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Artificial Intelligence Level 2

Artificial Intelligence Track

Artificial Intelligence:
Level 2

5.0 (2,640 reviews)

Master advanced AI development techniques including deep learning models, natural language processing, computer vision, and scalable AI solutions. Build production-ready systems with CNNs, transformers, reinforcement learning, and deploy models using Flask, FastAPI, Docker, and Kubernetes.

Created by Avanteia
5,180 Total Enrolled
15 Sep 2024 Last Updated
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Artificial Intelligence Level 2 Course
3 Months Duration
Certificate On Completion
Level-02 Level
16 Modules Syllabus
3 Months Duration
English Language
Certificate Included
Introduction

Overview

This advanced AI course delivers professional-level skills in deep learning, NLP, computer vision, and production deployment. You will master supervised and unsupervised learning, build CNNs and transformers, develop reinforcement learning agents, deploy models with Flask and FastAPI, and complete a capstone project that demonstrates end-to-end AI system development.

AI Basics Tools Python Projects Logic ML Models Advanced AI Real Use
Goal

Learning Outcome

Design and implement intelligent AI solutions using real-time data for automation, predictive analytics, and smart decision-making across industries.

Curriculum

Syllabus

Click any module to expand and view topics and hands-on labs included.

  • What is AI? Types of AI (ANI, AGI, ASI)
  • History & Milestones
  • AI in Daily Life (Examples: Google, Netflix, Alexa)
  • Careers in AI
Hands-on Lab
AI Type Classification Career Path Mapping Industry Research
  • Python Basics (Variables, Loops, Functions)
  • Data Types, Conditionals, OOP Overview
  • Working with Libraries: NumPy, Pandas
  • File Handling & Basic Visualization (Matplotlib)
Hands-on Lab
NumPy Array Operations Pandas Data Analysis Matplotlib Visualization
  • What is Data? Structured vs Unstructured
  • Data Preprocessing, Cleaning, Normalization
  • Exploratory Data Analysis (EDA)
Hands-on Lab
Dataset Cleaning EDA Visualization Normalization Exercise
  • What is ML vs AI?
  • Supervised vs Unsupervised
  • Simple Algorithms: Linear Regression, KNN, Decision Trees
  • Model Evaluation Basics (Accuracy, Precision, Recall)
Hands-on Lab
Algorithm Comparison Model Evaluation Metric Calculation
  • Regression Models: Linear, Logistic
  • Classification Algorithms: SVM, Naive Bayes, Random Forest
  • Model Evaluation Metrics (Confusion Matrix, ROC, F1)
Hands-on Lab
Spam Email Classification ROC Curve Analysis Confusion Matrix Build
  • Clustering (K-means, DBSCAN, Hierarchical)
  • Dimensionality Reduction (PCA, t-SNE)
  • Applications in Customer Segmentation
Hands-on Lab
Market Basket Analysis Customer Segmentation PCA Visualization
  • Text Preprocessing (Tokenization, Stop Words, Stemming)
  • Bag of Words, TF-IDF
  • Sentiment Analysis Basics
Hands-on Lab
Twitter Sentiment Analyzer TF-IDF Implementation Text Preprocessing Pipeline
  • Image Representation & Preprocessing
  • Edge Detection, Feature Extraction
  • Intro to CNNs (Conceptual)
Hands-on Lab
Face Detection using OpenCV Edge Detection Demo Feature Extraction
  • Saving & Loading Models (Pickle, Joblib)
  • Intro to Flask for AI Apps
Hands-on Lab
Deploy ML Model Locally Flask API Creation Model Persistence
  • Neural Networks Basics
  • Activation Functions, Backpropagation
  • Optimizers: SGD, Adam
Hands-on Lab
Build ANN for MNIST Digits Activation Function Comparison Optimizer Benchmarking
  • CNN Architecture (Conv, Pooling, Flatten)
  • Transfer Learning (ResNet, VGG)
Hands-on Lab
Image Classifier with CNN Transfer Learning Implementation ResNet Fine-Tuning
  • Word Embeddings (Word2Vec, GloVe)
  • Seq2Seq Models
  • Transformers, BERT, GPT
  • Fine-Tuning Pre-trained Models
Hands-on Lab
Chatbot using Transformer Models BERT Fine-Tuning Word Embedding Visualization
  • RL Fundamentals (Agent, Environment, Reward)
  • Q-Learning, Deep Q Networks
  • Applications: Game AI, Robotics
Hands-on Lab
Train AI to Play a Simple Game Q-Learning Implementation Deep Q Network Build
  • Model Serving with FastAPI
  • Scaling with Docker, Kubernetes
  • Monitoring & Retraining Models
Hands-on Lab
FastAPI Model Serving Docker Containerization Kubernetes Deployment
  • Bias in AI, Fairness, Explainability
  • AI & Jobs
  • AI Governance & Regulations
Hands-on Lab
Bias Detection Exercise Fairness Metric Analysis Explainability Demo
  • Fake News Detection with Transformers
  • AI-Powered Recommendation Engine
  • Object Detection System for Security
  • AI Chatbot with Real-Time Responses
Hands-on Lab
End-to-End Project Build Model Deployment Documentation & Presentation

Free Internship Opportunity Included

Gain real-world industry experience with a 3-Month Internship Program included as part of the training.

Results

What You Will Learn

Advanced ML & Deep Learning

Master supervised, unsupervised, and deep learning with CNNs, ANNs, and production-grade evaluation.

NLP & Transformers

Build sentiment analyzers, chatbots, and fine-tune BERT/GPT models for real-world text tasks.

Computer Vision

Develop image classifiers with CNNs, apply transfer learning, and build face detection systems.

Production Deployment

Deploy models with Flask and FastAPI, scale with Docker/Kubernetes, and monitor in production.

Testimonials

What Our Students Say

"

The Level 2 course took my skills to a professional level. The Active Directory labs and privilege escalation modules are exactly what I needed to land my first pentesting job. Avanteia's hands-on approach is unmatched.

Vikram Patil Penetration Tester, Mumbai
"

I completed the Beginner course first and immediately enrolled in Intermediate. The malware analysis and reverse engineering modules were eye-opening. The 2-month duration is perfect for working professionals.

Sneha Kadam Cybersecurity Analyst, Pune
"

The cloud security and wireless hacking modules are incredibly relevant. I used the skills from this course to secure my company's AWS infrastructure. Highly recommended for anyone serious about cybersecurity.

Rahul Menon Security Engineer, Bangalore

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