Popular 2,156 students 4.9/5

Artificial Intelligence Essentials

Master AI fundamentals, machine learning, deep learning, and neural networks. From theory to implementation with hands-on Python projects.

6
Modules
50+
Hours
80+
Examples
6
Assignments
Python TensorFlow Scikit-learn Neural Networks NLP Computer Vision

Your Learning Journey

Track your progress in real-time

0
Completed
0
Total
0
Remaining
Overall Progress 0%
Started
25%
50%
100%
Start Learning

Course Outline

1AI Fundamentals

0/2
Definition and core concepts of AI
Types of AI: Narrow vs General
Real-world applications across industries
AI vs Machine Learning vs Deep Learning
Learning approaches: Supervised, Unsupervised, Reinforcement
Early AI era and Turing Test
AI winters and renaissances
Expert systems and knowledge representation
Modern deep learning revolution
Current state and future directions

2Machine Learning Basics

0/3
Supervised vs unsupervised learning
Training, validation, and test sets
Overfitting and underfitting
Cross-validation techniques
Logistic regression
Decision trees and random forests
Support vector machines (SVM)
Naive Bayes classifier
Linear regression and variants
Polynomial and non-linear regression
Regularization techniques

3Deep Learning Fundamentals

0/3
Neurons and perceptrons
Activation functions
Forward propagation
Backpropagation and gradient descent
Image processing fundamentals
Convolutional and pooling layers
Building CNN architectures
Transfer learning with pre-trained models
Sequence modeling
LSTM and GRU cells
Time series prediction
Attention mechanisms

4Natural Language Processing

0/2
Text preprocessing and tokenization
Word embeddings (Word2Vec, GloVe)
Sentiment analysis
Named entity recognition
Attention is all you need
BERT, GPT, and modern architectures
Fine-tuning pre-trained models
Machine translation and text generation

5Computer Vision

0/2
Image properties and color spaces
Filtering and edge detection
Feature extraction
Object detection and localization
Semantic and instance segmentation
Face recognition and detection
Pose estimation
Visual question answering

6Advanced Topics & Applications

0/2
Markov decision processes
Q-learning and policy gradients
Game playing and robotics
Real-world RL applications
AI ethics and fairness
Bias detection and mitigation
Model interpretability
Production deployment and monitoring

7Real-World Projects & Capstone

0/7
Data exploration and visualization
Feature scaling and preprocessing
Multi-class classification models
Convolutional neural network design
Image augmentation techniques
Model evaluation and optimization
Text preprocessing and tokenization
LSTM and transformer models
Fine-tuning BERT models
YOLO architecture and implementation
Transfer learning with pre-trained models
Real-time detection on video
Q-learning and DQN implementation
Environment setup and training
Evaluation and agent visualization
Intent recognition and slot filling
Context management and conversation flow
Integration with REST API