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Curriculum Designed by Experts

Multisoft Virtual Academy offers an advanced ML, AI, and Generative AI Prompt Engineering Corporate Training program tailored for organizations aiming to future-proof their workforce. This course is designed to equip professionals with essential knowledge of Machine Learning algorithms, AI frameworks, and the art of crafting effective prompts for generative AI tools like ChatGPT. Through interactive sessions, real-time case studies, and expert mentoring, your team will gain practical skills to implement AI-driven solutions across industries. Upskill your employees with cutting-edge AI expertise and stay ahead in the competitive digital landscape.
ML, AI, Generative AI Prompt Engineering Training is a specialized course that teaches participants how to design effective prompts for AI models. It covers the fundamentals of Machine Learning, Artificial Intelligence, and Generative AI, enabling learners to guide model outputs, optimize interactions, and apply prompt strategies across real-world applications like content creation, automation, chatbots, and intelligent decision-making.
- AI Vs ML Vs DL Vs DS
- Types Of ML Techniques
- Supervised vs unsupervised and semi supervised and reinforcement learning
- Introduction to Artificial Intelligence
- Applications of Artificial Intelligence o AI Project Life Cycle
- AI Domains and Models o AI Ethics and Bias

- Introduction to Machine Learning
- Data Processing for Machine Learning
- Algorithms for Machine Learning
- Supervised (Regression and Classification) o Linear Regression
- Logistic Regression o kNN
- Decision Tree and Random Forest o Support Vector Machines
- Naive Bayes

- Unsupervised (Clustering, Dimensionality Reduction) o K Means Clustering
- Hierarchical Clustering

- Case Study
- Deep Learning Overview
- The Brain vs Neuron
- Introduction to Deep Learning
- Introduction to Artificial Neural Networks
- The Detailed ANN
- The Activation Functions
- How do ANNs work & learn?

- Convolutional Operation
- Relu Layers
- What is Pooling vs Flattening?
- Full Connection
- SoftMax vs Cross Entropy
- Building a real world convolutional neural network for image classification

- Recurrent neural networks rnn
- LSTMs understanding LSTMs
- Long short-term memory neural networks lstm in python

- Restricted Boltzmann Machine
- Applications of RBM
- Introduction to Autoencoders
- Autoencoders applications
- Understanding Autoencoders
- Building a Autoencoder model

- Introducing Tensorflow
- Introducing Tensorflow
- Why Tensorflow?
- What is TensorFlow?
- Tensorflow as an Interface
- Tensorflow as an environment
- Tensors
- Computation Graph
- Installing Tensorflow
- Tensorflow training
- Prepare Data
- Tensor types
- Loss and Optimization
- Running TensorFlow programs

- Tensors
- Tensorflow data types
- CPU vs GPU vs TPU
- Tensorflow methods
- Introduction to Neural Networks
- Neural Network Architecture
- Linear Regression example revisited
- The Neuron
- Neural Network Layers
- The MNIST Dataset
- Coding MNIST NN

- Deepening the network
- Images and Pixels
- How humans recognise images?
- Convolutional Neural Networks
- ConvNet Architecture
- Overfitting and Regularization
- Max Pooling and ReLU activations
- Dropout
- Strides and Zero Padding
- Coding Deep ConvNets demo
- Debugging Neural Networks
- Visualising NN using Tensorflow
- Tensorboard

- What is Generative AI?
- Why are Generative models required?
- Understanding generative models and their significance
- Generative AI v/s Discriminative Models
- Recent advancements and research in generative AI
- Generative AI end to end project lifecycle
- Key applications of generative models

- Introduction to OpenAI
- What is OpenAI API and how to generate OpenAI API key?
- Installation of OpenAI package
- Experiment in the OpenAI playground
- How to setup your local development environment?

- Customising Prompts for Specific Use Cases
- Fine-tuning Language Models for Optimal Output
- Managing Prompt Complexity and Length
- Enhancing Control and Diversity in Generated Text

- Importance of Interpretability in AI-Generated Outputs
- Techniques for Visualising and Understanding Model Responses
- Explainable AI in Prompt Engineering
- Addressing Bias and Fairness in AI-Generated Text

- Industry-Specific Applications of Generative AI
- Real-world Examples of Prompt Engineering Success Stories
- Identifying Opportunities for AI-Prompt Integration
- Challenges and Limitations in Deploying Generative AI Solutions

- Testing and Quality Control for AI-Generated Text
- Human-in-the-Loop Approaches to Validation
- Compliance and Regulatory Considerations
- Benchmarking and Performance Metrics for Prompted AI Models

- Introduction to LlamaIndex
- Difference between laingchain and LlamaIndex
- Difference between Llama and LlamaIndex
- Setup of LlamaIndex in our local env
- How to use LLMs with LlamaIndex?
- Exploring Llamahub
- How to connect with external Data?

Project#1: Loan Defaulter Prediction

Project#1: Medical Chatbot Project with Llama 2, Pinecone, Lang Chain & Deployment AWS

- Project#2: Source Code Analysis with Lang Chain, OpenAI and Chroma DB & Deployment AWS

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Choose topics, schedule and even a subject matter expert
Skilled professionals with relevant industry experience
Customized trainings to understand specific project requirements
Check performance progress and identify areas for development
Free ML, AI, Generative AI Prompt Engineering Corporate Training Assessment
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ML, AI, Generative AI Prompt Engineering Corporate Training Certification
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