CERTIFICATE IN ARTIFICIAL INTELLIGENCE
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- CERTIFICATE IN ARTIFICIAL INTELLIGENCE
Curriculum
- 3 Sections
- 169 Lessons
- 10 Weeks
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- Overview10
- Basics150
- 3.1Applications of AI
- 3.2Branches of AI
- 3.3Defining intelligence using Turing Test
- 3.4Making machines think like humans
- 3.5Building rational agents
- 3.6General Problem Solver
- 3.7Building an intelligent agent
- 3.8Installing Python 3
- 3.9Installing packages
- 3.10Loading data
- 3.11Classification and Regression Using Supervised Learning
- 3.12Classification and Regression Using Supervised Learning
- 3.13Supervised versus unsupervised learning
- 3.14What is classification?
- 3.15Preprocessing data
- 3.16Label encoding
- 3.17Logistic Regression classifier
- 3.18Naïve Bayes classifier
- 3.19Confusion matrix
- 3.20Support Vector Machines
- 3.21Classifying income data using Support Vector Machines
- 3.22What is Regression?
- 3.23Building a single variable regressor
- 3.24Building a multivariable regressor
- 3.25Estimating housing prices using a Support Vector Regressor
- 3.26Predictive Analytics with Ensemble Learning
- 3.27Predictive Analytics with Ensemble Learning
- 3.28What is Ensemble Learning?
- 3.29What are Decision Trees?
- 3.30What are Random Forests and Extremely Random Forests?
- 3.31Dealing with class imbalance
- 3.32Finding optimal training parameters using grid search
- 3.33Computing relative feature importance
- 3.34Predicting traffic using Extremely Random Forest regressor
- 3.35Detecting Patterns with Unsupervised Learning
- 3.36Detecting Patterns with Unsupervised Learning
- 3.37What is unsupervised learning?
- 3.38Clustering data with K-Means algorithm
- 3.39Estimating the number of clusters with Mean Shift algorithm
- 3.40Estimating the quality of clustering with silhouette scores
- 3.41What are Gaussian Mixture Models?
- 3.42Building a classifier based on Gaussian Mixture Models
- 3.43Finding subgroups in stock market using Affinity Propagation model
- 3.44Segmenting the market based on shopping patterns
- 3.45Building Recommender Systems
- 3.46Building Recommender Systems
- 3.47Creating a training pipeline
- 3.48Extracting the nearest neighbors
- 3.49Building a K-Nearest Neighbors classifier
- 3.50Computing similarity scores
- 3.51Finding similar users using collaborative filtering
- 3.52Building a movie recommendation system
- 3.53Logic Programming
- 3.54What is logic programming?
- 3.55Understanding the building blocks of logic programming
- 3.56Solving problems using logic programming
- 3.57Installing Python packages
- 3.58Matching mathematical expressions
- 3.59Validating primes
- 3.60Parsing a family tree
- 3.61Analyzing geography
- 3.62Building a puzzle solver
- 3.63Heuristic Search Techniques
- 3.64Heuristic Search Techniques
- 3.65What is heuristic search?
- 3.66Constraint Satisfaction Problems
- 3.67Local search techniques
- 3.68Constructing a string using greedy search
- 3.69Solving a problem with constraints
- 3.70Solving the region-coloring problem
- 3.71Building an 8-puzzle solver
- 3.72Building a maze solver
- 3.73Genetic Algorithms
- 3.74Genetic Algorithms
- 3.75Understanding evolutionary and genetic algorithms
- 3.76Fundamental concepts in genetic algorithms
- 3.77Generating a bit pattern with predefined parameters
- 3.78Visualizing the evolution
- 3.79Solving the symbol regression problem
- 3.80Building an intelligent robot controller
- 3.81Building Games with Artificial Intelligence
- 3.82Using search algorithms in games
- 3.83Combinatorial search
- 3.84Minimax algorithm
- 3.85Alpha-Beta pruning
- 3.86Negamax algorithm
- 3.87Installing easyAI library
- 3.88Building a bot to play Last Coin Standing
- 3.89Building a bot to play Tic-Tac-Toe
- 3.90Building two bots to play Connect Fourâ„¢ against each other
- 3.91Building two bots to play Hexapawn against each other
- 3.92Natural Language Processing
- 3.93Natural Language Processing
- 3.94Introduction and installation of packages
- 3.95Tokenizing text data
- 3.96Converting words to their base forms using stemming
- 3.97Converting words to their base forms using lemmatization
- 3.98Dividing text data into chunks
- 3.99Extracting the frequency of terms using a Bag of Words model
- 3.100Building a category predictor
- 3.101Constructing a gender identifier
- 3.102Building a sentiment analyzer
- 3.103Topic modeling using Latent Dirichlet Allocation
- 3.104Probabilistic Reasoning for Sequential Data
- 3.105Probabilistic Reasoning for Sequential Data
- 3.106Understanding sequential data
- 3.107Handling time-series data with Pandas
- 3.108Slicing time-series data
- 3.109Operating on time-series data
- 3.110Extracting statistics from time-series data
- 3.111Generating data using Hidden Markov Models
- 3.112Identifying alphabet sequences with Conditional Random Fields
- 3.113Stock market analysis
- 3.114Building A Speech Recognizer
- 3.115Building A Speech Recognizer
- 3.116Working with speech signals
- 3.117Visualizing audio signals
- 3.118Transforming audio signals to the frequency domain
- 3.119Generating audio signals
- 3.120Synthesizing tones to generate music
- 3.121Extracting speech features
- 3.122Recognizing spoken words
- 3.123Object Detection and Tracking
- 3.124Object Detection and Tracking
- 3.125Installing OpenCV
- 3.126Frame differencing
- 3.127Tracking objects using colorspaces
- 3.128Object tracking using background subtraction
- 3.129Building an interactive object tracker using the CAMShift algorithm
- 3.130Optical flow-based tracking
- 3.131Face detection and tracking
- 3.132Eye detection and tracking
- 3.133Artificial Neural Networks
- 3.134Artificial Neural Networks
- 3.135Introduction to artificial neural networks
- 3.136Building a Perceptron based classifier
- 3.137Constructing a single layer neural network
- 3.138Constructing a multilayer neural network
- 3.139Building a vector quantizer
- 3.140Analyzing sequential data using recurrent neural networks
- 3.141Visualizing characters in an Optical Character Recognition database
- 3.142Building an Optical Character Recognition engine
- 3.143Reinforcement Learning
- 3.144Reinforcement Learning
- 3.145Understanding the premise
- 3.146Reinforcement learning versus supervised learning
- 3.147Real world examples of reinforcement learning
- 3.148Building blocks of reinforcement learning
- 3.149Creating an environment
- 3.150Building a learning agent
- Conclusion9
- 4.1Summary
- 4.2Deep Learning with Convolutional Neural Networks
- 4.3Deep Learning with Convolutional Neural Networks
- 4.4What are Convolutional Neural Networks?
- 4.5Architecture of CNNs
- 4.6Types of layers in a CNN
- 4.7Building a perceptron-based linear regressor
- 4.8Building an image classifier using a single layer neural network
- 4.9Building an image classifier using a Convolutional Neural Network