Novice to Expert: How Long it Takes to Learn Machine Learning?

time taken to learn machine learning

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Are you in a dilemma of whether to pursue machine learning or not? Even if you have decided to become an ML engineer, the next big question you might have is, “How long does it take to master it?”

Well, your doubt is valid. ML is multi-disciplinary and demands mastering several fields. However, how much time it takes to learn depends on several factors, including educational background, skills, and expertise in statics and algorithms.

In this blog, we will show you which factors have an impact and the approximate time it takes to learn ML.

 

How long does it take to learn Machine learning?

Generally, machine learning may take six months or years, depending on your education, experience, and skills in the field of computer science.  However, before coming to any conclusion, it is essential to consider all the factors, such as your previous knowledge, learning capability, coding experience, and resources.

If you are a newbie in machine learning, first you have to understand various concepts involved in it and then find the exact roadmap to becoming an ML engineer. Furthermore, programming experience makes a huge impact on this time frame.

On the other hand, if you have previous experience in Python or R can learn ML in a maximum of six months, while a person without prior experience in programming will probably take longer. 

Let us first figure out the time taken to learn machine learning if you don’t have prior coding knowledge.

 

How long does it take to learn ML without coding knowledge?

Programming is an integral part of machine learning. ML engineers are expected to be proficient in programming languages such as Python or R. Between these two, Python is popular and easier.

Irrespective of which programming language you choose, you must devote time to learning the coding language and it different libraries

Let’s say you are choosing Python, which may take a few weeks to several months to master, depending on your learning ability.

Several factors come into play whenever we talk about how long it takes; for instance, writing the first program in Python may take a few hours, but overarching libraries like Scikit-Learn, Pandas, Numpy, and libraries may take several months.

Similarly, if you are considering R, this will be more complicated. The process is similar to Python, starting with the basics and then learning the various data packages such as DataExplorer, Dalex, Esquire, Caret, and Janitor.

 

What are other fields to master before ML?

Machine learning is a multidisciplinary field that requires repeated use of programming, mathematics and statistics. Thus, get your hands dirty with basic knowledge of these two fields.

Both mathematics and statistics are essential for machine learning. Linear algebra, calculus, and probability are the foundations of machine learning. But according to professionals, you can dive straight into learning ML. Yes, it’s helpful if you know mathematics but don’t let a lack of mathematical knowledge deter you.

On the other hand, statistics is a lot more mandatory. It helps in analyzing and visualizing data which can then be used to find patterns and trends.

 

How long does it take to learn ML with coding knowledge?

Let us now discuss the fundamental processes of machine learning and how much time you have to dedicate to learn each of these processes. 

ML process can be divided into three steps:

  • Data Manipulation 
  • Data Visualization/EDA 
  • ML Algorithms Concepts ( Concepts and Implementation )

 

Data Manipulation:

Data Manipulation refers to changing or altering data to make it more readable. The process is divided into various steps: importing data, searching, sorting, grouping, filtering, and transposing. To learn all these steps, you must invest a significant amount of time to implement all the fundamental techniques.

The ideal time to understand concepts is 2 hours; their implementation may take up to 10 hours. To gain more expertise, practice as much as you can.

 

Data Visualization:

Overarching data visualization may take a lot of practice, but to understand the basics, dedicate around 2 hours to know the basic visual plots and then around 5 hours to learn types of data visualizations. For further expertise, practice as much as you can.

 

Machine learning:

 We will categorize this section into three parts:

  • Basics: 13 Hours 
  • Applications of ML: 10 Hours
  • ML Algorithms (Concepts and Implementation) – Linear regression: 10 hours; logistic regression: 10 hours; decision trees (CART): 20 hours.

 

Machine Concepts:

Before starting with an algorithm, you must learn ML concepts. These concepts will help you build a foundation. Also, most of the interview questions are asked from this part. Therefore, you should invest more time to learn this section.

For clarification, “ML concepts” refer to topics like bootstrapping sampling, ensemble learning, bagging and boosting, AUC & ROC, normalization vs. standardization, etc. The standard time required to learn and go through each of them may be around 10+ hours.

 

ML Algorithm: 

There are three types of ML algorithms you have to master:

 

Linear Regression:

The time required to learn linear regression depends on your previous knowledge and mathematics skills; still, if we have to give a timeline, you can learn the entire concept within 10 hours.

Although this can be daunting for some people as you have to master the fundamental concepts of linear regression, such as simple linear regression, gradient descent, and regularization.

 

Logistic Regression: 

If we have to give you a particular time frame for this algorithm, this may take around ten or more hours. The entire process can be complex and time-consuming, as it covers various topics, such as the use of logistic regression in prediction, etc.

 

Decision Tree:

The ideal time to learn a decision tree is 10 to 20 hours for an overview and the basics. Further implementation takes even longer. Remember that the ultimate goal is to learn how to use decisions in real-world ML models.

 

Conclusion

The ideal time  required to become a machine learning expert depends on various factors, such as previous knowledge and learning ability, and one such factor is coding. Without coding, the timeframe may vary from 6 months to years. On the other hand for coders, the process will be less hectic; they can directly get into learning the machine learning process. The time frame in this case can range from 6 to 18 months, considering all the factors.

Find out what you already know and decide what works best for you.

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