AI Training Course Modules
Comprehensive artificial intelligence training programs for all skill levels
Beginner Level Entry Level
Foundation concepts for AI newcomers
Introduction to AI & Use Cases
Understanding AI fundamentals
Python for AI
Programming fundamentals for AI
Math for Machine Learning
Essential mathematical concepts
Data Preprocessing Basics
Preparing data for AI models
Beginner Level Pricing
Complete foundational AI training program
Intermediate Level Mid Level
Advanced machine learning techniques
Supervised & Unsupervised Learning
Core ML algorithms and techniques
Neural Networks & Deep Learning
Deep learning fundamentals
Natural Language Processing (NLP)
Text analysis and processing
AI Tools: TensorFlow, Keras, Scikit-Learn
Popular AI frameworks and libraries
Intermediate Level Pricing
Advanced machine learning program
Advanced Level Expert Level
Cutting-edge AI technologies and deployment
Generative AI (ChatGPT, DALL·E, etc.)
Latest generative AI technologies
Computer Vision & OpenCV
Image processing and analysis
Capstone Project + Resume Workshop
Portfolio development and career prep
Real-World Use Cases & Deployment
Production-ready AI solutions
Advanced Level Pricing
Expert-level AI specialization program
Complete AI Mastery
Full Stack AI Training Program
Program Fee
Flexible payment options6 Months
Program Duration
Self-paced supportedAll Levels
Comprehensive Learning
Beginner to ExpertAI Training Program Modules
Introduction to AI
Learn the basics of AI, its history, and real-world applications. Understand how AI is transforming industries.\r\n\r\n
Topics Covered:
- What is AI? (Basics for Non-Technical Users)
- What is AI? (Basics for Non-Technical Users)
- AI in Daily Life: Virtual Assistants, Smart Devices, etc.
- Benefits of Using AI in Business and Productivity
- Common AI Terminologies: NLP, Machine Learning, Automation
🛠️ Practical:
Hands-on exploration of AI-powered search engines (Google Bard, Perplexity AI, and ChatGPT).\r\n\r\n
Using AI for Content Creation
Learn how to use AI tools for generating creative content.
Topics Covered:
- AI-Powered Text Generation Tools:
- AI-Powered Text Generation Tools:
- ChatGPT, Jasper, Copy.ai, and Writesonic
- AI for Blog and Article Writing
- Generating Social Media Posts and Ads
- Proofreading and Grammar Checking with AI (Grammarly, QuillBot)
🛠️ Practical:
Create a blog post or marketing ad using AI.
Use Grammarly or QuillBot to refine content.
AI for Image and Graphic Design
Leverage AI tools for creating visuals and graphics.
Topics Covered:
- AI Image Generators: DALL·E, MidJourney, and Leonardo AI
- AI Image Generators: DALL·E, MidJourney, and Leonardo AI
- AI-Powered Photo Editing: Canva’s Magic Studio, Adobe Firefly
- Using AI for Logo and Graphic Design
- Enhancing Images with AI Tools (Remove.bg, Cleanup.pictures)
🛠️ Practical:
Create an AI-generated poster or logo.
Use Canva’s Magic Studio for quick design.
AI for Speech and Audio Tools
Use AI for speech-to-text and text-to-speech conversion.
Topics Covered:
- AI-Powered Transcription Tools: Otter.ai, Rev.ai, Descript
- AI-Powered Transcription Tools: Otter.ai, Rev.ai, Descript
- Text-to-Speech (TTS) Applications: ElevenLabs, Play.ht
- Real-Time Translation and Voice Cloning
- AI in Podcasting and Audio Editing
🛠️ Practical:
Use Otter.ai to transcribe a meeting.
Generate a professional voiceover using Play.ht.
AI for Business Productivity
Boost productivity using AI automation tools.
Topics Covered:
- AI-Powered Email and Document Writing (GrammarlyGO, Notion AI)
- AI-Powered Email and Document Writing (GrammarlyGO, Notion AI)
- Smart Scheduling with AI: Reclaim.ai, Clockwise
- Automated Task Management: Todoist with AI, Motion
- AI-Powered Presentation Tools: Tome.app, Beautiful.ai
🛠️ Practical:
Use Notion AI to generate meeting notes.
Create an AI-powered presentation using Tome.app.
AI-Powered Data Analysis and Visualization
Learn how to use AI for analyzing and visualizing data.
Topics Covered:
- Using ChatGPT for Data Analysis
- Using ChatGPT for Data Analysis
- AI-Powered Tools: Microsoft Power BI, Tableau GPT
- Excel and Google Sheets with AI Integration
- Auto-generating Reports with AI
🛠️ Practical:
Use Excel Copilot to generate insights.
Create a data visualization using Tableau GPT.
AI for Communication and Collaboration
Leverage AI in communication and team collaboration.
Topics Covered:
- AI-Powered Customer Support: Chatbots (Zendesk, Drift)
- AI-Powered Customer Support: Chatbots (Zendesk, Drift)
- AI in Virtual Meetings: Zoom AI Companion, Krisp.ai
- Automated Email Responses with Gmail AI
- Real-time Language Translation with AI
🛠️ Practical:
Use Zoom AI Companion to summarize a meeting.
Test Krisp.ai for noise cancellation.
AI for Digital Marketing and SEO
Use AI for marketing automation and analytics.
Topics Covered:
- AI-Powered Ad Creation: Adzooma, Smartly.io
- AI-Powered Ad Creation: Adzooma, Smartly.io
- AI for Social Media Scheduling: Buffer AI, Ocoya
- SEO Optimization with AI: Surfer SEO, NeuronWriter
- AI-Powered Analytics: Google Analytics 4 with AI Insights
🛠️ Practical:
Create an AI-powered ad copy using Jasper.ai.
Use Surfer SEO to optimize a blog post.
Using AI for Automation
Learn how to automate repetitive tasks using AI.
Topics Covered:
- Introduction to No-Code AI Automation Platforms: Zapier, Make
- Introduction to No-Code AI Automation Platforms: Zapier, Make
- Automating Emails, Reports, and Scheduling
- Using AI Bots for Customer Engagement
- AI-Powered Lead Generation and Follow-Ups
🛠️ Practical:
Automate email responses using Zapier.
Create an AI-powered workflow automation.
Ethical AI Usage and Best Practices
Learn about the ethical use of AI and its limitations.
Topics Covered:
- Privacy Concerns and AI Data Usage
- Privacy Concerns and AI Data Usage
- Bias and Fairness in AI Models
- Responsible AI Usage in Business
- Staying Updated with AI Trends
🛠️ Practical:
Case study on ethical AI usage.
Discussion on AI regulations and guidelines.
📘 AI Module Structure
Explore the core components of our AI training program.
Definition and History of AI
Key Concepts: AI vs. Machine Learning vs. Deep Learning
Types of AI: Narrow AI, General AI, Superintelligent AI
Key Terminologies: Algorithms, Data, Models, Training
Definition and Types of Machine Learning
Supervised Learning, Unsupervised Learning, Reinforcement Learning
Steps in a Machine Learning Workflow
Key Algorithms: Linear Regression, Decision Trees, k-Nearest Neighbors, Neural Networks
Process: Data Collection & Preparation, Feature Engineering, Model Selection & Training, Evaluation & Optimization
AI Tools and Technologies
Tools for Data Analysis
Python Libraries: Pandas, NumPy, Matplotlib
Data Visualization: Tableau, Power BI
AI Development Platforms
Google Colab, Microsoft Azure, Amazon SageMaker
Real-World Applications of AI
Industry Use Cases
Healthcare: Diagnostics, Drug Discovery
Finance: Fraud Detection, Algorithmic Trading
Retail: Personalized Recommendations, Inventory Management
Manufacturing: Predictive Maintenance, Quality Control
Ethical Considerations and Challenges
Bias in AI: Privacy Concerns
Accountability and Transparency
Regulatory Frameworks
AI Development Platforms
Google Colab, Microsoft Azure, Amazon SageMaker
Hands-On Sessions
Basic Python for AI
Writing and Running Python Code
Introduction to Jupyter Notebooks
Building a Machine Learning Model: Dataset Selection (e.g., Kaggle), Training with Scikit-learn
Deep Learning Project: Image Classification with TensorFlow, Sentiment Analysis with NLP
Assessment and Certification
Quizzes: At the end of each module
Capstone Project: Build an AI solution to a real-world problem
Certification: Awarded on successful completion
AI Development Platforms
Google Colab, Microsoft Azure, Amazon SageMaker
Deep Learning Essentials
Introduction to Neural Networks
Structure: Neurons, Layers, Activation Functions
Types of Networks: CNNs, RNNs, GANs, Transformers
Deep Learning Frameworks
Frameworks: TensorFlow, PyTorch, Keras
Learning Outcomes
- Have a solid understanding of AI concepts and techniques.
- Be able to apply AI tools to solve real-world problems.
- Understand the ethical implications of AI.
- Gain practical experience in developing AI models.1
Required Resources
- Laptop/PC with internet access.
- Python pre-installed.
- Access to training datasets.
- Accounts on platforms like Kaggle, Google Colab, and GitHub.
Duration
The module is designed to span 4 weeks, with a mix of lectures, hands-on sessions, and assessments:
- Week 1: AI Fundamentals
- Week 2: Machine Learning Basics
- Week 3: Deep Learning and Tools
- Week 4: Applications, Ethics, and Capstone Project
Contact Us
Email: info@skillexcellent.com | Phone: +91 92355 58887
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