Nowadays, machine learning is one of the hottest and most wanted technologies out there. And machine learning jobs are in remarkably high demand. More and more companies are adopting these technologies, and this demand is only going to get higher.
If you want to use AI and ML for your applications, you should know what skills you’ll need.
1. Probability and statistics
Hate it or not, mathematics is the foundation of machine learning. A formal characterization of probability (conditional probability, Bayes rule, likelihood, independence) and techniques derived from it (Bayes Nets, Hidden Markov Models, etc.) are at the core of Machine Learning algorithms. That’s why developers need a good knowledge of probability to create models from data. And statistics gives you the measures, distribution, and analysis methods needed for building and validating models from observed data.
2. Machine learning algorithms
Standard implementations of Machine Learning algorithms are widely available through libraries/packages/APIs, but applying them effectively involves choosing a suitable model (decision tree, nearest neighbor, ensemble of multiple models, etc.), a learning procedure to fit the data (linear regression, gradient descent, genetic algorithms, and other model-specific methods), as well as understanding how hyperparameters affect learning. You also need to be aware of the relative advantages and disadvantages of different approaches.
3. Data modelling and evaluation
A lot of machine learning has to work with unstructured data. The data you use and how you use it will define the success of your machine learning model. After you’ve built a machine learning model and trained it on some data, you have to evaluate how good is the model. How well is your model doing? Is it a useful model? Will training the model on more data improve its performance? Do you need to include more features? Depending on the task, you will need to choose an appropriate accuracy/error measure and an evaluation strategy.
4. Programming languages
There are no specific programming languages if you want to be a machine learning developer. However, programmers with a good knowledge of the major languages (mostly used for machine learning) grow quicker.
Python: it is easy to learn and to implement algorithms. Python supports object-oriented, procedure-oriented, and functional programming.
R: there’s no better language than R to analyze and manipulate data for statistics. The language’s AI focus is supported by libraries such as RODBC and Class, which are widely used in ML.
5. Natural language processing
Natural language processing (NLP) is a field in machine learning connected with understanding and processing the interactions between computers and human language. Specialists use NLP technologies to process natural language data on a vast scale, using analysis to perform specific tasks.
What is NLP used for? Natural Language Processing is the driving force behind the following common applications:
- Language translation applications such as Google Translate
- Word Processors such as Microsoft Word and Grammarly that employ NLP to check grammatical accuracy of texts.
- Interactive Voice Response (IVR) applications used in call centers to respond to certain users’ requests.
- Personal assistant applications such as OK Google, Siri, Cortana, and Alexa.
- Auto-Predict(Google Search predicts user search results)
*Tip: stay curious and relevant
The thing you need if you want to be good, according to ML programmers, is an undying curiosity. You must stay up to date with any coming changes and be aware of everything, that is happening in ML field. If you don’t want to miss new techniques, methodologies, algorithms, libraries, and paradigms - subscribe to tech blogs, follow technical conferences, and read research papers.