Getting a Job in AI and ML after Graduation

Getting a Job in AI and ML after Graduation

Artificial Intelligence and Machine Learning are among the most exciting and in-demand fields in technology. Induction into AI/ML for BTech graduates can open vast career opportunities on a spectrum of sectors, ranging from tech giants to healthcare, finance, and entertainment. The popularity of AI-based technologies like voice assistants, autonomous vehicles, predictive analytics, etc., has generated immense demand for related skilled professionals in the domain.

In this article, we will discuss how a BTech graduate can effortlessly penetrate the AI/ML industry, bringing forward the requirements of their skills and certifications and strategies further in the following section. We will discuss the top companies hiring for AI roles and emerging trends in the industry as well.

Understanding AI and Machine Learning

Before breaking into the field, it is very important to understand what AI and Machine Learning encompass.

• In general, the term artificial intelligence is taken to be a more expansive concept of machines or software designed to perform tasks so characteristic of humans, such as understanding a natural language, identifying patterns, and even, in some cases, making decisions.
• This refers to the subfield of AI where machines are trained so that they can learn from data, thereby doing better with time, without explicit programming. Most applications in AI are based basically on ML whether it’s a recommendation system or self-driving cars.

Key skills to master for AI and ML Jobs

AI/ML careers would require a basic technical skill set with hands-on experience. Here is the list of fundamental skills and tools in demand by most employers who look for AI or ML professional.

1. Programming Skills:

AI and ML require just understanding about the programming language. Most applications in the AI domain are coded either in Python or R or Java. The reason why most people prefer Python is that it is easy to learn, and it has an enormous library collection.

• Python: Learn the popular libraries such as TensorFlow, Keras, Scikit-learn, NumPy, and Pandas.
• R: R is mainly used in data science but may come handy for some AI applications.
• Java/C++: Many AI systems, especially in big data and enterprise application scenarios are written in Java or C++.

2. Mathematics and Statistics

AI/ML algorithms are based upon some mathematical concepts also. You must be very strong in the below fields to achieve success in this area:

• Linear Algebra: Matrix operations, vector spaces, and application of concepts such as neural networks and support vector machines.
• Calculus: Differentiation, gradients, and optimizations are crucial for almost all the algorithms working behind the gradient descent methods of machine learning.
• Probability and Statistics: Probabilities are used in statistical methods by most of the machine learning models: Bayesian classifiers and decision trees.

3. Data Handling and Processing

Since the machine learning models are data-driven, it means you should be able to work on big data and perform data wrangling, cleaning, and feature engineering. All handling large and important skills for building an accurate model is associated with the manipulation of structured and unstructured data.

• Techniques of Data Preprocessing: Techniques included normalization of the data, feature scaling, and encoding of categorical variables.
• Big Data Tools: Infer use of tools on an application which would include Hadoop, Apache Spark, or distributed computing environments.

4. Basic ML Algorithms and Frameworks

Any ML algorithm may be considered extremely generic. Ability to understand, apply, and evaluate algorithms like below

• Supervised Learning: Linear regression, logistic regression, decision trees, support vector machines SVM), neural network.
• Unsupervised Learning: k-means, hierarchical clustering, principal component analysis PCA).
• Reinforcement Learning: Applied to robotics, autonomous systems, game AI.

Some AI/ML frameworks like TensorFlow, PyTorch, and Keras are included in deep learning models to work

5. Deep Learning and Neural Networks

Deep learning is a sub-domain of machine learning that has also gained a lot of popularity. All AI applications – image recognition, NLP or speech recognition, work in the form of CNNs (Convolutional Neural Networks) or RNNs (Recurrent Neural Networks) under this category, individually known specifically for neural networks.

6. NLP and Computer Vision

Subject to your niche, if one of the niche AI domains is the area in which you specialize-for example, master NLP, so you know it like the back of your hand, or computer vision, in case you’re good with image and video analysis-then this can very well separate you from others. Commonly used tools within such domains are OpenCV in case of computer vision and NLTK in case of NLP.

7. Version Control and Collaboration

While working on AI/ML projects with teams, one needs to be well-versed with the tools such as Git for version control and any platform like GitHub or GitLab for collaboration.

Portfolio Development of Excellent AI/ML Skills

After having all these skills, the most important thing is to build a portfolio that showcases your capabilities. Traditionally, those entering the AI/ML field are largely practice-experience seekers.

1. Open-Source Project

With open-source AI/ML projects, you may be able to gain hands-on experience practically and even show off your skills. The platforms sharing and maintaining such projects don’t get much better than GitHub, where you might find or even contribute to such projects. If you could not find any suitable open-source projects, you could create your own; developing a machine learning model or even creating an AI application will help you begin solving one of the actual, real-world problems that are currently within reach.

2. Develop and publish AI/ML projects

Projects that would exhibit your grasp of AI/ML. Examples

• Design a sentiment analysis tool using NLP.
• Develop a recommendation engine for an e-commerce website.
• Develop an image classification model using deep learning.

Publish your projects showing on GitHub or your personal portfolio site before presenting to potential employers.

3. AI/ML Competitions

You may join the AI/ML competitions, which are hosted in the platforms like Kaggle and DrivenData, you will be solving real-world problems. Such a contest enhances your resume as well as better sharpens your skills from interaction with diverse challenges.

Certifications to Upskill Your AI/ML Portfolio

Certification solidifies your skill sets, in specific instances of a BTech graduate who’s making it big into the AI/ML domain. There are some of the extremely well-recognized certifications: .

• Google AI/ML Specialization: A full course that introduces AI and ML using TensorFlow.
• Coursera’s Machine Learning by Andrew Ng: One of the best courses for a beginner ML enthusiast.
• Deep Learning Specialization on Coursera: Taught by Andrew Ng specifies deep learning, neural networks etc.
• IBM AI Engineering Professional Certificate: Techniques applied in AI and ML.
• AWS Certified Machine Learning: AWS offers a specific certification in the area of developing ML models on its cloud platform.

Hot Companies Hiring AI/ML Expert

AI/ML engineers are the new favorite child, and the service line is no doubt growing. Here are some emerging top companies which are aggressively hiring AI talent:

Technology Giants

• Google: Google leads the race handily in the AI research and development area. Opportunities range from working on AI teams for such projects as Google Assistant, Waymo, and DeepMind.
• Amazon: Amazon is using AI/ML in recommendation systems, Alexa voice assistant, and AWS machine learning services.
• Microsoft: The activities by Microsoft through Azure AI, Cortana, and enterprise AI solutions.
• Facebook (Meta): The Company spends a lot on AI for social media, VR, and other communication technologies .
• Apple: Apple is utilizing AI on Siri, facial recognition, and user experience.

Companies with AI Focus

• Open AI: This one focuses on AI research and product development.
• NVIDIA: The company leads in AI computing and deep learning hardware through high-performance GPUs.
• Tesla: Another interesting area is the AI research that Tesla is doing related to car software on autonomous cars.

Companies and Startups

Besides the above, there are numerous start-ups which have started considering applications of AI/ML in every sphere of health care, finance, cybersecurity, and much more. Some of the above startups include:

• CureMetrix: AI for Early Breast Cancer Detection
• Zebra Medical Vision: AI-enacted diagnostics in medical image diagnostics.
• UiPath: Focused on AI-based robotic process automation.

Emerging Trends in AI/ML Sector

With the advancements in AI and ML, new trends are constantly being made. Awareness of them would surely endow you with an edge.

• Edge AI: AI models run on the local device, for example, on the smartphone and do not depend on cloud computing.
• Explainable AI (XAI): The pursuit of transparency of an explainable procedure by which AI determines a course of action.
• AI Ethics and Fairness: Developing fair, equitable, and ethically robust AI systems
• AutoML: Automation of end-to-end application of ML: from developing models to deployment to non-experts in the field.

How to Get Hired

1. Networking

Networking indeed really helps a lot in getting an AI/ML job. Attend industry conferences, AI meets, and forums online and make contacts with professionals within the field. LinkedIn is also a good source for rapport-building with recruiters and AI/ML experts.

2. Alter Your Resume and Portfolio:

When applying through an AI/ML job posting, you have to customize your resume and portfolio with content suited for the job. To do this you would add all technical skills, certifications, and projects undertaken in AI/ML. Hands-on experience counts so your portfolio has to represent work you are proud of.

3. Apply for Internships or Junior AI Jobs

For freshers, an internship or a junior position usually constitutes a stepping stone into this AI world. See the organizations that have full-time openings for entry-level AI talent and do not wait for that full-time opening to apply for internships which are quite often converted into a full-time offer.

Conclusion

As a BTech graduate you will most certainly face lots of competition in AI and Machine Learning. That is why, as a precautionary measure, he needs to develop hard technical skills, relevant experience, and implement the strategic approach so that issues related to job search are solved. Key Takeaway Focus on mastering those key technical skills and you will surely impress with your portfolio and network seriously with the professionals and mentors in the industry. Since AI/ML is one of the high-demand job specifications, the field can make for some serious money-making opportunities and scope for your growth.

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