Machine learning is gaining traction everywhere. Almost all industries are implementing machine learning in their business processes to improve — productivity, marketing, sales, customer satisfaction, and business profit. This has created an interest in many IT professionals and they tend to change their career track. New graduates look forward to having a career in machine learning. Both these groups of people have many questions to ask about machine learning.
Keeping those budding professionals in mind, I have compiled the common questions that might haunt them and answered them here for reference. These answers may serve as a good starter for these aspiring professionals.
1) How do I learn machine learning on my own?
Mastering machine learning enables you to become a Data Scientist, AI/Machine Learning Engineer. It also helps to use ML algorithms for development or add skills to your business analysis toolbox.
As a self-starter, you can follow these steps to excel.
- Develop a strong foundation in Statistics, programming language like R, and a little Mathematics.
- Read books, blogs, articles, and watch YouTube to understand the essential theory behind Machine learning.
- Build practical skills the industry demands by using machine learning study packages and practice the essential topics.
- Take hand-on projects into interesting domains, dive deeper into projects and build a strong portfolio along the way.
2) What skills are needed for machine learning jobs?
As a job seeker, you should be a potential prospect for the employer. You must have a deeper understanding of Algorithms, Applied Mathematics, Statistics, Probability, Programming languages, Analytical and problem-solving skills.
Here is a list of key skill-sets you must have.
You must have a clear understanding of fundamentals like -
- Data structures
- Computability and complexity, and
- Computer architecture
Though you can start with one language, at one stage in your career, you should know all the languages. It is recommended to learn -
👉Probability and Statistics
Many ML algorithms are extensions of Statistics. Knowledge in probability and statistics makes your ML projects easy. You should know
- Probability and its techniques
- Measures, distributions, and analysis methods
👉Data modeling and evaluation
To apply standard algorithms, you should learn to choose an -
- Appropriate accuracy/error measure
- Evaluation strategy
👉Applying machine learning algorithms and libraries
Applying machine learning algorithms is essential. For that, you should have a strong understanding of –
- Gradient descent
- Convex optimization
- Quadratic programming
- Partial differential equations
👉Software engineering and system design
At the end of the day, your deliverable is software. So, you must understand –
- How different components fit together
- Build appropriate interfaces
- Avoid bottleneck with careful system design
- Scale algorithms to an ever-increasing volume of data
- Distributed computing
- Advanced signal processing techniques
3) What are some algorithms that every machine learning engineer should know?
Learning algorithms helps you solve real-world problems without or with minimal human intervention. Some of the popular algorithms you must know include –
- Apriori Algorithm
- Artificial Neural Networks
- K Means Clustering Algorithm
- Linear Regression
- Logistic Regression
- Naïve Bayes Classifier Algorithm
- Support Vector Machine Algorithm
4) How should you start a career in machine learning?
Jumpstart your career in machine learning by following these set rules.
- Gain a strong basic in Algebra, Calculus, and Statistics
- Learn programming languages — R, Python, and Java
- Attend to the exploratory project(s)
- Create supervised and unsupervised models
- Learn big data technologies
- Explore deep learning models
- Take online courses or certifications
- Start participating in Kaggle competitions
5) Which are the best online courses/certifications for AI/machine learning?
The top three best online certifications are presented here.
- 1) Artificial Intelligence Engineer (AIE) — Artificial Intelligence Board of America (ARTiBA): In this certification program, you will learn the concepts of ML, supervised and unsupervised learning, Natural Language Processing, Cognitive computing, Reinforced Learning, and Deep Learning. With its global reach, it is the industry standard in professional credibility. You can create your career niche in AI functions across industries and countries.
- 2) Professional Certificate Program in Machine Learning & Artificial Intelligence — MIT Professional Education: This certificate program enables you to get acquainted with the latest advancements and technical approaches in AI. You will get well-versed in Algorithmics, Natural language Processing, Predictive Analytics, and, Deep Learning.
- 3) Deep learning specialization — Deeplearning.ai: It is a five-course specialization that helps you to get specialized in Deep Learning fundamentals and its applications. You will learn about neural networks, deep learning, convolutional learning, sequence models, and structuring ML projects.
6) What are some common machine learning interview questions?
It is necessary to understand the AI and ML concepts to clear your interview successfully. Some of the common interview questions are listed below.
- What is the difference between machine learning and data mining?
- When and why do a model exhibit poor performance?
- What is a cross-validation technique?
- In which algorithm techniques you are best at?
- What is the function of unsupervised learning?
- Where do you use pattern recognition?
- What is model selection?
- What is PCA, KPCA, ICA?
- Explain ensemble learning
- Which technique of machine learning do you use more, why?
- How do you screen for outliers?
7) What are the AI and ML starting salaries (WORLDWIDE)?
According to PayScale, an entry-level ML engineer with less than one-year experience earns an average total compensation of $93,678. A machine learning specialist may earn up to 1 million.
The top respondents are from the companies like Accenture, Apple Inc., Amazon Inc., Microsoft Corp, J.P. Morgan Chase & Co., and Robinson Worldwide Inc.
According to Indeed.com, the average salary for an artificial intelligence engineer in the San Francisco area is approximately $134,135 per year.