Data Science & Machine Learning Master Class

1.1. Foundations of Data Science

1.2. Machine Learning Fundamentals and Data Scientist Skills

2.1. Descriptive Statistics

2.2. Probability Fundamentals

2.3. Probability Distributions and Statistical Measures

2.4. Linear Algebra for Data Science

2.5. Calculus Fundamentals

3.1. Python Fundamentals

3.2. Python Programming Constructs

3.3. File Handling and Object-Oriented Programming

3.4. Python Libraries

4.1. Introduction to SQL and Data Retrieval

4.2. Data Grouping, Sorting and Analysis

4.3. Data Manipulation and Advanced SQL Queries

5.1. Data Handling Fundamentals

5.2. Data Preprocessing and Transformation

6.1. Introduction to Exploratory Data Analysis and Data Visualization

6.2. Data Quality Analysis and Feature Engineering

7.1. Introduction to Machine Learning

7.2. Supervised Learning Algorithms

7.3. Unsupervised Learning

7.4. Model Evaluation

7.5. Reinforcement Learning

8.1. Sampling Techniques and Statistical Estimation

8.2. Hypothesis Testing and Statistical Inference

8.3. Maximum Likelihood Estimation and A/B Testing

8.4. Advanced Linear Algebra for Machine Learning

8.5. Optimization Techniques in Machine Learning

8.6. Multivariable Calculus for Machine Learning

8.7. Advanced Mathematical Concepts for Machine Learning

9.1. Advanced Supervised Learning

9.2. Advanced Unsupervised Learning

9.3. Model Tuning & Optimization

10.1. Time Series Fundamentals and Statistical Forecasting Models

10.2. Advanced Time Series Forecasting Techniques

11.1. NLP Fundamentals

11.2. Text Representation Techniques

11.3. Advanced NLP Applications

12.1. Data Engineering Fundamentals and Data Pipelines

12.2. Data Storage, Processing, and Big Data Technologies

12.3. Modern Data Engineering Platforms and Data Governance

13.1. Foundations of Explainable AI and Model Interpretation

13.2. Advanced Explainable AI Techniques and Ethical AI

14.1. Deep Learning Fundamentals

14.2. Training and Regularization Techniques

14.3. Deep Learning Architectures

14.4. Deep Learning Frameworks and Applications

15.1. Transformer Foundations and BERT

15.2. Generative Language Models and Transformer Fine-Tuning

15.3. Advanced NLP Applications and Sequence Modeling

15.4. Attention Mechanisms, Transformers, and Transfer Learning

16.1. Computer Vision Fundamentals and Image Classification

16.2. Object Detection and Advanced Computer Vision Techniques

16.3. Deep Learning Models for Computer Vision

17.1. Big Data Fundamentals and Hadoop Ecosystem

17.2. Apache Spark and PySpark for Distributed Data Processing

17.3. Real-Time Data Streaming and Distributed Computing

17.4. Modern Big Data Storage and Processing Technologies

18.1. MLOps Fundamentals and Model Deployment

18.2. Model Serving, Containerization, and Orchestration

18.3. CI/CD Pipelines, MLflow, and Model Monitoring

18.4. Model Monitoring, Drift Detection, and Version Management

18.5. Production MLOps Best Practices and Continuous Improvement

19.1. Cloud Fundamentals and Amazon SageMaker

19.2. Enterprise Cloud Machine Learning Platforms

19.3. Cloud Storage and Serverless Machine Learning

19.4. Cloud MLOps, Security, and Production Deployment

20.1. AI Research Foundations: Reinforcement Learning and Graph Neural Networks

20.2. Self-Supervised Learning and Generative AI

20.3. Diffusion Models and AI Content Generation

20.4. Large Language Models and Emerging AI Technologies