- 10 Sections
- 131 Lessons
- 96 Hours
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- Module 1: Supervised and Unsupervised Learning with Python Part 0117
- 1.0The Course Overview
- 1.1Artificial Intelligence and Its Need
- 1.2Applications and Branches of AI
- 1.3Defining Intelligence Using Turing Test
- 1.4Making Machines Think Like Humans
- 1.5General Problem Solver
- 1.6Building an Intelligent Agent
- 1.7Installing Python 3 and Packages
- 1.8Loading Data
- 1.9Supervised Versus Unsupervised Learning
- 1.10What is Classification?
- 1.11Preprocessing Data
- 1.12Label Encoding
- 1.13Logistic Regression and Naïve Bayes Classifier
- 1.14Confusion Matrix
- 1.15Support Vector Machines
- 1.16Classifying Income Data
- Module 2: Supervised and Unsupervised Learning with Python Part 0221
- 2.0What is Regression?
- 2.1Building a Single and Multivariable Regressor
- 2.2Estimating Housing Prices
- 2.3What is Ensemble Learning
- 2.4What Are Decision Trees
- 2.5What are Random and Extremely Random Forests?
- 2.6Dealing with Class Imbalance
- 2.7Finding Optimal Training Parameters
- 2.8Computing Relative Feature Importance
- 2.9Predicting Traffic
- 2.10Clustering Data with K-mean Algorithm
- 2.11Estimating the Number of Clusters
- 2.12Estimating the Quality of Clustering
- 2.13Building a Classifier
- 2.14Segmenting the Market
- 2.15Creating a Training Pipeline
- 2.16Extracting the Nearest Neighbors
- 2.17Building a K-Nearest Neighbors Classifier
- 2.18Computing similarity scores
- 2.19Finding Similar Users
- 2.20Building a Movie Recommendation System
- Module 3: Artificial Intelligence with Python - Sequence Learning Part 0110
- 3.0Overview
- 3.1Introduction and Installation of Packages
- 3.2Tokenizing Text Data
- 3.3Converting Words to Their Base Forms
- 3.4Dividing Text Data into Chunks
- 3.5Extracting the Frequency of Terms Using a Bag of Words Model
- 3.6Building a Category Predictor
- 3.7Constructing a Gender Identifier
- 3.8Building a Sentiment Analyzer
- 3.9Topic Modeling Using Latent Dirichlet Allocation
- Module 4: Artificial Intelligence with Python - Sequence Learning Part 0215
- 4.0Understanding Sequential Data
- 4.1Handling Time-Series Data with Pandas
- 4.2Slicing Time-Series Data
- 4.3Operating on Time-Series Data
- 4.4Extracting Statistics from Time-Series Data
- 4.5Generating Data Using Hidden Markov Models
- 4.6Identifying Alphabet Sequences with Conditional Random Fields
- 4.7Stock Market Analysis
- 4.8Working with Speech Signals
- 4.9Visualizing Audio Signals
- 4.10Transforming Audio Signals to the Frequency Domain
- 4.11Generating Audio Signals
- 4.12Synthesizing Tones to Generate Music
- 4.13Extracting Speech Features
- 4.14Recognizing Spoken Words
- Module 5: Artificial Intelligence with Python - Heuristic Search Part 018
- Module 6: Artificial Intelligence with Python - Heuristic Search Part 0221
- 6.0Understanding Heuristic Search
- 6.1Constraint Satisfaction Problems
- 6.2Local Search Techniques
- 6.3Simulated Annealing
- 6.4Constructing a String Using Greedy Search
- 6.5Solving a Problem with Constraints
- 6.6Solving the Region-Coloring Problem
- 6.7Building an 8-puzzle solver
- 6.8Building a Maze Solver
- 6.9Understanding Evolutionary and Genetic Algorithms
- 6.10Generating a Bit Pattern with Predefined Parameters
- 6.11Visualizing the Evolution
- 6.12Solving the Symbol Regression Problem
- 6.13Building an Intelligent Robot Controller
- 6.14Using Search Algorithms in Games
- 6.15Minimax, Alpha-Beta Pruning and Negamax
- 6.16Installing easyAI Library
- 6.17Building a Bot to Play Last Coin Standing
- 6.18Building a bot to play Tic-Tac-Toe
- 6.19Building Two Bots to Play Connect Four Against Each Other
- 6.20Building Two Bots to Play Hexapawn Against Each Other
- Module 7: Artificial Intelligence with Python - Deep Neural Networks Part 018
- Module 8: Artificial Intelligence with Python - Deep Neural Networks Part 0215
- 8.0Introduction to Artificial Neural Networks
- 8.1Building a Perceptron Based Classifier
- 8.2Constructing Single and Multilayer Neural Networks
- 8.3Building a Vector Quantizer
- 8.4Analyzing Sequential Data Using Recurrent Neural Networks
- 8.5Visualizing Characters in an Optical Character Recognition Database
- 8.6Building an Optical Character Recognition Engine
- 8.7What Is Reinforcement Learning?
- 8.8Creating an Environment
- 8.9Building a Learning Agent
- 8.10What are Convolutional Neural Networks?
- 8.11Building a Perceptron-Based Linear Regressor
- 8.12Building an Image Classifier Using a Single Layer Neural Network
- 8.13Building an Image Classifier Using a Convolutional Neural Network
- 8.14Learning Web Application with Spring 5 and Angular 2
- Module 9: Artificial Intelligence with Python – Develop Your Live Project Part 0110
- 9.0Overview
- 9.1Classification Overview and Evaluation Techniques
- 9.2Decision Trees
- 9.3Prediction with Decision Trees and Student Performance Data
- 9.4Random Forests
- 9.5Predicting Bird Species with Random Forests
- 9.6The Problem of Text Classification
- 9.7Detecting YouTube Comment Spam with Bag of Words and Random Forests
- 9.8Word2Vec Models
- 9.9Detecting Positive/Negative Sentiment in User Reviews
- Module 10: Artificial Intelligence with Python – Develop Your Live Project Part 026
- 10.0Neural Networks
- 10.1Identifying the Genre of a Song Using Audio Analysis and Neural Networks
- 10.2Revising the Spam Detector to Use Neural Networks
- 10.3Overview of Deep Learning and Convolutional Neural Networks
- 10.4Identifying Handwritten Mathematical Symbols with Convolutional Neural Networks
- 10.5Revising the Bird Species Identifier to Use Images