
You can check out these Kaggle Datasets to start off with your first machine learning projects. The above snapshot is from the (free public) dataset offered by Inside Airbnb which provides Airbnb listings in different cities around the globe.
Step 4: Take things to the next level with Hadoop and Spark
Hadoop and Spark are the 2 systems you’ll want to tackle after you’ve built some proficiency in working with data sets using Python. These big data frameworks will enable you to work with data at the terabyte and petabyte scale.
The Spark Jupyter notebooks hosted on Databricks offers a tutorial-level introduction to the framework and also gives you practice with coding.
Step 5: Move onto TensorFlow
Machine learning algorithms? Check. Big data frameworks? Check. Advanced machine learning? Start working with TensorFlow.
You can take the TensorFlow and Deep Learning without a Ph.D. course by Google with educates the student about the theoretical and practical aspects. You can also benefit from upGrad’s PG Certification in Machine Learning & Deep Learning at this point.
Step 6: Go Big
After working with all the building blocks, it’s time now to wrestle with big data sets and apply all the knowledge you’ve gained in the previous 5 steps.
Refer to the Ways to Handle Data Files for Machine Learning to learn how to handle large datasets (theoretically). Then implement the gained knowledge using Publicly Available Data Sets.
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Step 7: Keep on practicing and growing
The final step is to simply practice and repeat the above mentioned 6 steps. You are now at a point where you can build your own machine learning models. It’s time to refine those skills now and keep getting better.
If a job is your shining pot of gold at the end of the rainbow, then you can gear up for an interview by going through Must-know Machine Learning Questions – Logistic Regression.
The above highly practical steps will ensure that you learn how to become a machine learning engineer in the least possible amount of time and still master all the required skills. The only thing required. Consistency and regular practice. Armed with these 2 traits, there is no reason why your desire to be a machine learning engineer will not be fulfilled.
Time to welcome a new era of technology with you as a harbinger of it.
Who can study Machine Learning?
Machine Learning is spearheading technology with Artificial Intelligence. A career in Machine Learning is challenging and rewarding. Machine Learning is a complex field that requires knowledge in data science, programming, Deep Learning tools, etc. The list could keep growing as the field evolves. The advantage is that you can find a wide variety of courses in Data Science, Machine Learning, Software engineering, etc. online. A Bachelor’s in Computer Science, Statistics, or Mathematics is preferred for a career in Machine Learning.
Is Machine Learning an evergreen field?
Machine Learning has evolved rapidly in the past few years and is expected to grow much more in the upcoming decades. Machine Learning has utilised various tools resulting in global applications from AI to branching into a sub-field. The global Machine Learning market is predicted to have a whopping 30.6 billion dollars worth by 2024. Machine Learning has integrated with AI to form Models, Computer Vision, Robotics, Audio, and Video Recognition and solidify its impact in the technology industry. It is not limited to computer applications anymore; it extends to other areas in the industry to assist their development.
What careers can you go into after Machine Learning?
Machine Learning careers will be dynamic and futuristic, offering challenge, recognition, and stability. The scope of Machine Learning extends from the technology industry and integrates with other areas. A background in ML is not necessary to become a Machine Learning Professional anymore. In-depth knowledge of software, Data Science, technical and soft skills, etc., are the base requirements to start your career in ML. An ML Engineer focuses more on programming languages, while a Data scientist predicts profitable solutions by analysing data. Even though Machine Learning can have multiple careers, they all use the fundamentals of Machine Learning, Data Science, analytics, and NLP.