1.1 Data Preprocessing
– Handling Imbalanced Data
– Feature Scaling and Normalization

1.2 Data Transformation and Reduction
– Principal Component Analysis (PCA)
– Feature Engineering

1.3 Data Visualization and Exploratory Data Analysis (EDA)
– Techniques for EDA
– Visualizing Distributions and Relationships

1.4 Classification and Clustering
– Supervised vs Unsupervised Learning
– Key Algorithms and Use Cases

1.5 Regression Models
– Linear and Non-linear Regression
– Model Evaluation Metrics

1.6 Model Tuning and Performance Analytics
– Hyperparameter Tuning
– Cross-validation Techniques

2.1 Neural Networks
– Activation Functions and Loss Functions

2.2 Advanced Neural Networks
– Deep Neural Networks (DNN)
– Optimization Techniques

2.3 Convolutional Neural Networks (CNN)
– Architecture of CNNs
– Applications in Image Processing

2.4 Recurrent Neural Networks (RNN)
– Understanding RNNs
– Time-Series Analysis

2.5 Long Short-Term Memory (LSTM)
– LSTM Architecture
– Applications in Sequential Data

2.6 Generative Adversarial Networks (GAN)
– GAN Architecture
– Training and Applications

2.7 Restricted Boltzmann Machines (RBM)
– Fundamentals of RBM
– Applications in Dimensionality Reduction

2.8 Autoencoders
– Architecture and Applications
– Variational Autoencoders

2.9 Transfer Learning
– Concept of Transfer Learning
– Practical Applications

2.10 Reinforcement Learning
– Basics of Reinforcement Learning
– Key Algorithms

3.1 Numpy
– Introduction to Numpy
– Array operations and manipulations

3.2 Pandas
– DataFrames and Series
– Data Cleaning and Transformation
– Handling Missing Data

3.3 Matplotlib
– Basic plotting with Matplotlib
– Customizing plots

3.4 Seaborn
– Statistical Data Visualization
– Advanced plotting with Seaborn

4.1 PyTorch
– Tensors and Automatic Differentiation

5.1 Text Generation and Processing
– Natural Language Processing (NLP)
– Text Analytics with NLP Models

5.2 Speech Analytics
– Speech Recognition and Synthesis
– Applications in Voice Assistants

5.3 Generating Images and Videos
– Techniques for Image Generation
– Video Synthesis

5.4 Code Generation
– Generating Code with AI
– Use Cases in Software Development

5.5 Simulation and Data Synthesis
– Simulating Real-world Scenarios
– Generating Synthetic Data for Training

6.1 Model Deployment Strategies
– Deployment Techniques
– Serving Models in Production

6.2 Scalability and Performance
– Scaling Machine Learning Models
– Optimizing for Performance

6.3 Association Mining and Recommendation Systems
– Building Recommendation Engines
– Techniques for Association Rule Mining

7.1 AWS AI Services
– Amazon Rekognition (Image and Video Analysis)
– Amazon Comprehend (Natural Language Processing)
– Amazon Transcribe (Speech-to-Text)
– Amazon Forecast (Time Series Forecasting)
– Amazon Personalize (Personalization and Recommendation)
– Amazon Lex (Conversational Interfaces)
– Amazon Fraud Detector (Fraud Detection)
– Amazon Translate (Language Translation)
– Amazon Polly (Text-to-Speech)

8.1 Pipeline as Code
– CI/CD Pipelines
– Automation Tools

8.2 Containerization with Docker and Podman
– Containerization Concepts
– Using Docker and Podman for AI Ops

8.3 Orchestration with Kubernetes and Openshift
– Kubernetes Basics
– Managing Containers with Openshift

8.4 Continuous Integration/Continuous Deployment (CI/CD) with Jenkins
– Setting up Jenkins Pipelines
– Integrating Jenkins with AI Ops