Build real-world Artificial Intelligence applications with Python

What is Artificial Intelligence?

Artificial Intelligence (AI) is like giving superpowers to computers. It’s all about making machines smart enough to do things that usually need human brains, like learning from experience, solving problems, understanding languages, and even recognizing objects in pictures or videos.

Imagine your computer or phone not just following your instructions, but actually learning from what it sees or hears to help you better. AI is the magic that lets machines think, learn, and make decisions almost like humans do. It’s behind cool stuff like self-driving cars, talking robots, and programs that beat humans at chess or create art.

AI is the brainpower that we’re adding to machines to make them incredibly clever and capable of doing things that once seemed only possible in science fiction!

Why learn Artificial Intelligence (AI)?

Learning AI is like discovering the secret sauce that makes computers super-smart. It’s about teaching machines how to learn, adapt, and do things that we once thought only humans could do.

Think about it: with AI, you can create robots that learn to walk, chatbots that understand and talk like humans, or programs that can predict trends in the stock market. It’s like being a digital architect, crafting brains for machines to make them incredibly clever.

Plus, in the job world, AI skills are like having a golden ticket. Companies are hunting for AI-savvy folks to join their teams and cook up innovative solutions. So, by diving into AI, you’re not just picking up a skill; you’re joining a league of tech heroes, ready to shape the future!

? Ready to dive into the captivating world of Artificial Intelligence and bring your ideas to life? Join our groundbreaking course that takes you on an exhilarating journey from AI basics to crafting powerful, real-world applications using the magic of Python!

Dive into real-world AI projects from day one! This course isn’t just about learning; it’s about conquering. Gain skills that the industry craves, propelling you into coveted roles in AI, data science, and machine learning.

Peek behind the scenes! Explore real-world case studies from industries leveraging AI. AI is not just about algorithms; it’s about creativity. Unlock your potential to innovate and create, see the impact firsthand, and discover exciting possibilities for your own AI journey.

Module 1: Supervised and Unsupervised Learning with Python Part 01

• The Course Overview
• Artificial Intelligence and Its Need
• Applications and Branches of AI
• Defining Intelligence Using Turing Test
• Making Machines Think Like Humans
• General Problem Solver
• Building an Intelligent Agent
• Installing Python 3 and Packages
• Loading Data
• Supervised Versus Unsupervised Learning
• What is Classification?
• Preprocessing Data
• Label Encoding
• Logistic Regression and Naïve Bayes Classifier
• Confusion Matrix
• Support Vector Machines
• Classifying Income Data

Module 2: Supervised and Unsupervised Learning with Python Part 02

• What is Regression?
• Building a Single and Multivariable Regressor
• Estimating Housing Prices
• What is Ensemble Learning
• What Are Decision Trees
• What are Random and Extremely Random Forests?
• Dealing with Class Imbalance
• Finding Optimal Training Parameters
• Computing Relative Feature Importance
• Predicting Traffic
• Clustering Data with K-mean Algorithm
• Estimating the Number of Clusters
• Estimating the Quality of Clustering
• Building a Classifier
• Segmenting the Market
• Creating a Training Pipeline
• Extracting the Nearest Neighbors
• Building a K-Nearest Neighbors Classifier
• Computing similarity scores
• Finding Similar Users
• Building a Movie Recommendation System

Module 3:  Artificial Intelligence with Python – Sequence Learning Part 01

• Overview
• Introduction and Installation of Packages
• Tokenizing Text Data
• Converting Words to Their Base Forms
• Dividing Text Data into Chunks
• Extracting the Frequency of Terms Using a Bag of Words Model
• Building a Category Predictor
• Constructing a Gender Identifier
• Building a Sentiment Analyzer
• Topic Modeling Using Latent Dirichlet Allocation

Module 4:  Artificial Intelligence with Python – Sequence Learning Part 02

• Understanding Sequential Data
• Handling Time-Series Data with Pandas
• Slicing Time-Series Data
• Operating on Time-Series Data
• Extracting Statistics from Time-Series Data
• Generating Data Using Hidden Markov Models
• Identifying Alphabet Sequences with Conditional Random Fields
• Stock Market Analysis
• Working with Speech Signals
• Visualizing Audio Signals
• Transforming Audio Signals to the Frequency Domain
• Generating Audio Signals
• Synthesizing Tones to Generate Music
• Extracting Speech Features
• Recognizing Spoken Words

Module 5:  Artificial Intelligence with Python – Heuristic Search Part 01

• Overview
• Understanding Logic Programming
• Installing Python Packages
• Matching Mathematical Expressions
• Validating Primes
• Parsing a Family Tree
• Analyzing Geography
• Building a Puzzle Solver

Module 6:  Artificial Intelligence with Python – Heuristic Search Part 02

• Understanding Heuristic Search
• Constraint Satisfaction Problems
• Local Search Techniques
• Simulated Annealing
• Constructing a String Using Greedy Search
• Solving a Problem with Constraints
• Solving the Region-Coloring Problem
• Building an 8-puzzle solver
• Building a Maze Solver
• Understanding Evolutionary and Genetic Algorithms
• Generating a Bit Pattern with Predefined Parameters
• Visualizing the Evolution
• Solving the Symbol Regression Problem
• Building an Intelligent Robot Controller
• Using Search Algorithms in Games
• Minimax, Alpha-Beta Pruning and Negamax
• Installing easyAI Library
• Building a Bot to Play Last Coin Standing
• Building a bot to play Tic-Tac-Toe
• Building Two Bots to Play Connect Four Against Each Other
• Building Two Bots to Play Hexapawn Against Each Other

Module 7:  Artificial Intelligence with Python – Deep Neural Networks Part 01

• Overview
• Installing OpenCV
• Frame Differencing
• Tracking Objects Using Colorspaces
• Object Tracking Using Background Subtraction
• Building an Object Tracker Using the CAMShift Algorithm
• Optical Flow Based Tracking
• Face Detection and Tracking

Module 8:  Artificial Intelligence with Python – Deep Neural Networks Part 02

• Introduction to Artificial Neural Networks
• Building a Perceptron Based Classifier
• Constructing Single and Multilayer Neural Networks
• Building a Vector Quantizer
• Analyzing Sequential Data Using Recurrent Neural Networks
• Visualizing Characters in an Optical Character Recognition Database
• Building an Optical Character Recognition Engine
• What Is Reinforcement Learning?
• Creating an Environment
• Building a Learning Agent
• What are Convolutional Neural Networks?
• Building a Perceptron-Based Linear Regressor
• Building an Image Classifier Using a Single Layer Neural Network
• Building an Image Classifier Using a Convolutional Neural Network
• Learning Web Application with Spring 5 and Angular 2

Module 9:  Artificial Intelligence with Python – Develop Your Live Project Part 01

• Overview
• Classification Overview and Evaluation Techniques
• Decision Trees
• Prediction with Decision Trees and Student Performance Data
• Random Forests
• Predicting Bird Species with Random Forests
• The Problem of Text Classification
• Detecting YouTube Comment Spam with Bag of Words and Random Forests
• Word2Vec Models
• Detecting Positive/Negative Sentiment in User Reviews

Module 10:  Artificial Intelligence with Python – Develop Your Live Project Part 02

• Neural Networks
• Identifying the Genre of a Song Using Audio Analysis and Neural Networks
• Revising the Spam Detector to Use Neural Networks
• Overview of Deep Learning and Convolutional Neural Networks
• Identifying Handwritten Mathematical Symbols with Convolutional Neural Networks
• Revising the Bird Species Identifier to Use Images

Certificates will be awarded to participants at the end of training.

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TalhaTraining

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