Machine Learning and Artificial Intelligence

1. Statistics Essentials
   1. Exploratory Data Analysis
   2. Inferential Statistics
   3. Hypothesis Testing
2. Machine Learning 1
   1. Linear Regression
   2. Logistic Regression
   3. Naive Bayes
   4. Model Selection
3. Machine Learning 2
   1. Advanced Regression
   2. Support Vector Machines
   3. Tree Models
   4. Boosting
   5. Model Selection - Practical Considerations
   6. Unsupervised Learning: Clustering
   7. Unsupervised Learning: Principal Component Analysis
   8. Time Series Forecasting
4. Deep Learning
   1. Introduction to Neural Networks - Part 1
   2. Introduction to Neural Networks - Part 2
   3. Convolutional Neural Networks
   4. Convolutional Neural Networks - Industry Applications
   5. Recurrent Neural Networks
5. Natural language processing
   1. NLP Lexical Processing
   2. Syntactic Processing
   3. Semantic Processing I
   4. Semantic Processing II
6. NLP with MLOps
   1. Intro to AWS
   2. MLOps: Introduction
   3. MLOps: Data Life Cycle
   4. MLOps: Model Life Cycle
   5. Advanced NLP
   6. MLOps: Deployment: NLP