Want to be an AI researcher?

From learning the basics of AI to diving deeper in order to solve real-world problems.

Step 1:

Learn the basics of python programming language like data structures, functions, objects, and classes. Once you are done with learning the basics of python, then you can make yourself familiar with a few libraries which we will be using in the next steps like NumPy, pandas, and scikit-learn.

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Step 2:

The next step is to learn about Exploratory Data Analysis. It refers to the process of performing initial investigations and analysis on data in order to find out any specific patterns, summarizing its main characteristics by using statistics and graphical visualizations.

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Step 3:

The next step is to learn about the Machine learning algorithms, metrics, and their use cases in real-world scenarios.

Most commonly these are the few basic terms that you will be hearing: Overfitting/ Underfitting, Bias/ Variance, Training, validation, test splitting of data, and Metrics to measure the performance of models like accuracy, precision, recall, and f1-score.

After getting familiar with basic terms, the next thing is to learn about machine learning methods that are used to solve real-world problems like face recognition, weather forecasting, recommendation systems, web advertising, fraud detection, malware detection, disease diagnosis, etc.

Supervised learning: It is the type of machine learning method in which models are trained using labeled training data, and on basis of that data, models predict the output. Here, labeled data means some input data is already mapped with the correct output which we expect. So models, learn to predict the correct output for some input data. It can be further divided into two types of problems: Classification and Regression.

Classification applications: Malware classification, disease diagnosis, character recognition, retail products classification.

Regression applications: Forecasting — stocks price, sales prediction, weather forecast.

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Unsupervised learning: It is the type of machine learning method in which no labeled training data is given, the goal is to discover the hidden patterns in the data, there are many clustering algorithms that we use to solve real-world applications.

Applications: Target market campaigns, customer segmentation, recommendation systems, anomaly detection.

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Step 4:

The next step is to learn about Deep learning: Each component within deep learning is a huge research area. Do you know how exactly our brain works? Neural networks behave in a similar way, it mimics the way the human brain operates.

First, start with the fundamentals of deep learning: Understanding neural networks, loss functions, activation functions, optimizers, backpropagation, batch normalization, data augmentation, regularization, and vanishing/ exploding gradient problems.

Learn about different architectures like linear neural networks, convolutional neural networks, recurrent neural networks, autoencoders, transformers, and generative adversarial networks (GAN).

Most common frameworks and result visualization tools: PyTorch, Tensorflow, Keras, Mlflow, Tensorboard, Wandb.

It further consists of several research areas based on the type of dataset and problem we are solving:

Computer Vision: It’s a massive research area, which trains machines to understand the visual world like human visibility systems by using images/ videos as input data. Few examples of existing CV tasks: Object detection, Object Classification, Object tracking, Object segmentation, and Content-based Image retrieval, 3D scene reconstruction.

Applications: facial recognition, self-driving cars, self-checkout devices, video surveillance, etc.

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Natural Language Processing: It’s also a massive research area, which trains machines to understand the human language (spoken/ written). A few examples of existing NLP research tasks are natural language generation, machine translation, named entity recognition, text classification.

Applications: Spam filters, Smart assistants, chatbots, language translation.

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Step 5:

Well, there are a few more topics that I have not added, but once you will get familiar with all these topics. Based on your interest you can dive deeper into that particular research area.

Next actions:

1. Please try to read multiple blogs/articles for each topic.
2. Solve some real-world case studies like building a recommendation system for Netflix/ Amazon, music notes generation using AI, etc.
3. Participate in Kaggle competitions, and AI Hackathons.
4. Develop the habit of reading research papers. (that’s the most important thing).
5. Link to research papers: paperswithcode, state of art papers