Disclaimer

• Everything shared here does not guarantee a job in AI.
• This presentation is for knowledge sharing on general skills I find useful for AI work.

Agenda

• About me
• Skills for AI Jobs ⚙
  • Technical Skills
  • Personal (Soft) Skills
• Skills for AI interviews
  • Personal Internet Profile
  • Projects
  • Contributions
  • Visibility
• How I got my AI Jobs?
• At the end of the day what really matters
• Conclusions
• Contact

About me

About me

How did I end up here?

• Neural networks are the reason I started my PhD in computer science.
• The professor I talked with asked me if I wanted to work on natural language processing and neural networks.
• The notion of neural networks sounded very exciting to me.
• That’s when I knew that is what I wanted to do for my career.

About me

In my free time

• I like to share my knowledge on NLP: I wrote tutorials from scratch on state-of-the-art language models like Bert and GPT2 with over 10k views. Check them out here.
• Contribute to open-source – Hugging Face Transformers and Datasets.
• Technical reviewer for one of the first books published on transformers models for NLP. The book is called Transformers for NLP in Python, by Denis Rothman.
• Technical reviewer for the next edition of the Transformers for NLP in Python book.
• I like to be involved in various talks and presentations around AI/ML/NLP.
• My personal project ML Things for things I find useful and speed up my work with ML.

Personal (Soft) Skills

• Business knowledge
• Problem-solving
• Curiosity
• Critical thinking
• Communication
• Collaboration

Technical Skills

• Statistics
• Math
• Programming and coding
• Machine learning & Deep Learning
• Data wrangling & Preparation
• Model deployment & Production
• Data Visualization

Technical Skills - Statistics

• Be familiar with:
  • Normal Distribution
  • Mean, Median and More
  • Standard Deviation/Variance
  • Hypothesis testing, A/B testing, and confidence intervals
  • Probability distributions (Binomial, Poisson, normal)
  • P-Value
  • Basic probability
  • Data summaries and descriptive statistics
• Be able to: collect, organize, analyze, interpret and present data.
• Be able to work with the data to find useful results.

Technical Skills - Math

• Linear Algebra is essential for understanding many algorithms and prediction models:
  • Graphing functions
  • Multiplying matrices, matrix inverse, transpose of a matrix
  • Scalar multiplication, linear transformation
• Calculus
  • Functions: Single variable and multiple variable
  • Product and chain rule
• Math Helps You Answer the ‘Why’

Technical Skills - Programming and coding

• Top data science programming languages for 2022 (DataCamp):
  Python
  R
  SQL
  Java
  Julia
  Scala
  C/C++
  JavaScript
  Swift
  Go
  MATLAB
  SAS

Technical Skills - Programming and coding

Top data science programming languages for 2022 (DataCamp):
Python
R
SQL
Java
Julia
Scala
C/C++
JavaScript
Swift
Go
MATLAB
SAS

Technical Skills - Programming and coding

• Python: good/proficient knowledge
  • Pandas, PySpark
  • NumPy
  • Matplotlib
  • Scikit-learn
  • TensorFlow
  • PyTorch
• R: basic/good/proficient knowledge.
• HTML: basic knowledge.
• SQL: basic/good knowledge.
• C/C++: basic knowledge.

Technical Skills - Programming and coding

• Use coding best practices.
• Insist on highest coding standards.
• Write your test functions!
• Be responsible.

Technical Skills - Machine learning & deep learning

• Machine Learning (Classical Machine Learning)
  • Linear regression
  • Logistic regression
  • Decision tree
  • SVM algorithm
  • Naive Bayes algorithm
  • Clustering: KNN algorithm, K-means
  • Random forest algorithm
  • Dimensionality reduction algorithms: PCA, T-SNE
  • Gradient boosting algorithm and AdaBoosting algorithm

Technical Skills - Machine learning & deep learning

• List of top 10 most popular deep learning algorithms (SimpliLearn):
  • Convolutional Neural Networks (CNNs)
  • Long Short Term Memory Networks (LSTMs)
  • Recurrent Neural Networks (RNNs)
  • Generative Adversarial Networks (GANs)
  • Radial Basis Function Networks (RBFNs)
  • Multilayer Perceptrons (MLPs)
  • Self Organizing Maps (SOMs)
  • Deep Belief Networks (DBNs)
  • Restricted Boltzmann Machines( RBMs)
  • Autoencoders

Technical Skills - Machine learning & deep learning

• Multilayer Perceptrons (MLPs)
• Convolutional Neural Networks (CNNs)
• Long Short Term Memory Networks (LSTMs)
• Recurrent Neural Networks (RNNs)
• Generative Adversarial Networks (GANs)
• Autoencoders
• Attention
• Transformers

Technical Skills - Data wrangling & preparation

• It includes two concepts such as Data Cleaning and Feature Engineering.
• Convert the raw data into a format that is convenient for the consumption of data.

Technical Skills - Model deployment & Production

• Develop and create models.
• Testing of models.
• Optimize and test code, then clean and test again.
• Prepare for container deployment
• Plan for continuous monitoring and maintenance
• Must know: Git
• Have some knowledge: Docker, Kubernetes, AWS

Technical Skills - Data Visualization

• Data visualization helps data scientists perform complex data analysis, recognizing patterns, and understanding datasets.
• Need to know how to use and understand:
  • Frequency Table
  • Bar Graph
  • Scatter Plots
  • Histograms
  • Heatmaps
• Need to know at least one data visualization tool.
  • For Python need to know Matplotlib.
  • For R need to know ggplot2.

Personal Internet Profile

• What do people see when they look you up?
• Create a profile that reflects your job interests
• For AI Jobs:
  • GitHub: repository + README + website
  • Blog Posts: Medium
  • LinkedIn

Projects

• Post all your projects on GitHub.
• Create new projects (research/class/personal).
• Keep each projects documented and clean.
• Aim to have each project end-to-end (from research to deployment).
• Write about your projects:
  • Research papers
  • Blog posts - Medium
  • Personal Website

Contributions

• Be active in your GitHub projects.
• Blog posts on Medium and personal website.
• Contribute to large open source projects (any kind of contribution).
• Presence in AI events (attendant or presenter).

Visibility

• Make yourself visible online.
• Follow well known people in AI related to your desired job area.
• Document all your important activities on your personal website and LinkedIn.
• Share any new event you attend / blog post / project everywhere online (LinkedIn, Twitter, Medium etc.)
• Sell yourself but have strong proof of your skills and knowledge.

How I got my AI Jobs?

• State Farm
  • Data Scientist
  • Machine Learning Engineer
  • Research Scientist
• Huntington
  • Machine Learning Engineer
• Amazon
  • Research Scientist
• Bonus:
  • How I got to be a technical reviewer for one of the first Transformers book? (Transformers for Natural Language Processing 1st and 2nd editions, by Denis Rothman)

At the end of the day what really matters

• Adaptable.
• Passionate.
• Curious.
• Willing to change and learn new things.
• Put in the time and effort.
• Persistent.

Conclusions

• You learned a little bit about me.
• Learned more about useful skills in AI jobs.
• Learned more about expectations of AI jobs.
• Got a few tips on how to be more successful in AI.

Questions 🖐

• What did you learn today?
• What motivated you in this presentation?
• Do you have any questions?

Contact

GitHub: gmihaila

Website: gmihaila.github.io

LinkedIn: mihailageorge

Medium: @gmihaila

Email: georgemihaila@my.unt.edu.com

Resources

how-much-math-data-science
The-most-in-demand-data-science-skills-you-need
top-programming-languages-for-data-scientists-in-2022
data-preparation-process-preprocessing-and-data-wrangling
what-does-it-take-to-deploy-ml-models-in-production
deep-learning-algorithm