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Machine Learning Engineer Roadmap

  • February 23, 2023
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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Analytics has become an increasingly important aspect of business, technology, and finance. Deriving meaningful insights from the available data is one of the most important objectives of the analysis process. Collection and analysis of data steps are some of the crucial steps. Today, the Global Machine Learning Market is expected to grow at a compound annual growth rate of 42.08% from 2018 to 2024. Programming languages also enable developers to work towards the customization of websites accordingly.

Machine learning is a subset of Artificial Intelligence (AI) that is used to make decisions or forecasts based on the data analysis step. It is a process of enabling computers to learn with experience and adjust according to the outcomes Machine Learning involves making machines smarter by allowing them to learn, predict and adapt from past outcomes.

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Machine learning is a manner of achieving AI to work without having to specify all the guidelines and processes beforehand. ML algorithms use past data to predict new outcomes and make informed data-driven decisions accordingly. This advanced type of Artificial Intelligence has tremendously reduced the industrial workload of the healthcare and finance sectors.

Essential Skills required by Machine Learning Engineers

Machine Learning Engineer focuses on the development of software that allows the use of data and predictions to be made. It also allows the automation of prediction models. The skills required by a machine learning engineer are as follows

  • Software Engineering: Fundamentals of Computer Science engineering are essential for a Machine Learning Engineer. A good understanding of Data Structures and Algorithms is also necessary. Such as Multi-dimensional arrays, arrays, stacks, queues, trees, etc. Machine Learning Engineers should be able to make algorithms that can search, sort, and optimize code effectively. Understanding computations, complexity, and computer architecture are essential as well.
  • Data Science: Programming Languages like Python, SQL, R, Java, etc are required for understanding the essence of data science. Probability and statistics are also very important. Topics like Hypothesis Testing, Likelihood, Bayes Rule, Calculus, Conditional Probability, Hidden Markov Models, Distributions, etc. are included in data science.
  • Machine Learning: Machine Learning algorithms are implemented through Scikit-Learn, Keras, TensorFlow, PyTorch, MLlib, etc. Machine learning engineers should also be adept at hyperparameter tuning.
  • Spark & Hadoop, Apache Kafka, Google Cloud ML Engine, Amazon Machine Learning, Azure Machine Learning, IBM Watson, etc are some of the other tools Machine learning engineers are required to know.

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Roadmap to becoming a Machine Learning Engineer

Familiarize yourself with the fundamentals, theories, concepts, and technologies used in Machine Learning. These make up the building blocks of Machine Learning. To always recommended to have basics at your fingertips and the same applies here. Learning Linear Algebra, Statistics, Standard Deviation, and Probability is required.

Understanding Machine Learning algorithms thoroughly. Topics such as Linear Regression, Logistic Regression, Support Vector Machines, Clustering, etc are important for building machine learning algorithms.

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Selection of a machine learning basis by understanding Supervised, Unsupervised, Classification, Pattern Recognition, Recommender system, Imitative learning, etc are important.

Having an understanding of all the Machine Learning algorithms. These include

  • Scikit-learn is an open-source software library that is capable of running with SciPy or NumPy.
  • Theano is another Python library that allows defining, optimization, and evaluation of mathematical expressions that involve multi-dimensional arrays.
  • Tensorflow is also an open-source software library for computation.
  • PyTorch is a Python package that has two high-level features 1) Tensor computation just like NumPy with a strong GPU acceleration, and 2) Deep neural networks.

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Working on a machine learning project to utilize all that you’ve learned. This can be achieved by working with a start-up or a small company initially.

What are the stages of Machine Learning?

  1. Data Gathering: It is important to gather correct and accurate data. This is a determinant step as the quality and the quantity of data will determine the outcomes of the project.
  2. Data Preparation: This includes de-duping, normalization, error correction, feature engineering, etc.
  3. Model Building: The solution is found by building the correct model and choosing the right model.
  4. Training and Testing your model: The test dataset is a subset of the training dataset and provides an unbiased evaluation.
  5. Evaluation and Optimization of the model.
  6. Experimental Tracking.
  7. Model Deployment.

What are Machine Learning algorithms?

  • Supervised learning
  • Unsupervised learning
  • Semi-supervised learning
  • Reinforcement learning
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random forest
  • Support Vector Machines (SVM’s)
  • Naive Bayes
  • KNN classification
  • K-Means
  • Artificial neural networks (ANNs)
  • Recurrent neural networks (RNNs)

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Conclusion

We have thoroughly understood the roadmap to become a Machine Learning Engineer, this will help you in following and choosing the right path. Machine Learning Engineers are currently in high demand because of the advancements in technologies and especially in Computer Science. Hence, to pursue a career as a Machine Learning Engineer, it is vital to understand the pros and cons of this field and to have an understanding of what exactly are the objectives of a Machine Learning Engineer.

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