Machine learning has helped organizations improve customer relationships and gain a competitive edge across industries. Companies are investing in machine learning solutions to improve manufacturing processes and innovate online operations. Smart algorithms continue to transform processes everywhere, from email to marketing campaigns. And companies are hiring certified machine learning engineers and practitioners who can integrate machine learning in business operations for efficient solutions. With the explosion in machine learning hiring, students and working professionals are upskilling to be on top of the career ladder. If you want to take advantage of this career opportunity, gear up to learn the skills with a machine learning course in Chennai.
What is Machine Learning, and why you Should Learn it?
Today, technologies such as machine learning have disrupted our lives in most aspects, and we cannot imagine a world without them. Whether it is smart virtual assistants, movie recommendation engines, surge pricing at Uber, or self-driving cars, these applications of machine learning have radically transformed our lives. Online fraud detection, real-time customer support or chatbots, email spam and malware filtering, traffic alerts, face recognition, and search engine result refining, are some other applications of machine learning used to bring about efficiencies in processes.
Almost every industry vertical is applying AI technologies, including machine learning. It is opening up a slew of opportunities in cutting-edge machine learning applications such as image recognition or medicine. Most of the hiring is by top tech companies looking at experts who can build machine learning algorithms to solve various business problems. While the number of machine learning jobs is on the rise, as any job portal will show you, organizations are faced with the challenge of hiring people with the right certifications and machine learning skills. The shortage of machine learning professionals has created a gap between the demand and the supply, and experienced engineers can expect high salaries.
A machine learning engineer often doubles up as a data scientist for a better synthesis of data and big data analytical tools. With data science playing a role in every large enterprise and IT company, it means that a qualified machine learning engineer can have a bright career prospect with the dual job role as a data scientist. Choice of a machine learning engineer role or a data scientist role; what more can a machine learning grad expect?
Top 10 machine learning interview questions to expect
To crack a machine learning interview, you must be thorough in theoretical knowledge, including the fundamental data structures and algorithms. You will most likely be asked about basic machine learning concepts, your project, and asked to solve a machine learning problem or a coding question.
To begin with, prepare for your theoretical segment with some of these sample questions:
1. Differentiate between machine learning and data mining.
Machine learning refers to the study, design, and development of algorithms, that can learn iteratively without being explicitly programmed. Data mining is the process of extracting unknown patterns from large datasets, often using machine learning algorithms.
2. What do you mean by overfitting?
In machine learning, when a statistical model is very complex and models the training data too well, it is overfitting. Overfitting makes the model learn the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.
3. How to ensure your model is not overfitting?
Overfitting usually occurs when using a small dataset. It can be avoided by using a large amount of training data or reducing the noise with fewer variables and parameters. Another way when building a model based on a small database is to use cross-validation methods such as K-folds or regularization techniques such as LASSO.
4. What are the different types of algorithm methods in machine learning?
- Supervised Learning
- Semi-supervised Learning
- Unsupervised Learning
- Reinforcement Learning
5. What is dimension reduction in machine learning?
It is the technique of reducing the number of input variables in the training data or feature set by bringing down the number of columns either by combining columns or removing extra variables.
6. What do you understand by the Reinforcement Learning technique?
It is an algorithm technique used in Machine Learning that used rewards for desired behavior or punishing for errors. Reinforcement learning is used by software and machines to discover the most suitable behavior or path in a given situation. Reinforcement Learning learns by decoding its environment, taking actions on it, and learning through trial and error, reward or penalty for every action it performs.
7. What are the three stages of building the hypotheses or model in machine learning?
- Model building to select an algorithm suitable for the model and training it according to the requirements of the problem.
- Applying the model by checking the accuracy of the model through test data.
- Model testing by performing the required changes after testing, and then applying the final model.
8. What should you do when your model is suffering from low bias and high variance?
When the model’s predicted value is close to the actual value, it is known as low bias. When the model is suffering from low bias, we can use bagging algorithms like random forest regressor.
9. Describe Training Set and Training Test.
The “training data set” refers to the sample data used to create the model for the learning process and is used to fit the parameters. While the “training test” is the validation used to qualify performance and test the accuracy of the hypotheses generated by the learning.
10. What are the common ways to handle missing data in a dataset?
There are several ways to handle missing values. Such as deleting the rows with missing values, replacing missing values with the most frequent value such as mean/median/mode, imputing missing values for a continuous variable, imputing missing values for categorical variable, assigning a unique category, using algorithms that support missing values, or developing a model for prediction of missing values
The job market for machine learning engineers has opened up to IT professionals who are certified in machine learning and experienced in use cases that demand AI technologies. So are you ready to learn machine learning and land your dream job at a top organization?