Project Development and Management
Undersanding
stakeholders
Root cause
analysis
Requirement
analysis
User stories
Use cases
Design
Thinking
Managing
overwhelimg
projects
Narrowing
down the
topic
Effective
teamwork
Divide and conquer
Helpful
apps
Time Management
Overcoming
Procrastination
Practical Resources
AI Engineering
Great books
Designing Machine
Learning Systems
by Chip Huyen
Machine Learning
Systems
by Vijay Janapa Reddi
Data and Feature Engineering
Book
Feature Engineering
for Machine Learning
by Alice Zheng
Tips on Handling
Class Imbalance
Tips on
Data Augmentation
Tips on Handling
Missing Values
Feature Scaling
and Normalization
Encoding
Categorical Variables
Feature Selection
Techniques
Handling
Outliers
Exploratory
Data Analysis
Dimensionality
Reduction (PCA)
Data
Leakage
Text and NLP
Feature Engineering
Model Selection, Training, and Evaluation
Choosing the
Right Model
Train/Val/Test Splits
and Cross-Validation
Bias-Variance,
Over/Underfitting
Hyperparameter
Tuning
Regularization
Evaluation Metrics
for Classification
Evaluation Metrics
for Regression
Ensemble
Methods
Transfer Learning
and Fine-Tuning
Neural Network
Architecture Basics
Learning Curves and
Diagnosing Problems
Baseline
Models
Reproducibility in
ML Experiments
System Deployment and Maintenance
Model Serving
Basics
Containerizing
ML Models (Docker)
MLOps
Overview
CI/CD for
ML Pipelines
Model Versioning and
Experiment Tracking
Monitoring Models
in Production
Data and
Concept Drift
A/B Testing and
Canary Deployments
Model Retraining
Strategies
Scaling
Inference
Model Compression
and Optimization
Cost Management for
ML Infrastructure
Responsible AI
in Production
Practical Resources
Software Engineering
Academic Writing,
Reading Research and Presenting
How to read a
research paper
efficiently
Finding and
evaluating
credible sources
Reference
management
tools
Avoiding plagiarism
and citing
sources correctly
Writing a
literature review
Structuring a
report or thesis
(IMRaD)
Academic writing
style: clarity,
concision, tone
Writing a
strong abstract
Data visualization
for reports
and papers
Designing
effective slides
Public speaking
and presentation
delivery
Handling Q&A
and defending
your work
Giving and
receiving
peer feedback
Note-taking
systems
for research