28 APRIL, 2021
Data Scientists, Artificial Intelligence Engineers, Machine Learning Engineers and Data Analysts are nowadays some of the top in-demand roles in the Tech Industry, making Artificial Intelligence among the most popular sectors in the world.
If you aspire to apply for these types of jobs, knowing what interview questions you might be asked is essential - that way, you can craft your answers in advance and feel confident in your responses.
Our team has been working very closely with hiring managers around the globe, gaining a in-depth insight of what companies are really looking for when they hire. After years of work and research, we have been able to identify some of the must-know Q&A to make sure your next interview in Machine Learning and Data Science is a success!
But remember! Even if the most important part of preparing for an interview is practice, it's also essential to set a succesful mindset.
Artificial Intelligence (AI) is the domain of producing intelligent machines.
ML refers to systems that can assimilate from experience (training data) and Deep Learning (DL) states to systems that learn from experience on large data sets.
ML can be considered as a subset of AI.
Deep Learning (DL) is ML but useful to large data sets.In summary, DL is a subset of ML & both are subsets of AI.
Additional Information: ASR (Automatic Speech Recognition) & NLP (Natural Language Processing) fall under AI and overlay with ML & DL as ML is often utilised for NLP and ASR tasks.
Machine Learning involves algorithms that learn from patterns of data and then apply it to decision making.
Deep Learning, on the other hand, is able to learn through processing data on its own and is quite similar to the human brain where it identifies something, analyses it, and makes a decision.
The key differences are as follows:
The manner in which data is presented to the system.
Machine learning algorithms always require structured data and deep learning networks rely on layers of artificial neural networks.
Supervised learning technique needs labeled data to train the model.
For example, to solve a classification problem (a supervised learning task), you need to have label data to train the model and to classify the data into your labeled groups. Unsupervised learning does not need any labelled dataset.
Machine Learning algorithms can be primarily classified depending on the presence/absence of target variables.
A. Supervised learning: [Target is present] The machine learns using labelled data. The model is trained on an existing data set before it starts making decisions with the new data. The target variable is continuous: Linear Regression, polynomial Regression, quadratic Regression. The target variable is categorical: Logistic regression, Naive Bayes, KNN, SVM, Decision Tree, Gradient Boosting, ADA boosting, Bagging, Random forest etc.
B. Unsupervised learning: [Target is absent] The machine is trained on unlabelled data and without any proper guidance. It automatically infers patterns and relationships in the data by creating clusters. The model learns through observations and deduced structures in the data.Principal component Analysis, Factor analysis, Singular Value Decomposition etc.
C. Reinforcement Learning:The model learns through a trial and error method. This kind of learning involves an agent that will interact with the environment to create actions and then discover errors or rewards of that action.
We have to build ML algorithms in System Verilog which is a Hardware development Language and then program it onto an FPGA to apply Machine Learning to hardware.
There are various means to select important variables from a data set that include the following:
- Identify and discard correlated variables before finalising on important variables
- The variables could be selected based on ‘p’ values from Linear Regression Forward, Backward, and Stepwise selection
- Lasso Regression
- Random Forest and plot variable chart
- Top features can be selected based on information gain for the available set of features.
- Deciding which Machine Learning algorithm purely depends on the type of data in a given dataset.
- If data is linear then, we use linear regression.
- If data shows non-linearity then, the bagging algorithm would do better. If the data is to be analysed/interpreted for some business purposes then, we can use decision trees or SVM.
- If the dataset consists of images, videos, audios then, neural networks would be helpful to get the solution accurately.
- There is no certain metric to decide which algorithm to be used for a given situation or a data set. We need to explore the data using EDA (Exploratory Data Analysis) and understand the purpose of using the dataset to come up with the best fit algorithm. So, it is important to study all the algorithms in detail.
This represents only 5% of the knowledge we have of the Data Science & Machine Learning Interview Process.
If you’re looking for a more comprehensive insight into career options and have full clarity on the steps required to achieve success, check out our MLDS Career Accelerator Programme Syllabus or watch our Free Webinar | How To Accelerate Your Tech Career.
Freya supports the tech talent toolbox arm of Reflection X where she is coaching technologists to achieve their full potential. With over 10 years of experience within the Tech and AI recruitment space, she has been from working closely with thousands individuals and leaders during hiring processes while partnering with very early stage start-ups all the way to some of the top AI Labs in the world.