Top Five Skills Required To Become A Machine Learning Expert

Machine learning has now been around for a long time as it has the ability to apply complex mathematical calculations to big data quickly and iteratively. This iterative aspect of machine learning is what helps it independently adapt to new data. Corporate giants such as Google, Microsoft, and Apple are, over the past year, testing the capabilities of machine learning for their businesses.

 

What Is Machine Learning?

 

Machine learning is a branch of science which applies Artificial Intelligence (AI) to enable systems to get into a mode of self-learning by experience without being explicitly programmed. These computer programs will learn, change, grow, and develop by themselves while exposed to new data. This is made possible through the previous computations that are made available and reliable results are produced using pattern recognition.

 

If you are one who is passionate about the technological advancements in the field of information technology or in particular about machine learning and are keen on pursuing a career in these fields, you need to possess a deep understanding of applied mathematics and the broad range of machine learning algorithms. You need to have an interest in probability and statistics and must have problem-solving, decision-making, analytical, and programming skills. Apart from these general skills, the technical skills that you need to have are:

 

1. Data Modeling And Evaluation

 

 

A machine learning model should always be evaluated to determine its performance in predicting new and future data. The accuracy metric of the machine learning model on data must be checked as future data contains unknown target values. This assessment is used as a proxy for predictive accuracy on future data. A machine learning expert must properly learn the steps of evaluating a model.

 

One must hold out a sample of data that has been labeled with the ground truth from the training data source. The machine learning model is then trained. The held-out observations for which you know the target values are sent to the model. The predictions returned by the model are then compared against the known target value. Finally, the summary metric must be computed to check how well the predicted and true values match.

 

2.Machine Learning Algorithms

 

 

A machine learning expert needs to be familiar with machine learning algorithms. Some of them are:

 

• Decision tree – It is similar to a flow chart where the flow starts at the root node and ends when a decision is made at the leaves. It is a tree-like graph which shows the prediction from a series of feature-related splits. The output of decision trees can be easily interpreted even by people without analytical, statistical, or mathematical knowledge. It is useful for analysts to identify significant variables and the relation between two or more variables.

 

 

• Random forest – These are ensembles of decision trees. Each decision tree is created by using a subset of the attributes. Those decision trees vote on the ways to classify a given instance of input data and random forest bootstraps those votes and chooses the best prediction. Random forest is a supervised classification algorithm. It creates a forest and orders their nodes and splits randomly. If one input a training data set into a decision tree, it formulates a set of rules which can then be used to perform predictions.

 

 

• Linear regression – It expresses the linear relationship between input x and output y. Regression is a method of modeling a target value on the basis of independent predictors. Linear regression with one input variable is simple linear regression. If there is more than one input variable, it is called multiple linear regression. It is a simple and useful algorithm that every machine learning enthusiast must know about.

 

3. Advanced Signal Processing Techniques

 

Signals can be processed to suit your needs in machine learning. Some of the types of signals are:

 

• Windowing – It is used to take a small window of the dataset and processing it to make a single finite rather than make it be of a periodic nature.

 

 

• Discrete Fourier Transform (DFT) – It is a sinusoidal signal to be decomposed into various frequencies. This makes it ‘discrete’ by nature.

 

 

• Fast Fourier Transform (FFT) – It is an algorithm which computes the discrete Fourier transform or the inverse of a sequence. It converts a signal from its original domain (time or space) to a representation in the frequency domain and vice versa.

 

4. Programming languages

 

A machine learning expert must be skilled in programming languages. Some of the programming languages that he must be proficient in are:

 

• R – It is one of the most efficient environments for analyzing and manipulating data for statistical purposes. R can be used to produce a well-designed publication-quality plot, mathematical symbols, and formulae. It has numerous packages like Class and G models for easy implementation of machine learning algorithms.

 

 

• Python – Its syntaxes are simple and can be easily learned. Its development time is short when compared to other languages such as C++ or JAVA. It supports functional, procedure-oriented, and object-oriented programming.

 

5.  Distributed computing

 

Industries like healthcare and advertising use distributed algorithms where a single application can be used to accumulate a lot of data. It helps handle large-scale data. It helps develop efficient and scalable algorithms which are accurate and are based on computation requirements such as time, communication needs, and memory. It helps to allocate the learning process into several workstations. This is what makes machine learning tasks on big data flexible, efficient, and scalable.

 

Conclusion

 

Machine learning plays a vital role in today’s data-rich world. It is a clever alternative to analyzing huge volumes of data. Machine learning has already penetrated human lives. The product suggestions that e-Commerce websites offer while doing online shopping, the results you get on search engines, the playlists of music streaming apps, friend suggestions on Facebook and the newsfeeds on social media are all real-time applications of machine learning.