Machine Learning fundamentals
Deep Learning basics
Natural Language Processing (NLP)
Computer Vision fundamentals
Reinforcement Learning concepts
Supervised vs Unsupervised learning
Model evaluation metrics
Feature engineering concepts
Training, validation, and testing pipelines
Bias and fairness in AI models
Explainable AI (XAI)
Data analysis and interpretation
Data cleaning and preprocessing
Exploratory Data Analysis (EDA)
Statistical modeling basics
A/B testing frameworks
Experiment design
Data visualization
Data pipeline understanding
Structured vs unstructured data management
SQL
Python for data analysis
Excel / Google Sheets
Tableau / Power BI
Jupyter Notebook
Python fundamentals
APIs and RESTful services
JSON and data formats
Version control (Git, GitHub)
Software development lifecycle (SDLC)
Machine Learning fundamentals
Deep Learning basics
Natural Language Processing (NLP)
Computer Vision fundamentals
Reinforcement Learning concepts
Supervised vs Unsupervised learning
Model evaluation metrics
Feature engineering concepts
Training, validation, and testing pipelines
Bias and fairness in AI models
Explainable AI (XAI)
Data analysis and interpretation
Data cleaning and preprocessing
Exploratory Data Analysis (EDA)
Statistical modeling basics
A/B testing frameworks
Experiment design
Data visualization
Data pipeline understanding
Structured vs unstructured data management
SQL
Python for data analysis
Excel / Google Sheets
Tableau / Power BI
Jupyter Notebook
Python fundamentals
APIs and RESTful services
JSON and data formats
Version control (Git, GitHub)
Software development lifecycle (SDLC)
Machine Learning fundamentals
Deep Learning basics
Natural Language Processing (NLP)
Computer Vision fundamentals
Reinforcement Learning concepts
Supervised vs Unsupervised learning
Model evaluation metrics
Feature engineering concepts
Training, validation, and testing pipelines
Bias and fairness in AI models
Explainable AI (XAI)
Data analysis and interpretation
Data cleaning and preprocessing
Exploratory Data Analysis (EDA)
Statistical modeling basics
A/B testing frameworks
Experiment design
Data visualization
Data pipeline understanding
Structured vs unstructured data management
SQL
Python for data analysis
Excel / Google Sheets
Tableau / Power BI
Jupyter Notebook
Python fundamentals
APIs and RESTful services
JSON and data formats
Version control (Git, GitHub)
Software development lifecycle (SDLC)