AI using Python | FutureSkills Academy

Artificial Intelligence Using Python Beginner to Advance

About AI

Artificial Intelligence (AI) is a technique in which machines or computer systems are designed in such a way

that they can think, learn and take decisions like humans.

Key Terms with Definitions

AI (Artificial Intelligence) is a technology that makes machines intelligent.
Machine Learning (ML) is a part of AI in which the machine learns from data.
Deep Learning is an advanced form of ML in which neural networks are used.
Algorithm is step-by-step instructions that tell the machine what to do.
Data is a Information which the machine uses to learn.

Practical Example / Use Case

Siri / Google Assistant understands voice commands and responds (Speech AI).
Netflix Recommendations suggest new shows based on the shows you've watched (Machine Learning).
Self-driving Cars understand road signs, traffic and obstacles (Computer Vision + AI).

Master the most in-demand skill of the 21st century.

Chapter 1: Introduction to Artificial Intelligence

What is AI and why it is important.
Real-life applications of AI.
Different branches like machine learning, NLP, robotics.
Turing Test and how machines can think like humans.
How to install Python and start coding for AI.

Chapter 2: Supervised Learning (Classification & Regression)

Difference between supervised and unsupervised learning.
Classify data using algorithms like Logistic Regression, Naive Bayes, and SVM.
Predict values using regression (like predicting house prices).
Data preprocessing: scaling, normalization, label encoding.

Chapter 3: Ensemble Learning (Advanced Prediction)

What is ensemble learning and why it improves accuracy.
Learn decision trees, random forests, and extremely random forests.
Handle imbalanced datasets.
Use real-world data like traffic prediction.

Chapter 4: Unsupervised Learning (Detecting Patterns)

What is unsupervised learning and clustering.
Apply algorithms like K-Means, Mean Shift, and Gaussian Mixture Models.
Analyze shopping and stock market patterns without labeled data.

Chapter 5: Recommender Systems

Build a movie or product recommender system.
Use collaborative filtering and K-Nearest Neighbors.
Find similar users or products based on preferences.

Chapter 6: Logic Programming

Use logic to solve AI problems (not machine learning).
Match expressions, solve puzzles, analyze family trees.
A good introduction to symbolic reasoning.

Chapter 7: Heuristic Search Techniques

Search smarter using rules (heuristics).
Learn techniques like simulated annealing and greedy search.
Solve puzzles and mazes using AI search.

Chapter 8: Genetic Algorithms

Learn AI inspired by nature and evolution.
Concepts like selection, crossover, mutation.
Create intelligent robots and solve optimization problems.

Chapter 9: AI for Games

Make game-playing bots (Tic-Tac-Toe, Connect Four, Hexapawn).
Learn Minimax, Alpha-Beta pruning, and other game AI algorithms.
Build competitive AI opponents.

Chapter 10: Natural Language Processing (NLP)

Make computers understand text and language.
Learn tokenization, stemming, lemmatization.
Build sentiment analyzer, gender detector, topic modeling.

Chapter 11: Probabilistic Reasoning

Analyze data that changes over time (sequential data).
Learn Hidden Markov Models (HMM) and Conditional Random Fields (CRF).
Apply it to stock markets and time-series analysis

Chapter 12: Speech Recognition

Work with sound and voice data.
Visualize and transform audio signals.
Build systems that recognize spoken words.

AI ADVANCE LEVEL

Chapter 13: Object Detection and Tracking

Use OpenCV to detect and track objects in video.
Techniques: background subtraction, optical flow, CAMShift.
Build face and eye tracking systems.

Chapter 14: Artificial Neural Networks

Understand how neural networks mimic the brain.
Build single-layer and multi-layer neural networks.
Create an OCR system to recognize handwritten text.

Chapter 15: Reinforcement Learning

Make agents that learn from their environment.
Understand reward, state, and action.
Build bots that learn by doing and improve over time.

Chapter 16: Deep Learning with CNNs

Learn Convolutional Neural Networks (CNN) for image data.
Build an image classifier using TensorFlow.
Understand deep learning layers and architecture.