Artificial Intelligence Professional

Module 1: Introduction to Artificial Intelligence

-Overview of AI Concepts and History
-AI vs. Machine Learning vs. Deep Learning
-Key AI Applications in Various Industries
-Ethical Implications of AI
-AI Terminology and Key Concepts

Module 2: Machine Learning Basics

-Supervised Learning: Classification and Regression
-Unsupervised Learning: Clustering and Dimensionality Reduction
-Introduction to Neural Networks
-Evaluating Model Performance (Accuracy, Precision, Recall)
-Hands-on with Basic ML Algorithms

Module 3: Data Handling and Preprocessing

-Data Collection Techniques
-Data Cleaning and Preparation
-Feature Selection and Engineering
-Handling Missing Data and Outliers
-Data Normalization and Standardization

Module 4: Introduction to Neural Networks

-Understanding Neurons and Layers
-Building a Simple Neural Network
-Activation Functions and Loss Functions
-Forward and Backpropagation
-Training Neural Networks: Epochs, Batch Size, Learning Rate

Module 5: AI Tools and Frameworks

-Overview of AI Development Tools (TensorFlow, PyTorch)
-Introduction to Jupyter Notebooks
-Setting Up a Python Environment for AI Development
-Introduction to Keras for Rapid AI Prototyping
-Hands-on with Pre-trained AI Models

Module 1: Introduction to Machine Learning

-Machine Learning Concepts and Terminologies
-Overview of Supervised vs. Unsupervised Learning
-ML Workflow and Lifecycle
-Model Selection and Evaluation
-Understanding Bias-Variance Tradeoff

Module 2: Data Preprocessing

-Techniques for Data Cleaning and Transformation
-Feature Scaling (Normalization, Standardization)
-Encoding Categorical Variables (One-Hot Encoding, Label Encoding)
-Handling Missing Data (Imputation Techniques)
-Data Splitting Strategies (Train-Test Split, Cross-Validation)

Module 3: Supervised Learning Algorithms

-Linear Regression: Implementation and Applications
-Decision Trees and Random Forests: Concepts and Use Cases
-Support Vector Machines: Theory and Implementation
-K-Nearest Neighbors: Algorithm and Applications
-Model Evaluation Metrics (Confusion Matrix, ROC Curve)

Module 4: Unsupervised Learning Algorithms

-K-Means Clustering: Theory and Implementation
-Hierarchical Clustering: Concepts and Methods
-Principal Component Analysis (PCA): Theory and Applications
-Anomaly Detection Techniques
-Dimensionality Reduction Methods

Module 5: Model Evaluation and Validation

-Cross-Validation Techniques (K-Fold, Leave-One-Out)
-Performance Metrics (Precision, Recall, F1 Score, ROC Curve)
-Hyperparameter Tuning and Grid Search
-Model Comparison and Selection
-Handling Overfitting and Underfitting

Module 6: Introduction to Neural Networks

-Basic Neural Network Architecture
-Activation Functions (Sigmoid, ReLU, Tanh)
-Forward Propagation and Backpropagation
-Training Neural Networks (Epochs, Batch Size, Learning Rate)
-Regularization Techniques (Dropout, L2 Regularization)

Module 7: Machine Learning Tools and Environments

-Using Scikit-Learn for ML Models
-TensorFlow and Keras for Deep Learning
-Model Deployment Techniques (Flask APIs, Docker)
-Cloud Platforms for ML (AWS, Google Cloud, Azure)
-Introduction to AutoML Tools

Module 1: Introduction to Deep Learning

-Overview of Deep Learning and Neural Networks
-Key Terminologies and Concepts (Activation Functions, Backpropagation)
-Comparison with Traditional Machine Learning Techniques
-Deep Learning Use Cases and Applications
-Tools and Frameworks for Deep Learning

Module 2: Neural Networks Fundamentals

-Basic Neural Network Architecture (Input, Hidden, Output Layers)
-Activation Functions (Sigmoid, ReLU, Tanh)
-Training Neural Networks: Gradient Descent, Backpropagation
-Regularization Techniques (Dropout, L2 Regularization)
-Performance Metrics and Loss Functions

Module 3: Convolutional Neural Networks (CNNs)

-CNN Architecture and Layers (Convolutional, Pooling, Fully Connected)
-Applications in Image Classification, Object Detection, and Segmentation
-Transfer Learning with Pre-trained CNN Models
-Techniques for Improving CNN Performance (Data Augmentation, Fine-Tuning)
-Case Studies and Applications

Module 4: Recurrent Neural Networks (RNNs)

-RNN Architecture and Components (Cells, Gates)
-Applications in Sequence Prediction and Natural Language Processing
-Long Short-Term Memory (LSTM) Networks and Gated Recurrent Units (GRUs)
-Challenges in Training RNNs (Vanishing and Exploding Gradients)
-Use Cases and Examples

Module 5: Advanced Deep Learning Models

-Generative Adversarial Networks (GANs): Architecture, Training, and Applications
-Attention Mechanisms and Transformers (BERT, GPT)
-Reinforcement Learning Basics and Applications
-Techniques for Scaling Deep Learning Models (Distributed Training, Multi-GPU)
-Future Trends in Deep Learning

Module 1: Introduction to Computer Vision

-Overview of Computer Vision and Its Applications
-Basic Image Processing Techniques (Filtering, Edge Detection)
-Image Representation and Histograms
-Understanding Color Spaces and Transformations
-Hands-on with OpenCV Basics

Module 2: Feature Detection and Image Segmentation

-Keypoint Detection and Matching (SIFT, SURF)
-Image Segmentation Techniques (Thresholding, Region-based)
-Contour Detection and Shape Analysis
-Object Detection Algorithms (HOG, Haar Cascades)
-Implementing Image Segmentation with OpenCV

Module 3: Deep Learning for Computer Vision

-Convolutional Neural Networks (CNNs) Fundamentals
-Building a Simple CNN Model for Image Classification
-Transfer Learning with Pre-trained Models (VGG, ResNet)
-Object Detection with YOLO and SSD
-Implementing Semantic Segmentation with U-Net

Module 4: Advanced Computer Vision Techniques

-Image Generation with GANs
-Video Analysis and Action Recognition
-Face Detection and Recognition Techniques
-3D Vision and Augmented Reality Applications
-Real-world Applications of Computer Vision

Module 5: Deploying Computer Vision Models

-Model Optimization for Deployment (Quantization, Pruning)
-Deploying on Edge Devices (Raspberry Pi, NVIDIA Jetson)
-Building and Serving CV Models as APIs
-Integrating CV Models with IoT and Robotics
-Case Studies on Computer Vision in Production

Module 1: Introduction to Data Science

-Definition, Scope, and Applications
-Data Science vs. Data Analytics vs. Business Intelligence
-Key Roles in Data Science (Data Scientist, Data Analyst, etc.)
-Data Science Lifecycle (Data Collection, Cleaning, Analysis, and Reporting)
-Tools and Technologies Used in Data Science

Module 2: Data Collection and Cleaning

-Methods of Data Collection (Surveys, APIs, Web Scraping)
-Techniques for Data Cleaning (Handling Missing Values, Outliers)
-Data Transformation (Normalization, Standardization)
-Tools for Data Cleaning (OpenRefine, Pandas)
-Data Integration (Combining Data from Multiple Sources)

Module 3: Exploratory Data Analysis (EDA)

-Univariate and Bivariate Analysis
-Data Visualization Techniques (Histograms, Scatter Plots)
-Identifying Trends and Patterns in Data
-Summary Statistics (Mean, Median, Mode, Variance)
-Correlation Analysis and Heatmaps

Module 4: Statistical Foundations

-Descriptive vs. Inferential Statistics
-Probability Distributions (Normal, Binomial, Poisson)
-Statistical Tests (t-tests, Chi-square tests, ANOVA)
-Hypothesis Testing and Confidence Intervals
-Sampling Methods and Data Distribution

Module 5: Data Preparation

-Data Cleaning Best Practices
-Feature Engineering (Creating New Features from Existing Data)
-Feature Selection Techniques (Filter, Wrapper, Embedded Methods)
-Data Splitting (Training, Validation, Testing Sets)
-Handling Imbalanced Datasets (Resampling Techniques)

Module 6: Introduction to Machine Learning

-Overview of Supervised and Unsupervised Learning
-Introduction to Classification (Logistic Regression, KNN)
-Introduction to Regression (Linear Regression, Polynomial Regression)
-Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
-Basics of Model Overfitting and Underfitting

Module 7: Data Science Tools and Environments

-Python vs. R for Data Science
-Key Python Libraries (Pandas, NumPy, Scikit-Learn, Matplotlib)
-Setting Up Data Science Environments (Anaconda, Jupyter Notebook)
-Data Visualization Tools (Tableau, PowerBI)
-Collaborative Tools (GitHub, JupyterHub)

Module 1: Introduction to Python Programming

-Basic Syntax and Structure
-Data Types and Variables
-Control Flow Statements (if, for, while)
-Functions and Lambda Expressions
-Exception Handling and Debugging

Module 2: Functions and Modules

-Defining and Using Functions
-Lambda Functions and Map/Filter/Reduce
-Creating and Importing Modules
-Python Packages and Distribution
-Standard Library vs. Third-Party Libraries

Module 3: Data Structures

-Lists and Tuples: Operations and Methods
-Dictionaries: Key-Value Pairs, Methods
-Sets: Operations and Use Cases
-Stacks and Queues (Implementation and Applications)
-Understanding Complex Data Structures (Graphs, Trees)

Module 4: Object-Oriented Programming

-Basic Concepts (Classes, Objects, Inheritance)
-Encapsulation and Abstraction
-Polymorphism and Method Overriding
-Designing Class Hierarchies
-Using Magic Methods and Operator Overloading

Module 5: File Handling and I/O Operations

-Reading from and Writing to Text Files
-Working with Binary Files
-Handling CSV and JSON Formats
-File and Directory Operations
-Using Context Managers for File Operations

Module 6: Libraries and Frameworks

-Data Analysis with Pandas
-Data Visualization with Matplotlib and Seaborn
-Web Development Frameworks (Flask, Django)
-Working with APIs (Requests, RESTful APIs)
-GUI Development (Tkinter, PyQt)

Module 7: Testing and Debugging

-Writing Unit Tests with Unittest and Pytest
-Debugging Techniques (Using PDB, IDE Debuggers)
-Best Practices for Writing Testable Code
-Continuous Integration and Deployment (CI/CD) Tools
-Code Quality and Documentation Standards

Module 1: Introduction to PowerBI

-Overview of PowerBI Desktop, PowerBI Service, and PowerBI Mobile
-Connecting to Various Data Sources (Databases, Cloud Services, Excel)
-Navigating PowerBI Interface and Features
-Data Loading and Initial Setup
-Key Concepts of PowerBI Reports and Dashboards

Module 2: Data Transformation and Modeling

-Using Power Query Editor for Data Cleaning and Transformation
-Building and Managing Data Models (Relationships, Calculations)
-Creating Calculated Columns, Measures, and KPIs
-Advanced Data Modeling Techniques (Data Aggregation, Hierarchies)
-Performance Optimization in Data Models

Module 3: Creating Visualizations

-Building Basic Visualizations (Tables, Charts, Maps)
-Customizing Visual Elements and Using Themes
-Interactive Visualizations and Report Features (Slicers, Filters, Drillthroughs)
-Design Principles for Effective Reports and Dashboards
-Creating and Managing Bookmarks and Tooltips

Module 4: Advanced Data Analysis

-Using DAX (Data Analysis Expressions) for Complex Calculations
-Advanced Report Features (Dynamic Titles, Conditional Formatting)
-Trend Analysis, Forecasting, and What-If Scenarios
-PowerBI Service Features (Sharing, Collaboration, Data Refresh)
-Integration with Other Microsoft Tools (Excel, Azure)

Module 5: PowerBI Administration and Security

-Managing Workspaces, Reports, and Data Access
-Configuring Row-Level Security and Access Controls
-Monitoring and Managing Report Performance
-Automating Data Refresh and Scheduling
-Data Compliance and Security Best Practices

Module 1: Introduction to Tableau

-Overview of Tableau Desktop, Tableau Server, Tableau Online
-Connecting to Various Data Sources (Excel, SQL, Web Data Connectors)
-Navigating Tableau Interface and Basic Features
-Data Importing and Preparation
-Key Concepts of Tableau Dashboards and Stories

Module 2: Data Preparation and Cleaning

-Using Tableau Prep for Data Cleaning and Transformation
-Handling Missing Values and Outliers
-Data Aggregation and Filtering Techniques
-Creating Custom Calculations and Fields
-Data Joins and Blends

Module 3: Creating Visualizations

-Building Basic Visualizations (Bar Charts, Line Graphs, Pie Charts)
-Advanced Visualization Techniques (Heatmaps, Tree Maps, Bullet Graphs)
-Customizing Visualizations and Applying Formatting
-Interactive Elements (Filters, Actions, Dashboard Controls)
-Best Practices for Data Visualization Design

Module 4: Dashboard and Storytelling

-Designing Interactive Dashboards (Layout, Design Principles)
-Using Dashboards for Data Exploration and Insights
-Creating and Sharing Stories with Tableau
-Advanced Dashboard Features (Parameter Controls, Dashboard Actions)
-Collaboration and Sharing Options in Tableau Server/Online

Module 5: Data Analysis and Insights

-Using Tableau Calculations for Advanced Analysis
-Performing Trend Analysis, Forecasting, and Predictive Modeling
-Integrating Tableau with External Data Sources (APIs, SQL)
-Data Performance Optimization Techniques
-Case Studies and Real-world Applications

Module 1: Advanced Machine Learning Algorithms

-Ensemble Methods (Boosting, Bagging, Stacking)
-Support Vector Machines (SVMs) and Kernel Methods
-Advanced Regression Techniques (Ridge, Lasso, ElasticNet)
-Model Evaluation and Selection (Cross-Validation, Grid Search)
-Real-world Applications and Case Studies

Module 2: Deep Learning Techniques

-Advanced Neural Network Architectures (ResNets, DenseNets)
-Transfer Learning and Fine-Tuning Pre-trained Models
-Hyperparameter Tuning and Optimization
-Advanced Convolutional Networks (Segmentation Networks, Object Detection)
-Implementing Complex Models with TensorFlow and PyTorch

Module 3: Natural Language Processing (NLP)

-Advanced NLP Techniques (Word Embeddings, Attention Mechanisms)
-Implementing Transformer Models (BERT, GPT)
-Text Generation and Summarization
-Named Entity Recognition (NER) and Question Answering
-Real-world NLP Applications and Case Studies

Module 4: Reinforcement Learning

-Reinforcement Learning Basics (Markov Decision Processes, Q-Learning)
-Policy Gradient Methods and Actor-Critic Algorithms
-Deep Reinforcement Learning Techniques
-Implementing RL Models for Game Playing and Robotics
-Evaluating and Improving RL Models

Module 5: Machine Learning in Production

-Model Deployment Techniques (Serving, APIs)
-Monitoring and Maintenance of ML Models
-Handling Model Drift and Retraining
-Scalability and Performance Optimization
-Best Practices for Production-Ready Machine Learning Systems

Module 1: Advanced AI Concepts

-Deep Learning Architectures (CNNs, RNNs)
-Transfer Learning and Pre-trained Models
-Natural Language Processing (NLP) Overview
-Reinforcement Learning Basics
-Real-world AI Case Studies

Module 2: Neural Network Architectures

-Convolutional Neural Networks (CNNs) for Image Processing
-Recurrent Neural Networks (RNNs) and LSTMs for Sequence Data
-Generative Adversarial Networks (GANs)
-Attention Mechanisms and Transformer Models
-Advanced Model Optimization Techniques

Module 3: Natural Language Processing (NLP)

-Text Preprocessing Techniques (Tokenization, Lemmatization)
-Implementing Word Embeddings (Word2Vec, GloVe)
-Sequence Modeling with RNNs and LSTMs
-Introduction to Transformer Models (BERT, GPT)
-Real-world NLP Applications

Module 4: AI in Production

-Deploying AI Models in Production (APIs, Microservices)
-Monitoring and Maintaining AI Systems
-Handling Model Drift and Retraining
-Scalability Challenges in AI Systems
-Best Practices for Production AI Deployment

Module 5: Ethics and Governance in AI

-Understanding AI Bias and Fairness
-Privacy Concerns in AI Systems
-AI Governance and Compliance
-Implementing Ethical AI Solutions
-Case Studies on AI Ethics and Failures