What is Machine Learning? | How to become a Machine Learning Engineer?

Posted on Posted in Machine Learning

“People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.”

What is Machine Learning?

Machine learning has become an expansive field involving computer science, statistics and engineering. It is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data and finding patterns in data. This learning process make better decisions in the future based on the examples that we provideover time.
So, the primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Machine Learning algorithms are classified as –

1) Supervised Machine Learning

The program is trained using a pre-defined labeled “training set” to facilitate its ability to reach on conclusions when new data is fed into the system. The program is trained until the desired level of accuracy is reached.

2) Unsupervised Machine Learning

In this case, there are no labels associated with data. These category of machine learning algorithms organize the data into a group of clusters to describe its structure and make complex data look simple and organized for analysis.

Basically, the program is given a set of data points and it has to detect pattern and relationship on its own.

3) Reinforcement Machine Learning

Here the system interacts with the environment and produces actions discovering errors or rewards. These kind of algorithms choose an action, based on each data point and later learn how good the decision was. Over time, the algorithm changes its strategy to learn better and achieve the best reward.

Some characteristics of ML

• the emphasis on predictions;
• evaluating results via prediction performance;
• having concern for overfitting but not model complexity per se like statistical analysis.
• emphasis on performance;
• obtaining generalizability through performance on novel datasets;
• usually no superpopulation model specified;
• concern over performance and robustness.

Through several years , ML researchers have worked tirelessly to improve interpretations while statistical researchers have improved the prediction performance of their algorithms.

How to become a Machine Learning Engineer?

Now a days, Machine Learning is one of the promising field in Analytics edge.

Machine Learning Engineers are part of Engineering team who build and implement production machine learning systems such as recommendations engine, personalized ranking etc. while Data Scientist are part of Data Science team who works on understanding data to inform product decisions.

To be a Machine Learning Engineer one should have Following Skills:

  1. Computer Science Fundamentals and Programming
  2. Probability and Statistics
  3. Data Modeling and Evaluation
  4. Applying Machine Learning Algorithms and Libraries like:scikit-learnTheanoSpark MLlibH2OTensorFlow etc.
  5. In depth knowledge in algorithm like: decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods etc.
  6. Full knowledge about bias and variance tradeoff, overfitting and underfitting, missing data, data leakage, etc.

What is Deep Learning? How To Start with Deep Learning?

Deep Learning is a growing trend and new area of research in Machine Learning. It promises general, powerful, and fast machine learning, moving us one step closer to Artificial Intelligence.

To start with deep learning, you need to have in depth knowledge of below fields:
  1. Probability and Statistics
  2. Multivariate Calculus
  3. Convex Optimization (for stochastic gradient descent)
  4. Linear Algebra and matrices
  5. Stochastic methods: Understanding approximation using stochastic methods would also be useful