For everyone including jobseekers and career changers - with Career Guidance Modules - Learn from Industry Leaders

What you'll learn:
Importance of AI
AI Fundamentals
AI Applications - NLP, Speech recognition, Predictive analysis, Chatbots, Facial recognition, Expert systems, Gaming, Healthcare, etc
Machine Learning Essentials
Importance of Data and Data Processing
Model Training
Ethical Considerations
Advancements in AI
Supervised Learning Models: Classification
Various types of classification models for binary and multi-class classification
Performance metrics: Confusion Matrix, F1, Precision, Recall, Accuracy - which one to use in which case, with examples
Unsupervised Learning: Clustering, performance metrics
Model Improvement Methods
What does model improvement mean? What are the different options at our disposal and when to use them?
Feature Selection and Dimensionality reduction methods: Lasso Regression, Ridge Regression, PCA
Hyperparameter Tuning: Various methods, deep dive into Grid-search and Random-search
Cross-Validation
Application to hyperparameter tuning

Requirements:
No previous knowledge required! Suitable for candidates from any domain.