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Do you require machine learning skills to function well in your job role? This article provides a guide on how you can develop the skills and include them on your resume.

 

What are Machine Learning Skills?

Machine learning is a branch of artificial intelligence ( AI) and also computer science that focuses on the utilization of data and algorithms to imitate the way humans learn and also to enhance accuracy. Machine learning is an essential component. Through the use the statistical methods, algorithms are also trained to make classifications or predictions, and also cover insights that subsequently drive decision-making within applications and businesses and also affect the growth of metrics. As big data continues to grow and expand, the market demand for data scientists will keep on increasing. They will also be required to specify the most business questions and also the data to answer them.

 

How Machine Learning Works

1. A Decision Process: in general, machine learning algorithms are used to make predictions or classifications. Based on some input data, which can be labeled or unlabeled, your algorithm will bring about an estimate of a data pattern in a data.
2. An Error Function: an error function assesses the prediction model. If there are some known examples, an error function can make a comparison to evaluate the accuracy of the model.
3. A Model Optimization Process:  It is the model that can fit better to the data points in the training sets, then weights are adjusted to decrease the discrepancy between the known example and the model estimate. The algorithm will repeat this “evaluate and optimize” process, updating weights automatically until a threshold of accuracy is met.

 

Importance of Machine Learning Skills

• Machine learning enhances hospitals: Managing patient flow is one of the biggest challenges of hospitals and other healthcare agencies. Hospitals have the challenge of overcrowded emergency rooms, cancellations, delays, and above all, a patient’s condition is affected by these situations. Machine learning can help decrease these issues by creating predictive models based on real-time data. It plays a huge role in scheduling overtime, reducing waiting times, enhancing unloading management, and many more. This can help the hospital to save money and provide better care. Cancer is a very complex disease because it is hard to predict the outcome. A machine-learning model could help predict the chances of a patient responding to first-line therapies.
• It enhances the banking sector:  the banking sector is very much complicated, and there are cases of fraud and criminal activities to extort money from customers. Machine learning helps in the area of fraud detection and it can also recognize malicious activity, verify user identity and also respond immediately to emergencies. Banks use machine learning because it decreases the risk of data breaches and cyber-attacks.
• Machine learning can threaten and improve cyber security: Artificial intelligence is used to get more information about attacks and also make more effective discovering of fraud. This is one of the most common problems of technological advancement because malicious actors spring up. To fight these advanced threats and more outdated attacks, the organization needs highly defensive technology. Machine learning can analyze past attacks, react to activity in real-time, automate tasks, and also saves money.
• Machine learning is vital for self-driving cars: while many are driving their cars personally, these cars will become more common, the algorithms can collect data through sensors and cameras, assess the data,and decide what the car will do. Algorithms can make cars drive on their own. This has helped to reduce the rate of accidents and helped cars to reach their destinations successfully. Studies that machine learning can enhance the self-driving of cars.
• It enhances video games: Machine learning could transform the gaming industry. With the use of advanced algorithms, elements of a game- including objects, non-player characteristics,and also the game world could respond to changes based on a player’s action. A player’s experience would be very much peculiar based on their choices and thus makes the gameplay very much engaging. Some video games such as chess have started utilizing machine learning because it enhances the features of video games and makes it much easier for video game developers. It has been estimated that over 1.3 people play video games and the use of machine learning could affect a lot of people.

 

How to improve your Machine Learning Skills

• Handling missing values & Outliers: One of the easiest ways to enhance the accuracy of machine learning models is by handling missing values and outliers. If some of your data values are missing or contain outliers, it can make your result to be less accurate. This is because missing values can bring about incorrect assumptions about your data. The solution to this is to utilize multiple algorithms.
• Reframe the problem: sometimes, improving a model may have nothing to do with data or techniques which are utilized to train a model. You can consider looking at the problem from a different perspective and leverage the data to extract latent indicators and also hidden relationships to readjust the question.
• Utilize meaningful data and values: Although some data samples and features can enhance the accuracy of the model, they can also introduce noise since not all data and features are meaningful. It is also essential to consider that not all features are the most essential that impacts decisions made by the Utilizing techniques like permutation Feature Importance (PFI) can help recognize these salient features and also help explain the model but also utilize the output as a feature selection method to decrease the number of noisy features which is enrolled in the training process.
• Cross–validation: Cross-validation is a training and model evaluation technique that divides data into several partitions and also equips multiple algorithms on these partitions. The technique enhances the robustness of the model holding data from the training process. In addition to enhancing performances on unseen observations, it can be a very efficient tool for training models with smaller datasets.

 

Jobs that Require Machine Learning Skills

• Data Analyst: a data analyst is responsible for evaluating data from mainly a large dataset. They share the result of their findings with the leadership team within the organization so that individuals can make crucial business decisions based on the data collected. Data analysts utilize machine learning to organize the information in a digestible format.
• Business intelligence developer: A business intelligence developer is responsible for utilizing different software to gain insight into a company’s business activities to share these insights with individuals that are responsible for making crucial business decisions. They are responsible for developing reports and dashboards to disseminate the information that they have gathered. A business intelligence developer utilizes machine learning to help a business manage data, improve strategy and predict trends.
• Software developer:  A software developer is responsible for paying keen attention to users’ needs, these users can be the employees of an organization, and software is designed to meet up with such needs. They utilize machine learning to make employees more productive and also help them to work efficiently.
• Data Scientist: A data scientist is responsible for utilizing data visualization and also programming skills to predict future events at an organization. They vary from data scientists typically because they use the latest insights to guide business decisions. Data scientists can utilize machine learning to predict events based on past patterns.
• Machine learning engineer: A machine learning engineer designs and execute systems in the specialization of machine learning.

 

How to Include Machine Learning Skills on Your Resume

• Discuss your Past Experience from Your previous Workplace: You should discuss your experience from your previous workplace by stating the projects you have accomplished and also how you accomplished them. If you are a fresher or an entry-level professional, give an expanded explanation about the projects that you have done so far. You should also discuss your past achievements and the accolades you have received from your superiors. You should explain the following in your resume:

1. 1. 1. Machine learning projects with objectives, strategies, and results
      2. Mastery of any programming language
      3. Past proven expertise in solving logical problems using data
      4. Training or internship in data mining or data analytics
      5. Highlight your knowledge about Python or R if you possess any.

• Discuss your programming language Skills:

Discuss the relevant knowledge of programming that you have. Programming executes any language with essential components and features. Some programming languages are best considered for machine learning complex projects. So if you possess a working knowledge of these programming languages, your resume will be highly marketable.

• Highlight Your Skills

At this point, highlight both the hard skills and soft skills you possess in your resume. You can separate the hard skills from the soft skills like this:

Hard Skills

• Java
• ML
• Pandas
• MATLAB
• Data Modeling
• Regression
• Classification
• Clustering models
• Tensorflow
• Pytorch
• Python
• Data structures
• Natural language processing

Soft Skills

• Time management
• Critical thinking
• Interpersonal skills
• Presentation skills
• Teamwork and collaboration
• Written and verbal communication
• Problem-solving
• Attention to detail

Examples of How to Include Machine learning on Your Resume

Data Analyst Resume Sample

• Employed Microsoft SPSS statistical software to track and analyze data
• Designed and constructed statistical analysis models on large data sets using  Teradata
• Increased sales by 17%
• Successfully analyzed data to identify key metrics and draw a conclusion
• Recommended solutions to improve systems efficiencies, leading to a 15% reduction in operating costs

Machine learning Engineer Resume Sample

• Designed and formulated analysis systems to extract information from large scale
• Formulated customer segmentation algorithm in R leading to a 22% increase in market share
• Optimized personalization algorithms for applications with 2M+users
• Applied data mining to shipping unification problem, saving $1.2M
• Foresaw product sales to within 2% by applying a logistic regression model
• Mentored institutions on big data and analytics, facilitating receipt of the Global IT Innovation Award for customer segmentation algorithm.

 

How to Demonstrate Machine Learning Skills in an Interview

• Be prompt: do not delay when answering questions so that the interviewer will not get the impression that the questions are difficult. Try as very much as possible to answer the questions quickly, in answering the question you should ensure you brainstorm so that you can answer the question correctly. It is not just about answering it quickly but it must be correct and accurate.
• Use the STAR Model

Situation

Describe a situation you handled or a project you handled utilizing machine learning skills.  The situation should be described in detail so that the interviewer can have a clearer picture of the project you undertook.

Task

The next step is the task at hand, what is the task at hand that required you to utilize your machine-learning skills. If the task is difficult, you should specify and if the task is simple you should also specify. Ensure you talk about a difficult project that you handled. This will prove to the interviewer that you are a candidate that is not threatened by a challenging or complex project.

Action

At this stage, you need to specify what action you undertook and what the necessary steps you followed are. You should be very much detailed about it and also discuss why you undertook such action. This will show the interviewer that you possess an analytical mind. Discuss the action you undertook in an emergency.

Result

At this point, you need to specify the result of your action; if you achieve a successful result, you should go ahead to specify and if the result was not successful, you should also state the truth. Feel free to discuss the lessons you have learned from the failure of your project and how you feel you can do better next time.

 

Examples of Interview Questions to Test Machine Learning Skills

• What are the three various types of machine learning
• What are the ‘training set’ and ‘test set’ in a Machine learning model? How much Data will you allocate to your validation, training, and test sets?
• How do you handle missing or corrupted data in your Dataset?
• How can you select a classifier based on a training set data size?
• Discuss the confusion Matrix concerning machine learning algorithms
• What are false positives and false negatives and how are they important?
• What are the three stages of building a model in machine learning?
• What do you understand by deep learning?
• Differentiate between machine learning and deep learning?
• What do you understand by the applications of supervised machine learning in Modern Business?
• Explain semi-supervised machine learning.
• Differentiate between supervised and unsupervised machine learning?
• Differentiate between inductive machine learning and deductive machine learning?

 

Resume Skills