AI+ Developer

– Natural Language Processing
• Text Preprocessing and Representation
• Text Classification
• Named Entity Recognition (NER)
• Question Answering (QA)

– Reinforcement Learning
• Introduction to Reinforcement Learning
• Q-Learning and Deep Q-Networks (DQNs)
• Policy Gradient Methods

– Cloud Computing in AI Development
• Cloud Computing for AI
• Cloud-Based Machine Learning Services

– Large Language Models
• Understanding LLMs
• Text Generation and Translation
• Question Answering and Knowledge Extraction

– Cutting-Edge AI Research
• Neuro-Symbolic AI
• Explainable AI (XAI)
• Federated Learning
• Meta-Learning and Few-Shot Learning

– AI Communication and Documentation
• Communicating AI Projects
• Documenting AI Systems
• Ethical Considerations

– Foundations of Artificial Intelligence
• Introduction to AI
• Types of Artificial Intelligence
• Branches of Artificial Intelligence
• Applications and Business Use Cases

– Mathematical Concepts for AI
• Linear Algebra
• Calculus
• Probability and Statistics
• Discrete Mathematics

– Python for Developer
• Python Fundamentals
• Python Libraries

– Mastering Machine Learning
• Introduction to Machine Learning
• Supervised Machine Learning Algorithms
• Unsupervised Machine Learning Algorithms
• Model Evaluation and Selection

– Deep Learning
• Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)

– Computer Vision
• Image Processing Basics
• Object Detection
• Image Segmentation
• Generative Adversarial Networks (GANs)

– Optimization Techniques in Data Science
• Introduction to Optimization in Data Science
• Gradient Descent
• Stochastic Gradient Descent
• Adaptive Learning Rate Methods

– Introduction to Machine Learning
• Machine Learning Basics

– Introduction to Deep Learning
• Deep Learning Basics

– Introduction to Reinforcement Learning
• Reinforcement Learning Basics

– Evaluation Metrics
• Introduction to Evaluation Metrics in Machine Learning
• Classification Metrics
• Regression Metrics
• Importance of Multiple Metrics
• Choosing Metrics Based on Business Context
• Evaluating Metrics on Test Set

– Data Pre-Processing
• Explanation of the Topics
• Data Cleaning
• Data Transformation
• Feature Engineering
• Feature Selection
• Data Reduction

– Exploratory Data Analysis (EDA) in Python
• Introduction to EDA in Python
• Importing and Loading Data
• Data Cleaning
• Univariate Analysis
• Bivariate and Multivariate Analysis
• Data Transformations and Encodings
• Identifying Outliers and Anomalies
• Tools for EDA in Python
• The Iterative Nature of EDA

– Feature Engineering
• Introduction to Feature Engineering
• Feature Creation
• Feature Selection
• Feature Extraction
• Feature Scaling
• Missing Value Imputation
• Discretization
• Feature Encoding

– Feature Selection
• Filter Methods
• Wrapper Methods
• Embedded Methods

– Dimensionality Reduction
• Introduction to Dimensionality Reduction
• Problems with High-Dimensional Data
• Benefits of Dimensionality Reduction
• Common Techniques
• Key Takeaways and Best Practices

– Data Visualization
• Introduction to Data Visualization
• Types of Data Visualization
• Categories of Visualizations
• Popular Types of Visualizations
• Key Takeaways and Best Practices

– Supervised Machine Learning Algorithms
• Introduction to Supervised Learning Algorithms
• Common Tasks in Supervised Learning
• Popular Algorithms

– Unsupervised Machine Learning Algorithms
• Introduction to Unsupervised Learning Algorithms
• Types of Unsupervised Learning Algorithms

– Boosting Algorithms
• AdaBoost Algorithm Explanation
• XGBoost Algorithm Explanation
• CatBoost Algorithm Explanation
• GradientBoost Algorithm Explanation

– Working with Imbalanced Data
• Sampling Methods
• Algorithm Modifications

– Hyperparameter Tuning
• Introduction to Hyperparameters
• Hyperparameter Tuning Techniques
• Challenges in Hyperparameter Tuning
• Strategies for Efficient Tuning
• Tools for Hyperparameter Tuning

– Timeseries
• Introduction to Time Series Data
• Key Aspects of Time Series Analysis
• Stationarity and Autocorrelation
• Time Series Forecasting
• Time Series Models
• Visualization in Time Series Analysis
• Key Takeaways and Best Practices

– Deep Learning
• Neural Networks
• Activation Function
• Loss Functions
• Optimizers
• Regularization
• Forward Propagation
• Backward Propagation
• Hyperparameter Tuning in Neural Networks

– Specialization
• NLP
• Computer Vision
• Reinforcement Learning

– GenAI
• LLMs-Text
• LLMs – Text to Image

– Explainable AI
• Explanation of the Topics
• Explainable Modeling
• Model-Agnostic Methods
• Interactive Explanations
• Explainable Deep Learning
• Visual Explanations
• Natural Language Explanations

– Model Deployment
• What is Model Deployment
• Key Steps in Deploying a Model
• Challenges with Model Deployment
• Best Practices

– Python Basics
• Data Types
• Variables and Assignment
• Operators
• Control Flow
• Functions and Arguments
• Strings and Methods
• Data Structures
• Modules and Importing
• File I/O
• Exceptions and Error Handling

– Python Advanced
• Object-Oriented Programming
• Decorators
• Generators and Iterators
• Lambda Functions
• Regular Expressions
• Debugging and Testing
• Multi-Processing & Multi-Threading
• Essential Libraries for Data Science
• Working with Databases
• API Development
• Package Creation and Distribution
• Performance Optimization and Profiling
• Design Patterns

– Mathematics for Machine Learning
• Linear Algebra
• Matrix Operations
• Vector Spaces
• Eigenvectors and Eigenvalues
• Linear Transformations in Python
• Matrix Factorization
• Introduction to Tensor Operations in Linear Algebra

– Calculus
• Differential Calculus
• Integration in Python

– Probability for Data Science
• Probability Basics
• Calculating Basic Probabilities
• Probability Distributions (Normal, Binomial, Poisson)
• Conditional Probability
• Monte Carlo Simulation
• Central Limit Theorem
• Statistical Inference in Probability
• Probability in Machine Learning Algorithms
• Decision Making Under Uncertainty
• Real-world Applications of Probability in Data Science

– Statistics for Data Science
• Introduction to Statistics for Data Science
• Descriptive Statistics
• Probability and Distributions
• Statistical Inference