Of all the innovative new technologies empowering the business world today, machine learning is proving to be one of the most transformational. It’s a disruptive technology, altering the landscape for business processes across the entire corporate spectrum—from data science, IT services, cybersecurity, and product development to customer relations, marketing, financial operations, and supply chain management. 

But for many professionals, machine learning is still a bit of a mystery. At first blush, it seems that only PhDs and senior data scientists are the ones utilizing its full capabilities. However, the reality is that anyone with a fundamental background in mathematics can learn the nuts and bolts of machine learning and apply it to the enterprise. Sometimes it just takes a little motivation to get the learning process kick-started. 

Looking forward to becoming a Machine Learning Engineer? Check out the AI and ML Certification and get certified today.

If you’re ready to take the plunge, follow these basic guidelines to begin fulfilling the prerequisites for an exhilarating machine learning job. 

What Is Machine Learning and Where Does It Fit?

Machine learning is a fast-growing subset of the artificial intelligence (AI) sector. Broadly speaking, AI technologies enable computers to mimic human intelligence, using logic, if-then rules, decision trees, and other intelligent functions to improve the efficiency of a wide range of business processes.

As a subset of AI, machine learning makes use of statistical techniques that empower machines to perform tasks better through repeated experiences. When automating data analysis, enabling computers to learn and adapt through experience to complete specific tasks without explicit programming is vital. It’s also becoming a valuable resource in recommendation engines, predictive analytics, facial recognition, and fraud protection, among many others. 

Machine learning is changing the competitive landscape of almost every conceivable industry. The global machine learning market is forecasted to grow to $20.83 billion by 2024, with a CAGR of over 44 percent, according to a report from Zion Market Research. Opportunities abound for professionals who want to be part of this new era of machine learning technologies. 

Start with Core Math and Statistics Skills

While you don’t have to be a math whiz to lock down a machine learning job, the reality is that some math is a prerequisite. The basic mathematical skills generally required include linear algebra, matrix algebra, statistics and probability, and some basic calculus. 

These math skills are required to perform various core tasks in machine learning, such as creating mathematical algorithms, running regression analysis, building and evaluating data models, and predicting and interpreting data. If you already have this background from undergraduate or graduate work, that’s great! But if not, a wide variety of classes are available online to help you get caught up. 

Supplement with Python and Data Science

One of the fastest-growing segments for machine learning is in data science. AI and machine learning are becoming synonymous with the data science field. The terms “AI” and “machine learning” were included in the job descriptions for about 75 percent of data scientist jobs, and demand for workers with AI skills has more than doubled in the last three years, according to Indeed.  

Python is perhaps the most popular programming language for linking machine learning with data science. According to the Tiobe Index of Language Popularity, Python was the fastest growing language last year and the most frequently taught first computer language in universities today (recently surpassing Java). Simplilearn’s Data Science with Python Course provides a broad education of the most relevant concepts of machine learning, including:

  • Machine learning data models
  • Data visualization, wrangling, and exploration
  • Hypothesis building
  • Web scraping
  • Natural language processing
  • And other essential programming techniques

In our Data Science with Python courses, learners also have an opportunity to study IBM Watson for Chatbots as an add-on to data science programs.

Accelerate your career with the Post Graduate Program in AI and Machine Learning with Purdue University collaborated with IBM.

Get Excited About Machine Learning!

It might sound a bit obvious, but a fundamental prerequisite for getting a machine learning job is getting excited about a machine learning job! Professionally speaking, there are tremendous career development opportunities in the space. Machine learning engineer is the best job of 2019 due to growing demand and high salaries, according to Indeed, with average salaries of $138,712 in the United States (as of Nov. 19).

Build Your Machine Learning Acumen Today 

Again, you don’t have to be a veteran data scientist or have a Ph.D. in advanced mathematics to enjoy working with machine learning tools and techniques. With the necessary foundation in math, statistics, and data science concepts, anyone can build a career with this booming technology. Start today with Simplilearn’s AI ML Course

Our AI & Machine Learning Courses Duration And Fees

AI & Machine Learning Courses typically range from a few weeks to several months, with fees varying based on program and institution.

Program NameDurationFees
AI & Machine Learning Bootcamp

Cohort Starts: 3 Jun, 2024

6 Months$ 10,000
Post Graduate Program in AI and Machine Learning

Cohort Starts: 13 Jun, 2024

11 Months$ 4,300
Generative AI for Business Transformation

Cohort Starts: 21 Jun, 2024

4 Months$ 3,350
Applied Generative AI Specialization

Cohort Starts: 25 Jun, 2024

4 Months$ 4,000
Professional Certificate Program in No Code Machine Learning

Cohort Starts: 26 Jun, 2024

4 Months$ 2,565
AI and Machine Learning Bootcamp - UT Dallas6 Months$ 8,000
Artificial Intelligence Engineer11 Months$ 1,449

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