AI+ Developer

Text Preprocessing and Representation Text Classification Named Entity Recognition (NER) Question Answering (QA)

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

Cloud Computing for AI Cloud-Based Machine Learning Services

Understanding LLMs Text Generation and Translation Question Answering and Knowledge Extraction

Neuro-Symbolic AI Explainable AI (XAI) Federated Learning Meta-Learning and Few-Shot Learning

Communicating AI Projects Documenting AI Systems Ethical Considerations

Text Preprocessing and Representation Text Classification Named Entity Recognition (NER) Question Answering (QA)

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

Cloud Computing for AI Cloud-Based Machine Learning Services

Understanding LLMs Text Generation and Translation Question Answering and Knowledge Extraction

Neuro-Symbolic AI Explainable AI (XAI) Federated Learning Meta-Learning and Few-Shot Learning

Communicating AI Projects Documenting AI Systems Ethical Considerations

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

Linear Algebra Calculus Probability and Statistics Discrete Mathematics

Python Fundamentals Python Libraries

Introduction to Machine Learning Supervised Machine Learning Algorithms Unsupervised Machine Learning Algorithms Model Evaluation and Selection

Neural Networks Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs)

Image Processing Basics Object Detection Image Segmentation Generative Adversarial Networks (GANs)

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

Cloud Computing for AI Cloud-Based Machine Learning Services

Understanding LLMs Text Generation and Translation Question Answering and Knowledge Extraction

Neuro-Symbolic AI Explainable AI (XAI) Federated Learning Meta-Learning and Few-Shot Learning

Communicating AI Projects Documenting AI Systems Ethical Considerations

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

Linear Algebra Calculus Probability and Statistics Discrete Mathematics

Python Fundamentals Python Libraries

Introduction to Machine Learning Supervised Machine Learning Algorithms Unsupervised Machine Learning Algorithms Model Evaluation and Selection

Neural Networks Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs)

Image Processing Basics Object Detection Image Segmentation Generative Adversarial Networks (GANs)

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

Cloud Computing for AI Cloud-Based Machine Learning Services

Understanding LLMs Text Generation and Translation Question Answering and Knowledge Extraction

Neuro-Symbolic AI Explainable AI (XAI) Federated Learning Meta-Learning and Few-Shot Learning

Communicating AI Projects Documenting AI Systems Ethical Considerations

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

Machine Learning Basics

Deep Learning Basics

Reinforcement Learning Basics

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

Explanation of the Topics Data Cleaning Data Transformation Feature Engineering Feature Selection Data Reduction

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

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

Filter Methods Wrapper Methods Embedded Methods

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

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

Introduction to Supervised Learning Algorithms Common Tasks in Supervised Learning Popular Algorithms

Introduction to Unsupervised Learning Algorithms Types of Unsupervised Learning Algorithms

AdaBoost Algorithm Explanation XGBoost Algorithm Explanation CatBoost Algorithm Explanation GradiendBoost Algorithm Explanation

Sampling Methods Algorithm Modifications

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

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

Neural Networks Activation Function Loss Functions Optimizers Regularization Forward Propagation Backward Propagation Hyperparameter Tuning in Neural Networks

NLP Computer Vision Reinforcement Learning

LLMs-Text LLMs - Text to Image

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

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

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

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

Linear Algebra Matrix Operations Vector Spaces Eigenvectors and Eigenvalues Linear Transformations in Python Matrix Factorization Introduction to Tensor Operations in Linear Algebra

Differential Calculus Integration in Python

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

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

Course Objectives

• Understand the essentials of AI and gain proficiency in Python for AI development.
• Learn the architectures of deep learning and neural networks and their applications.
• Explore various specialized AI applications across different industry sectors.
• Acquire knowledge about large language models, including GPT, and master the art of prompt engineering.
• Develop the skills to utilize AI tools, manage AI operations, and effectively deploy AI models in real-world scenarios.