Advanced Programming using R & Python
- 1. R Programming: Introduction & Installation of R, R Basics, Finding Help, Code Editors for R, Command Packages, Exploratory Data Analysis, Data Objects, Data Types & Data Structure. Viewing Named Objects, Structure of Data Items, Control Structures, Functions in R (numeric, character, statistical), working with objects, Viewing Objects within Objects, Constructing Data Objects, Non parametric Tests- ANOVA, chi-Square, t-Test, U-Test
- 2. Python Programming: Introduction to Python, Basic Syntax, Data Types, Variables, Operators, Input/output, Flow of Control (Modules, Branching), If, If- else, Nested if-else, Looping, For, While, Nested loops, Control Structure, Break, Continue, Pass, Strings and Tuples, Accessing Strings, Basic Operations, String slices, Working with Lists, Introduction, Accessing list, Operations, Function and Methods, Files, Modules, Dictionaries, Functions and Functional Programming, Declare, assign and retrieve values from Lists, Introducing Tuples, Accessing tuples, matplotlib, seaborn,
- 3. Case Studies: Mathematical computing with Python, Data migration and visualization: Pandas and Matplotlib, Pycharm, Anaconda, Data manipulation with Pandas
Fundamental of Artificial Intelligence
- 1. Introduction to AI, Evolution & Revolution of AI, Introduction to AI, Introduction of Applications in various Domains (Scientific including Health Sciences, Engineering, Financial Services and other industries), Ethics of AI, Structure of AI, Real world Implications, Intelligent Agents, Uninformed Search, Constraint Satisfaction Search, Combinatorial Optimization Problems, Heuristic & Meta-heuristics, Genetic Algorithms for Search, Game Trees, Supervised & Unsupervised Learning, Knowledge Representation, Propositional and Predicate Logic, Inference and Resolution for Problem Solving, Rules and Expert Systems, Artificial Life, Emergent Behavior, Genetic Algorithms
- 1. Machine Learning in Nut shell, Supervised Learning, Unsupervised Learning, ML applications in the real world, Uses of Machine learning
- 2. Introduction to Feature engineering and Data Pre-processing: Data Preparation, Feature creation, Data cleaning & transformation, Data Validation & Modelling, Feature selection Techniques, Dimensionality reduction, Recommendation Systems and anomaly PCA,
- 3. ML Algorithms: Decision Trees, Oblique trees, Random forest, Bayesian analysis and Naïve bayes classifier, Support vector Machines, KNN, Gradient boosting, Ensemble methods, Bagging & Boosting , Association rules learning, Apriori and FP growth algorithms, Linear and Nonlinear classification, linear and logistic Regression, Clustering, K-means, Overview of Factor Analysis, ARIMA, ML in real time, Algorithm performance metrics, ROC, AOC, Confusion matrix, F1 score, MSE, MAE, DBSCAN Clustering in ML, Anomaly Detection, Recommender System
Machine Learning Tools: Introduction to the basic data science toolset
● Usage of ML algorithms, Algorithm performance metrics (confusion matrix sensitivity,Specificity, ROC, AOC, F1 Score, Precision, Recall, MSE, MAE)
● Credit Card Fraud Analysis, Intrusion Detection system
Deep Neural Networks
- 1. Introduction to Deep Neural Network, RNN, CNN, LSTM, Deep Belief Network, semantic Hashing, Training deep neural network, Tensorflow 2.x, Pythorch, building deep learning models, building a basic neural network using Keras with Tensor Flow, Troubleshoot deep learning models, building deep learning project. (Alog model), Transfer Learning, Inductive, unsupervised Transductive, Deep Learning Tools & Technique, Tuning Deep Learning Models, Trends in Deep Learning, Application of Multi Processing in DL, Deep Learning Case Studies
Natural Language Processing & Computer Vision
- 1. Understanding Language, NLP Overview, Introduction to Language Computing, Language in Cognitive Science, Definitions of language, Language as a rule-governed dynamic system, Language and symbolic systems: Artificial language (Logical language/programming language) vs. Natural Language, Linguistics as a scientific study, And Description of different branches of Linguistics: Statistical Linguistics, Psycholinguistics, Neurolinguistics, Computational Linguistics, Sociolinguistics etc.
- 2. Language Analysis and Computational Linguistics, Semantics, Discourse, Pragmatics, Lexicology, Shallow Parsing and Tools for NLP, Deep Parsing and Tools for NLP, Statistical Approaches, NLP with Machine Learning and Deep Learning, Pre-processing, Need of Pre-processing Data, Introduction to NLTK, Using Python Scripts
- 3. Word2Vec models (Skip-gram, CBOW, Glove, one hot Encoding), NLP Transformers, Bert in NLP Speech Processing, NLP Model Deployment Techniques using Flask, NLP Applications- Language identification, Auto suggest/ Auto complete, chat bots, Robotics
- 4. Introduction to Computer Vision, Computer Vision and Natural Language Processing, The Three R's of Computer Vision, Basics of Image Processing, Low-, Mid- & High-Level Vision, Edge Detection, Interest Points and Corners, Image Classification, Recognition, Bag of Features, and Large-scale Instance Recognition, Object Detection & Transfer Learning, AlexNet, ResNet, ImageNet, Gender Prediction, Face / Object Recognition
- 1. Introduction to reinforcement learning as an approximate dynamic programming problem, Overview of reinforcement learning: the agent environment framework, successes of reinforcement learning, Bandit problems and online learning, Markov decision processes, Returns, and value functions, Solution methods: dynamic programming, Solution methods for learning, Solution methods for temporal difference learning, Eligibility traces, Value function approximation Models and planning (table lookup case), Reinforcement Learning Applications, Implementing a Reinforcement Learning application
Case studies: successful examples of RL systems, simulation based methods like Q-learning.
Evaluate text with Azure Cognitive Language Services
1.Classify and moderate text with Azure Content Moderator
In this module, we'll introduce you to Azure Content Moderator and show how to use it for text moderation.
2. Add conversational intelligence to your apps by using Language Understanding Intelligent Service (LUIS)
In this module, we'll introduce you to Language Understanding Intelligent Service (LUIS) and show you how to create a LUIS application
3. Discover sentiment in text with the Text Analytics API
Learn what your customers are really saying about your product or brand when they send feedback. We'll create a solution that uses Azure Functions and the intelligence of the Text Analytics API to discover sentiment in text messages.
Process and Translate Speech with Azure Cognitive Speech Services
1. Create speech-enabled apps with the Speech service
The Speech service enables you to build speech-enabled applications. This module focuses on using the speech-to-text and text-to-speech APIs, which enable you to create apps that are capable of speech recognition and speech synthesis.
2. Translate speech with the speech service
Translation of speech builds on speech recognition by recognizing and transcribing spoken input in a specified language, and returning translations of the transcription in one or more other languages.
Create Intelligent Bots with the Azure Bot Service
1. Build a bot with QnA Maker and Azure Bot Service
Bots are a popular way to provide support through multiple communication channels. This module describes how to use the QnA Maker service and Azure Bot Service to create a bot that answers user questions.
Process and classify images with the Azure cognitive vision services
1. Identify faces and expressions by using the Computer Vision API in Azure Cognitive Services
Learn about the Computer Vision API in Azure that allows you to identify facial details in pictures.
2. Process images with the Computer Vision service
Use the Computer Vision API to analyze images for insights, extract text from images, and generate high-quality thumbnails
3. Classify images with the Microsoft Custom Vision Service
Create, train and test a custom image classification model using the Custom Vision Service to accurately identify paintings from famous artists.
4. Evaluate the requirements for implementing the Custom Vision APIs
Evaluate the requirements for a solution that implements the Custom Vision Prediction and Training APIs. Your completed design plans will support workflow requirements, and you'll be better prepared to work with developers and architects.
5. Extract insights from videos with the Video Indexer service
Learn how to use the Video Indexer API to upload, index, retrieve insights, and search for content in video files.
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Python is an interpreted, high-level, general-purpose programming language.
Project Jupyter is a nonprofit organization created to "develop open-source software, open-standards, and services for interactive computing across dozens of programming languages"
Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.
Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy
It is easily the most widely used tool in the industry today. Google might have something to do with that!
It is used extensively for building deep learning applications
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Frequently Asked Questions
What is artificial intelligence?
AI can be described as an area of computer science that simulates human intelligence in machines. It’s about smart algorithms making decisions based on the available data. Whether it’s Amazon’s Alexa or a self-driving car, the goal is to mimic human intelligence at lightning speed (and with a reduced rate of error).
What are intelligent agents?
An intelligent agent is an autonomous entity that leverages sensors to understand a situation and make decisions. It can also use actuators to perform both simple and complex tasks. In the beginning, it might not be so great at performing a task, but it will improve over time. The Roomba vacuum cleaner is an excellent example of this.
What’s the most popular programming language used in AI?
The open-source modular programming language Python leads the AI industry because of its simplicity and predictable coding behavior. It's popularity can be attributed to open-source libraries like Matplotlib and NumPy, efficient frameworks such as Scikit-learn, and practical version libraries like Tensorflow and VTK.
What are AI neural networks?
Neural networks in AI mathematically model how the human brain works. This approach enables the machine to think and learn as humans do. This is how smart technology today recognizes speech, objects, and more.
What’s a Turing test?
The Turing test, named after Alan Turing, is a method of testing a machine’s human-level intelligence. For example, in a human-versus-machine scenario, a judge will be tasked with identifying which terminal was occupied by a human and which was occupied by a computer based on individual performance. Whenever a computer can pass off as a human, it’s deemed intelligent. The game has since evolved, but the premise remains the same.
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