Artificial Intelligence Course in Chennai

• MLOps lifecycle and pipeline
• MLOps components and processes

• An overview of Azure Machine Learning
• Steps to deploy machine learning models using Azure

Understanding Version Control

• Definition and importance of version control
• Different types of version control systems
• Introduction to SVN

Exploring Git

• Life cycle of Git
• Common commands and their uses
• Branch operations in Git, including creation and merging
• Conflict resolution during merges
• Workflow best practices
• Collaborating with GitHub
• Collaborative techniques using pull requests
• Different methods of GitHub authentication: SSH and HTTP

Establish a solid foundation in:

• Extraction, loading, and transformation of data to derive insights
• Manipulation and pre-processing of datasets
• Engineering features and scaling data according to the problem's demands
• Building models using various machine learning algorithms, both supervised and unsupervised
• Evaluating and monitoring machine learning models

Constructing a Recommendation Engine

• Techniques in machine learning for recommending movies, restaurants, books, etc.

Text Classification and Rating Prediction

• Building machine learning models for text data, predicting ratings and sentiments

Predictive Analysis on Census Data

• Analysing population data and predicting/classifying features such as population count, income levels, etc.

Real Estate Price Prediction

• Building a model to forecast housing prices based on multiple features

Real-time Object Detection

• Building a machine learning model for detecting objects

Stock Market Insights

• Analysing historical stock data to gain insights on specific stocks through feature engineering and selection

Predicting Consumer Behaviour in Banking

• Classification problem using machine learning models to predict consumer actions

Designing an AI Chatbot

• Using the NLTK python library, apply machine learning techniques to create an interactive AI chatbot

Fundamentals of Text Processing

• ]Text mining, cleaning, and pre-processing techniques
• Tokenization: Various tokenizers, frequency distribution, stemming, POS tagging, lemmatisation, and entity recognition

Text Analysis

• Text classification, sentiment analysis using NLTK
• Machine Learning overview, words, term frequency, countvectorizer, inverse document frequency, text conversion, confusion matrix, Naive Bayes classifier

Understanding Language Structure

• Language modelling, predicting tag sequences, syntax trees, context-free grammars, chunking, paraphrasing techniques

Building with NLP

• Using NLP techniques, construct a recommendation engine and an AI chatbot assistant

Introduction to RBM, DBNs, & Variational AutoEncoder

• Overview of RBM and autoencoders
• Utilising RBM for deep neural networks and collaborative filtering
• Features and applications of autoencoders
• Implementing Object Detection using CNN
• Building a CNN using TensorFlow
• Understanding convolutional, dense, and pooling layers

Generating Images and Advanced Models

• Neural style image generation and deep generative models
• Overview of generative models, sequence-to-sequence model (LSTM)

Parallel and Distributed Computing for Deep Learning

• Differentiating distributed and parallel computing
• Introduction to TensorFlow distributed computing and parallel training

Deepening Knowledge in Reinforcement Learning

• Mapping the human mind with DNN
• Components and architecture of ANN

Deployment Techniques for Deep Learning Models

• Persisting models, saving, and serialising in Keras
• Deploying models with TensorFlow Serving, Docker, Kubernetes, and more
• Introduction to TensorFlow Lite and CNN model deployment

Dive into Big Data and Spark

• Overview of Apache Spark framework and RDDs
• Exploring gaps in traditional computing methods

Advanced RDD Techniques

• Working with RDD persistence, caching, transformations, actions, and functions
• The role of Key-Value pairs in RDDs and partitioning
• Mastering Advanced Concepts and Spark-Hive Integration
• Passing functions to Spark and understanding Spark SQL architecture
• UDFs, DataFrames, data loading techniques, and performance tuning
• Integrating Spark with Hive

• Strategies for Job Searching
• Crafting a Resume
• Optimising LinkedIn Profiles
• Preparing for Interviews with Industry Expert Sessions
• Simulated Interview Practices
• Potential placements with X+ hiring partners after passing the Placement Readiness Test.