Guide to Kickstart Your Career in AI and Machine Learning
As the world around us transforming at a rapid pace, the human world is facing some of the complex problems with regards to climate, data security, infrastructure and more. The modern-day entrepreneurs and engineers are designing solutions powered by AI, machine learning and big data, creating technological innovation across the world. Those who are seeking career or business opportunities in these advanced technologies need to base their ideas on problem-solving mindset to achieve growth in their professional journey. Creating models based on solutions with AI and Machine Learning applications is paramount for career growth in the industry. However, those who wish to begin their career in AI and ML, need to understand the knowledge and skills it requires before you begin.
Data Science and Statistics
The data science ecosystem is an interactive map for AI and machine learning applications for deriving data for an action. As there is no single application which solves every use case, AI and machine learning use big data for understanding the intent of a user’s trigger by interacting with the database of user's past applied use-cases. It involves everything that a statistician do which includes, collecting data, sampling and resampling the data, running a regression and drawing insights using hypothesis testing and other statistical tools.
The sheer number of the data makes data-science complicated and hence the data scientists use SAS, Apache Spark, BigML and D3.js, as a tool based on the use-case. While ‘SAS’ is one of the most reliable tools for statistical modelling and organising a huge amount of data, it is also one of the most expensive tools and it is designed for big corporations and industries. The Apache Spark is an improvement on Hadoop and is specifically designed for batch processing and stream processing. Its API lets repeated access to its database enabling AI applications with quality insights and predictive data, making it one of the reliable tools in AI and machine learning industry.
Learning Python Computer Language
Python is one of the leading programming languages used in Machine learning and AI as it is one of the most concise and readable computer languages. Apart from its appeal to human language, Python also has an extensive library of pre-written packages of AI and machine learning algorithms which makes easy for engineers to apply codes to perform a certain AI or ML function. As the computer language is supported by multiple operating systems, programmers can write codes in one and perform algorithms in another system. This facilitates engineers with flexibility, independence and makes Python one of the most preferred computer language for AI and ML solutions.
The Synergy of Statistics and Mathematics
Statistics and mathematics plays an crucial role in implementing the theories to a real-world solution. Similar to statistical tools used by data scientists, AI and ML also involves mathematical tools such as Linear Algebra, Vectors and Matric Factorisation. The vector spacing equations helps in image processing and recognition for AI and ML algorithms. The linear algebra and matrices are used for function derivatives when optimizing a loss function whereas Matic Factorisation formulas are used in decomposing complex operations making it easier for machines to perform an AI functions.
Machine Learning Workflow
Machine learning uses a workflow structure to perform training sequences for machines, where it performs a series of functions enabling the algorithm to learn. The machine learning workflow contains the following operations:
- Gathering data - Gathering a larger chunk of data helps the algorithm to be more accurate at its function. This involves collecting quantitative data sets from multiple sources.
- Data pre-processing - This process helps in identifying missing data, errors in data sets and classifying raw data in different parameters based on quality, source etc.
- Researching the model that will be best for the type of data – Programmers use statistical and mathematical tools to design a model which is best suited for the type of data.
- Training and testing the model - This process involves training, validating and testing the model and take corrective actions until the desired functionality is achieved.
- Evaluation - To improve the model, the algorithms go through a series of evaluation to identify further development.
These operations construct a robust machine learning workflow aimed to achieve the best solution for a use-case.
Although AI and ML are one of the booming sectors, it is always believed that one needs to be an expert in mathematics and statistics to achieve their career goals in these sectors. However, until and unless the real-world application is known, the statistical equations may not be useful. For someone who is solution oriented and understands the practical application of these tools, AI and ML is a perfect career opportunity.
As each market problems are unique, organisations seek local talent who are skilled with AI and ML programming. Thus, there is an immense demand for such talent with problem-solving and programming skills. Apart from the engineers, the industry also has opened new opportunities such as conversation designing, insight and analysis for non-engineers who want to kick-start their career in AI and Machine learning technology.
From user-based solutions such as AI assistants to business-oriented solutions such as consumer insights, AI and machine learning have opened the doors to new opportunities. As technologies like natural language processing and predictive engines progress, the industry is looking at immense growth in the near future. While the market is expected to grow at $2.6 Trillion by 2020, the synergy of big data, AI and machine learning, the technology is creating solutions for some of the most complex human problems and driving innovation.