Artificial Intelligence Course by LYFAUX

BEST ARTIFICIAL INTELLIGENCE PROGRAM BY LYFAUX

Become an industry-ready AI professional with hands-on training, real-world projects, and expert mentorship.

Master the most in-demand skills in AI, Machine Learning, Deep Learning, and Generative AI with real-world projects, tools, and hands-on experience.

No prior experience required. Learn at your own pace and become the Top 1% with our Cybersecurity Program!

About Course

Learn the Fundamentals and Advanced Concepts of Artificial Intelligence, Machine Learning, Deep Learning and Generative AI and grow your skill set with our Courses and Certificates, Taught by Industry Leaders.

Expert Guidance

Extensive Knowledge on AI, ML, DL and Gen AI

180+ Hours of Learning

CAREER OPPORTUNITIES

After course completion, you can become:

Machine Learning Engineer

Data Scientist / Data Analyst

Deep Learning Engineer

NLP Engineer / LLM Engineer

Generative AI Developer

AI Product Developer

Research Assistant / AI Researcher

AI Consultant / Freelancer

AI Integration Engineer (No-Code + APIs)

And Much More

Why LYFAUX

🏆 Why should you choose this program?

Taught by Real-World Artificial Intelligence Experts
Learn AI/ML/GenAI in one structured program
Capstone Projects that build your portfolio
Real-world tools, platforms, and hands-on code
Certification included
High-growth industry with 100k+ jobs globally

This Comprehensive AI, ML, DL and Gen AI certification course is designed to empower you with job-ready skills that are and will continue to be in high demand in present and in future as well. By the end of this course, you’ll not only understand the theory but also know how to apply these skills to solve real-world problems based on Artificial Intelligence.

🧠 Module 1: Introduction to Artificial Intelligence

📘 Topics Covered:

What is AI? History, Evolution & Applications
Types of AI: Narrow, General, Super AI
AI vs ML vs DL vs GenAI
Real-world AI Use Cases & Industries

🛠️ Tools Used:

ChatGPT, Google Bard
AI Demo Apps (Runway, Fireflies AI, etc.)

🎯 Learning Outcome:

Understand the foundation of AI and where it is used. Get familiar with different AI branches and real-life applications.

🐍 Module 2: Python for AI & Data Science

📘 Topics Covered:

Python Basics: Variables, Loops, Functions, OOP
Data Structures & File Handling
Working with NumPy, Pandas, Matplotlib

🛠️ Tools Used:

Google Colab, Jupyter Notebook
Visual Studio Code

🎯 Learning Outcome:

Write clean Python code and manipulate data effectively. Build foundational scripts for future AI/ML projects.

📊 Module 3: Mathematics & Statistics for AI

📘 Topics Covered:

Descriptive & Inferential Statistics
Probability Theory, Distributions
Linear Algebra, Matrices
Calculus for Optimization

🛠️ Tools Used:

NumPy, SciPy, Matplotlib
Excel/Google Sheets (for basics)

🎯 Learning Outcome:

Gain the mathematical intuition to understand ML algorithms and deep learning mechanics.

🤖 Module 4: Supervised Machine Learning

📘 Topics Covered:

Regression: Linear, Logistic
Classification: Decision Trees, SVM, KNN
Model Evaluation: Confusion Matrix, ROC, Accuracy

🛠️ Tools Used:

Scikit-learn
Google Colab, Python

🎯 Learning Outcome:

Build prediction/classification models and evaluate them using real-world datasets.

🧩 Module 5: Unsupervised Machine Learning

📘 Topics Covered:

Clustering: K-Means, DBSCAN
Dimensionality Reduction: PCA, t-SNE
Anomaly Detection

🛠️ Tools Used:

Scikit-learn, Seaborn
Google Colab

🎯 Learning Outcome:

Analyze unlabelled data and derive hidden patterns for insights and segmentation.

⚙️ Module 6: Model Optimization

📘 Topics Covered:

Overfitting vs Underfitting
Regularization: L1/L2
Cross Validation, Grid Search
Feature Scaling & Selection

🛠️ Tools Used:

Scikit-learn
GridSearchCV, RandomizedSearchCV

🎯 Learning Outcome:

Tune models for better accuracy and performance with real-world datasets.

🧠 Module 7: Neural Networks (ANN)

📘 Topics Covered:

Perceptrons, Layers, Activation Functions
Forward & Backpropagation
Optimizers: SGD, Adam

🛠️ Tools Used:

TensorFlow, Keras
Google Colab

🎯 Learning Outcome:

Design and train your first neural network for prediction/classification problems.

🖼️ Module 8: Convolutional Neural Networks (CNNs)

📘 Topics Covered:

Filters, Pooling, Feature Maps
CNN Architectures: LeNet, VGG, ResNet
Image Classification Use Cases

🛠️ Tools Used:

TensorFlow, Keras, OpenCV
Datasets: CIFAR-10, MNIST

🎯 Learning Outcome:

Create a deep learning model for computer vision tasks like object/image recognition.

⏳ Module 9: Recurrent Neural Networks (RNN, LSTM, GRU)

📘 Topics Covered:

Sequence Modeling
RNN, LSTM, GRU Architectures
Time Series Forecasting & Text Analysis

🛠️ Tools Used:

TensorFlow/Keras
Datasets: IMDB, Stock Market

🎯 Learning Outcome:

Build models to analyze sequences like text and predict future data points.

📚 Module 10: Natural Language Processing (NLP)

📘 Topics Covered:

Text Preprocessing: Lemmatization, Stop Words
Bag of Words, TF-IDF, Word Embeddings
Sentiment Analysis, Text Classification

🛠️ Tools Used:

NLTK, SpaCy, Scikit-learn
Hugging Face Datasets

🎯 Learning Outcome:

Process and analyze human language for building NLP-based applications.

🔍 Module 11: Transformers & Language Models

📘 Topics Covered:

Attention Mechanism, Self-Attention
Transformers: BERT, GPT, LLaMA
Prompt Engineering

🛠️ Tools Used:

Hugging Face Transformers
OpenAI Playground

🎯 Learning Outcome:

Work with state-of-the-art LLMs and integrate them into real-world applications.

🧬 Module 12: Generative AI Fundamentals

📘 Topics Covered:

GANs, VAEs, Diffusion Models
Image Generation, Deepfakes
Ethical Considerations in GenAI

🛠️ Tools Used:

TensorFlow, PyTorch
Runway ML, Midjourney

🎯 Learning Outcome:

Understand and build GenAI models that generate images, text, and more.

💬 Module 13: GenAI APIs & LLM Integration

📘 Topics Covered:

API Integration (OpenAI, Cohere, Gemini)
Prompting, Embedding, Vector DBs
LangChain & RAG

🛠️ Tools Used:

OpenAI API, LangChain, Pinecone
Google Gemini API, Hugging Face

🎯 Learning Outcome:

Build AI-powered apps using GenAI APIs with practical use cases.

🚀 Module 14: Model Deployment & AI App Development

📘 Topics Covered:

Save & Export Models
Build APIs with Flask/FastAPI
Deploy with Streamlit, Hugging Face Spaces

🛠️ Tools Used:

Streamlit, Flask, Docker
GitHub, Hugging Face

🎯 Learning Outcome:

Convert your AI models into full-stack web apps and launch them live.

🧪 Module 15: Real-World AI Projects & Ethics

📘 Topics Covered:

Case Studies from Healthcare, Finance, E-commerce, and EdTech
AI Bias, Privacy & Fairness
Responsible AI and Ethical Guidelines
Building Human-Centered AI Systems

🛠️ Tools Used:

Dataset Repositories (Kaggle, UCI)
Explainable AI Tools (SHAP, LIME)
AI Fairness Toolkits (IBM AI Fairness 360)

🎯 Learning Outcome:

Learn how to apply AI to real-world scenarios while ensuring ethical, fair, and responsible AI practices.

🤖 Capstone Path 1: AI-Powered Chatbot with GenAI

🔹 Project Objective:
Develop an intelligent chatbot using GPT or LLaMA that answers FAQs, provides support, and can be integrated into websites.

📋 Tasks:

Dataset Collection & Intent Mapping
Prompt Engineering & Fine-Tuning
Integration with UI via API
Testing Responses & Deployment

🧰 Tools Required:

OpenAI GPT, LangChain, Hugging Face, Pinecone, Streamlit or Node.js

🎯 Final Deliverables:

Chatbot Web App
Backend with Prompt Logic & Memory
User Testing Report & Logs

👨‍💼 Ideal For:

Those interested in GenAI, NLP, or Product Engineering roles

🧠 Capstone Path 2: Deep Learning Model for Image Classification

🔹 Project Objective:
Train a CNN model to classify images (e.g., handwritten digits, vehicles, medical X-rays).

📋 Tasks:

Dataset Preprocessing & Augmentation
Model Architecture Design (CNN)
Model Training, Validation & Optimization
UI Integration or Deployment

🧰 Tools Required:

TensorFlow, Keras, OpenCV, Google Colab, Streamlit

🎯 Final Deliverables:

Trained CNN Model
Accuracy Reports & Confusion Matrix
Image Classification Demo App

👨‍💼 Ideal For:

Aspiring Computer Vision Engineers or Deep Learning Specialists

📈 Capstone Path 3: Stock Market Trend Predictor using LSTM

🔹 Project Objective:
Use time-series forecasting to predict future stock prices or market trends.

📋 Tasks:

Data Collection via Yahoo Finance API
Feature Engineering & Scaling
Build & Train LSTM/GRU Model
Visualize Forecasted Trends

🧰 Tools Required:

Python, Pandas, TensorFlow, Keras, Matplotlib, Plotly

🎯 Final Deliverables:

Trend Prediction Dashboard
Model Evaluation Reports
Time-Series Visualization

👨‍💼 Ideal For:

Those pursuing roles in Finance, Data Science, or Quantitative Analysis

🧬 Capstone Path 4: AI Model for Medical Diagnosis

🔹 Project Objective:
Develop a machine learning model that predicts diseases like Diabetes or Heart Disease using patient data.

📋 Tasks:

EDA & Data Cleaning
Train Classification Models (SVM, RF, XGBoost)
Evaluate Performance with AUC, Precision, Recall
Build a Dashboard or Web App for Input

🧰 Tools Required:

Scikit-learn, XGBoost, Pandas, Matplotlib, Streamlit

🎯 Final Deliverables:

Trained ML Model
Healthcare Dashboard
Report with Confusion Matrix & Evaluation Metrics

👨‍💼 Ideal For:

AI/ML aspirants targeting roles in Healthcare or Applied AI domains

🎨 Capstone Path 5: Text-to-Image Generator using Stable Diffusion

🔹 Project Objective:
Create a web app that uses Stable Diffusion to generate art from text prompts.

📋 Tasks:

Set up Diffusion Models
UI for Prompt Input
Model Inference & Image Rendering
Gallery of Generated Images

🧰 Tools Required:

Stable Diffusion, Hugging Face Diffusers, Replicate API, Gradio, Streamlit

🎯 Final Deliverables:

Web App for AI Image Generation
Prompt Examples & Art Showcase
Report on Model Customization & Safety Measures

👨‍💼 Ideal For:

Those exploring Generative AI, Creative Tech, or Digital Product Design

🔐 Capstone Path 6: AI-Based Phishing Website Detector

🔹 Project Objective:
Build a machine learning model that detects whether a website is legitimate or a phishing attempt based on its URL and content features.

📋 Tasks:

Collect phishing & legitimate website datasets
Extract features (URL length, domain age, SSL status, etc.)
Train classification models (Random Forest, SVM, XGBoost)
Evaluate with Precision, Recall, and ROC Curve
Develop a simple interface or Chrome Extension for real-time checking

🧰 Tools Required:

Python, Scikit-learn, Pandas, BeautifulSoup, Flask/Streamlit, PhishTank Dataset

🎯 Final Deliverables:

ML-powered Website Checker
Performance Report with ROC & Confusion Matrix
Real-time Detection Interface (Web or Extension)

👨‍💼 Ideal For:

Learners interested in Cybersecurity + AI, Threat Detection, or Anti-Phishing Tech Roles