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Artificial Intelligence & Machine Learning course in chennai

Artificial Intelligence & Machine Learning

Global Certification

:

AI-100 Designing and Implementing an Azure AI Solution In Microsoft

Exam Code

:

AI-100

Training Name

:

HB Certified networking professional in Routing

Description

:

Artificial Intelligence is the big thing in the technology field and a large number of organizations are implementing AI and the demand for professionals in AI is growing at an amazing speed. Artificial Intelligence makes computers to perform tasks such as speech recognition, decision-making and visual perception which normally requires human intelligence that aims to develop intelligent machines.

Weekend Batch

:

Saturday & Sunday

Training Time

:

4 or 6 hrs Per day

Duration

:

40 hrs

Venue

:

HB Educational Services, 39/40, 1st Main Road, Gandhi Nagar, Adyar, Chennai-20

Target Audience

Professionals for this field come from various domains like mathematics, statistics, engineering, management, etc. Anybody interested in pursuing a career in AI or genuinely want to know about various domains and applications of AI can go through the tutorials.

Training Overview

The main purpose of this course is to provide the most fundamental knowledge to the students so that they can understand what the AI is. You will learn some basic search algorithms for problem solving; knowledge representation and reasoning; pattern recognition; fuzzy logic; and neural networks.

Training Content

Machine Learning Engineering Stack

  • Hadoop Or Spark ?
  • Luigi Framework
  • PySpark in Practise
  • Dockers & Containers for Data Science.
  • CI/CD - Continuous Integration/Continuous Delivery
  • Version Control Using Git
  • Logging, Testing, Debugging
  • Python Code Optimization
  • AWS Services for AI/ML
  • ML Tools and Libraries
  • Capstone : Proposa

Data Wrangling

  • Introduction to APIs and Web Scraping
  • Data Wrangling with Pandas
  • Importing Data from Web
  • SQL For Data Analytics
  • SQL at Scale Using Spark
  • Handling Unstructured Data
  • Big Data Analytics - Spark Data Frames | PySpark | Dask
  • Data Storytelling
  • Capstone : Data Collection

Foundations of Machine Learning

  • Linear Regression
  • Logistic Regression and Gradient Descent
  • Statistical Modelling
  • Decision Trees for Classification and Regression
  • Random Forest Algorithm
  • K-Means Clustering
  • Gradient Boosting : XBoost and CatBoost
  • Model Selection : Feature Selection, Dimensionality, Cross-Validation
  • Model Evaluation : Performance Metrics
  • Model Interpretation : Explainable AI & ML
  • ML Hands On and Best Practices

Machine Learning at Scale

  • Advanced Data Wrangling at Scale (Big Pandas, Advanced SQL)
  • Scalable Machine Learning with Dask
  • Distributed Computing with Dask
  • Parallel Computing with Dask
  • ML at Scale on SparkML
  • Supervised Learning with SparkML
  • Recommendation Engines at Scale with Spark
  • Monitoring and Debugging Scaleable ML Systems
  • Building, Debugging and Tuning SparkML Pipeline
  • Best Practices for ML at Scale

Deep Learning

  • Deep Neural Networks
  • Backpropagation & Mathematics Behind It
  • CNNs, RNNs, LSTMs, GANs and the Maths Behind Them
  • Stochastic Gradient Descent
  • Deep Learning with (Keras, Tensor Flow, Pytorch)
  • Building and Deploying Deep Learning Apps
  • Using Kears, TensorFlow and Pytorch.
  • Building a CNN using TensorFlow
  • Deep Learning using Pytorch
  • Fast.ai : Deep Learning Framework
  • Transfer Learning : Research Work
  • AutoML and Deep Learning Optimization
  • DL Hands On and Best Practices

Natural Language Processing

  • NLP Fundamentals in Python
  • NLP Feature Engineering
  • Beyond word2vec & Mathematics Behind It : GLoVE, fastText, StarSpace
  • Text Classification Models with FastText in Python.
  • Building Chatbots in Python
  • Conversational AI and NLU
  • Advanced Modern NLP
  • Capstone Submission

Image Processing and Computer Vision

  • Computer Vision : Advanced & Applied
  • Deep Learning In Computer Vision
  • Image Processing Pipelines
  • Image Compressing : Color Quantization
  • Image Clustering and Classification - Deep CNNs, GANs
  • Object Detection Using TensorFlow and SSDs
  • Semantic Segmentation for Deep Learning
  • Applications and Trends: Hands On and Best Practices
  • Image Processing Tutorial : Dataset Preprocessing, Building Utils, Training & Inference, Model to Production.

Deploying AI Systems : From Model to Production

  • Production Data Science with Git
  • Building Quality APIs : Swagger
  • Testing APIs : Postman
  • Designing of Deployment Solution Architecture
  • Technical Considerations of Productionizing Models
  • Building Robust ML systems
  • Deploying Python Models to Production
  • Deploying Large Spark Models to Production

Pre-Requisites

:

Basic understanding of mathematics especially linear algebra, probability and statistics is a must. Experience in any of the programing language will be a plus.

Participant Fee

:

Upon Request

For more details, please call / whatsapp +91-44-42115338, or 91-9884987719 or +91-8939273509

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