About Speaker
Raj Kumar DwivediAssociate Director
Raj has more than 13 years of experience in Problem solving, Business Analysis, Consulting, Solution Design and Analytics. He has extensive experience in Machine Learning, Data Analytics, Creating Point of Views Doc...
How to develop a Machine Learning careerDeveloping a career in machine learning can be an exciting and rewarding journey. There is huge demand for Machine Learning engineers in various fields like banking, automobile, health care etc. Machine Learning aspirants should gain a strong foundation in mathematics and statistics. Having a solid understanding of these subjects will help them grasp the underlying principles of machine learning algorithms.
Aspirants should also learn programming languages such as Python and R. These languages have robust libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn) that make it easier to implement and experiment with machine learning models. They should study the core concepts and algorithms of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning. Learn about different types of algorithms, including decision trees, support vector machines, neural networks, and ensemble methods.
Aspirants should enroll in online or offline courses and workshops to understand the technical aspects of the AI/ML and they should try to find out real world use cases where Machine Learning models can be implemented to improve efficiency and automate the business process. There are numerous online platforms that offer machine learning courses, such as Coursera, edX, Udemy, LinkedIn, and Udacity. These courses cover a wide range of topics, from introductory to advanced levels, and can provide them with practical knowledge and hands-on experience.
Remember that developing a career in machine learning requires continuous learning and staying updated with the latest advancements. Be persistent, stay curious, and seek opportunities to apply your skills to solve real-world problems.
Aspirants should also learn programming languages such as Python and R. These languages have robust libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn) that make it easier to implement and experiment with machine learning models. They should study the core concepts and algorithms of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning. Learn about different types of algorithms, including decision trees, support vector machines, neural networks, and ensemble methods.
Aspirants should enroll in online or offline courses and workshops to understand the technical aspects of the AI/ML and they should try to find out real world use cases where Machine Learning models can be implemented to improve efficiency and automate the business process. There are numerous online platforms that offer machine learning courses, such as Coursera, edX, Udemy, LinkedIn, and Udacity. These courses cover a wide range of topics, from introductory to advanced levels, and can provide them with practical knowledge and hands-on experience.
Remember that developing a career in machine learning requires continuous learning and staying updated with the latest advancements. Be persistent, stay curious, and seek opportunities to apply your skills to solve real-world problems.
Key points of discussion
- Understanding of machine learning and artificial intelligence
- Key distinctions between artificial intelligence and machine learning
- Emerging industry trends that are creating demand for machine learning engineers
- Specific skill sets machine learning engineers require
- The education required for machine learning engineers, and the corresponding educational path
- The kinds of careers available to machine learning engineers
Who should attend
helping Students, Developers, Professionals,CIOs, IT Directors/Managers,CISOs, Business decision makers
helping Students, Developers, Professionals,CIOs, IT Directors/Managers,CISOs, Business decision makers