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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.
What is Machine Learning?
Machine learning is a discipline of artificial intelligence (AI) and computer science that concentrates on using data and algorithms to learn and mimic the human learning processes and progressively increase accuracy.
There are various ways that machine learning is distinct from standard programming. In traditional programming, an individual inputs data into a computer and creates a program to transform that data into the desired outcome. Computer programming is a human-centered process only as good as our ability to define and organize data.
The rapidly expanding discipline of data science includes machine learning as a key element. Algorithms are trained using statistical techniques to produce predictions or categorizations and to find significant findings in data mining projects. The decisions made due to these findings influence key growth indicators in applications and enterprises, ideally.
Why Use Machine Learning?
Machine learning promotes creativity and offers novel possibilities across multiple fields because it can analyze large volumes of data, detect trends, and generate accurate predictions.
The complexity of massive data is handled by machine learning algorithms, which also reveal hidden patterns and offer useful insights that were previously unthinkable. This allows companies to improve procedures, spot patterns, and acquire a competitive edge.
Automating time-consuming and repetitive operations with machine learning frees staff members to work on more challenging and innovative projects.
By combining machine learning, autonomous systems and robots improve security, productivity, and precision, opening the path for an era with smarter and automated machines.
Jobs in Machine Learning
Here are some of the most popular jobs for an individual with knowledge of machine learning:
Machine Learning Engineer
One of the most desired jobs in the machine learning field is machine learning engineer. These engineers construct intricate data sets, build and improve data pipelines, and create and apply machine learning models. Machine learning engineers create many digital platforms’ recommender systems.
Data Scientist
Data scientists employ statistical techniques and machine learning algorithms to gather insights from massive data and find solutions to challenging obstacles. They work on data collecting, cleaning, exploratory data analysis, and constructing predictive models. Additionally, feature selection, model assessment, and model deployment are tasks carried out by data scientists.
AI Engineer
As a subfield of AI, machine learning has a large community of AI Engineers with experience in its tools and applications. You may create and change machine learning models, use machine learning for image identification, and create apps for neural networks. One should learn about machine learning and how to become an AI engineer to build a successful career.
Deep Learning Engineers
Deep learning engineers are experts at developing and executing deep neural networks for applications like speech synthesis, image recognition, and natural language understanding. To create and train sophisticated deep learning models, they work using frameworks like TensorFlow and PyTorch.
NLP Scientist
A natural language processing scientist utilizes algorithms to identify natural language rules, which are applied to help machines learn and understand the language. This is made simpler by machine learning since you can create an algorithm that searches for and evaluates patterns.
Software Developers
Software developers may apply machine learning to build models that output data in the format users want to access, analyze data to forecast how users will respond to specific aspects of an application, and program chatbots to communicate with users more naturally.
How to Become a Machine Learning Engineer
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.
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