Introduction to Text Mining, Text Preprocessing, Document-Term Matrix (DTM) and TF-IDF, Exploratory Text Analysis, Sentiment Analysis

Advanced Certification in Data Science & Gen AI with Visualizations Tools Introduction to Text Mining, Text Preprocessing, Document-Term Matrix (DTM) and TF-IDF, Exploratory Text Analysis, Sentiment Analysis

Advanced Certification in Data Science & Gen AI with Visualizations Tools

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Advanced Excel

Orientation (Introduction to Data Science, Scope of Data Science)

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Python

Introduction to Python, Why Python, Variables, Data Types, Type castings, Strings, Indexing

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Operators and Conditional Statements, Looping Statements and it's Control Statement

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Lambda Functions, *args, **kwargs, Functions

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Data Structures - List, Tuple and List Comprehensions

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Data Structures - Set and Dictionaries

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Classes, Objects and Constructors, Inheritance

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Polymorphism, Abstraction and Encapsulation

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Connecting to Databases, Establishing connections to databases, Executing SQL Queries, ORM (Object-Relational Mapping), Working with NoSQL Databases

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Introduction of Numpy, and Pandas

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Introduction of Seaborn and Matplotlib

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Statistics

Introduction to Statistics, Descriptive Statistics, Sample, Population, Major of Central Tendency, Standard Deviation,

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Variance, Range, IQR, Outliers, Correlation, Covariance Skewness, Kurtosis, Probability

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Probability, Probability distributions, Central Limit Theorem, Binomial and Poisson Distribution

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Normal Distribution, Type I & Type II Error

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T-test, Z-test, Hypothesis, Testing Interview Questions

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Machine Learning

Introduction to ML, Types of variables, Encoding, Normalization, Standardization, Types of ML, Linear Regression

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Linear Regression, Logistic Regression, SVM, KNN, Naïve Bayes, Decision Tree, Random Forest

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Mean Absolute Error, Mean and Root Mean Square Error, Confusion Matrix, R2 Score, Adjusted R2 Score,F1 Score

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Classification Report, AUC ROC, Accuracy, Ensemble Techniques, Random Forest, Xgboost

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Unsupervised Machine Learning, PCA, Clustering, k-Means Clustering and Hierarchical clustering

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Deep Learning

Introduction to Neural Network, Foreward Propagation, Activation Function

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Activation Function(Linear, Sigmoid, Relu, Leaky Relu), Optimizers, Gradient Descent, Stochastics Gradient Descent

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Mini batch Gradient Descent, Adagrad, Padding, Pooling, Convolution

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Checkpoints and Neural Networks Implementation and Introduction to Time Series Analysis,

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Various components of the TSA, Decomposition Method (Additive Method and Multiplicative)

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ARMA and ARIMA

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R- Programming

Introduction to R, Installing R and RStudio, Basics of RStudio IDE, Writing and executing R scripts, Variables and Data Type in R, Operators

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Creating vectors, Vector indexing and slicing, Vectorized operations, Creating matrices, Matrix operations, Matrix indexing, Creating lists, Creating data frames, Indexing and manipulating lists and data frames

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Conditional statements, Loops, Applying functions, Flow Control, Functions in R, Object-Oriented Programming in R, S3 and S4 classes, Methods and inheritance, Creating and using objects

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Creating and using factors, Working with dates and times, Reading and writing(CSV files and Excel files), Introduction to the readr and readxl packages

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Introduction to dplyr, Selecting, filtering, and arranging data, Grouping data, Summarizing data with summarize and mutate

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Data Manipulation in R- dplyr, Data Manipulation & Data Visualization in R- tidyr

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Introduction to Text Mining, Text Preprocessing, Document-Term Matrix (DTM) and TF-IDF, Exploratory Text Analysis, Sentiment Analysis

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Install Necessary Packages, Create a New Package, Package Structure, Writing Functions, Documenting Functions, Testing Your Package, Building and Checking and Sharing Your Package

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Introduction to APIs, Using the 'httr' Package, GET & POST Request, and Authentication, Introduction to Web Scraping, Using the 'rvest' Package, Handling Dynamic Content, Handling Sessions and Cookies

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Connecting to Databases in R, Packages Installation, Connect to Database, Execute Queries, Write Data, Disconnect and Error Handling

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Project Session

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SQL

Basic of Database,Types of Database,Data Types, SQL Operators, Expression, Create, Insert

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Drop, Truncate, Delete, Alter, Update, Select, Range, Operater, IN,Wildcard, Like, Clause

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Constraint, Aggregation Function, Group by, Order by, Having

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Joins, Case, Complex Queries, Doubt Clearing

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Tableau

Tableau Desktop, Tableau products

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Data import, Measures, Filters

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Data transformation, Marks, Dual Axis

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Manage worksheets, Data visualization, Dashboarding,Project

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Power BI

Power BI Platform, Process Flow

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Features, Dataset, Bins

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Pivoting, Query Group, DAX Function

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Formula, Charts, Reports, Dashboards

No-SQL

Introduction, SQL vs NoSQL, Data Model, Data types, Object ID, DAta type, Binary Data, Date, Null, Boolean, Integer, String

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Collection method, queries, CRUD Operation, Insert, Find, Update, Delete, Validate, Bulk write, Delete one

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Java

Introduction to the Java, Installation, Syntax main()/printIn()/print()/ Variable [String, Int, Boolean, float, char], Datatypes, Operators

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Conditions, Loop, Methods, Class, File Handling

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Introduction of BigData & Hadoop

Types of Data, Introduction to Bigdata(History, V's of Bigdata, Advantages & Disadvantages of BigData ), Big Data Applications in Various Sectors, Introduction to Hadoop, Scaling (Horizontal and Vertical), Challenges in Scaling, Parallel Computing, Distributed Computing and Hadoop, Hadoop Tools Overview, Big Data Analytics Lifecycle

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On Premises Installation Oracle Virtual Box and setup of VM & Ubuntu, Basic Linux command, Download and Installation of Hadoop, Introduction to Hadoop, Core components of Hadoop, Hadoop working, Principle

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VM creation on Cloud (AZURE), Configuration & Insight to Single Node Hadoop Deployment(bsshrc, hadoop-env, core-site, hdfs-site, mapred-site, yarn-site) , Format HDFS Namenode.

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HDFS Architecture, Hadoop Commands and Implementation

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Mapreduce, Mapreduce Implementation

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Introduction to Hive, Hive Installation, Hive Implementaion

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Hive Query Language, SQL Opeartions

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HIVE_SQL Opeartions

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SPARK

Installation of Spark, PySpark, Introduction to Sqoop, Installation of Sqoop,

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PySpark Query, Installation of Hbase, Hbase Query

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PIG Installation and Query

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PIG Query, Oozie

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Flume and Doubt Clear

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Project Session

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Computer Vision

Introduction to Image Processing, Feature Detection, OpenCV

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Convolution, Padding, Pooling & its Mechanisms

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Forward Propagation & Backward Propagation for CNN

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CNN Architectures like AlexNet, VGGNet, InseptionNet, ResNet, Transfer Learning

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NLP

Introduction to Text Mining, Text Processing using Python and Introduction to NLTK

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Sentiment Analysis, Topic Modeling (LDA) and Name- Entity Recognition

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BERT (Bidirectional Encoder Representations from Transformers), Text Segmentation, Text Mining, Text Classification

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Automatic Speech Recognition, Introduction to Web Scraping

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RL Framework, Component of RL Framework, Exampes of Systems

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RL Framework, Component of RL Framework, Exampes of Systems

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Types of RL Systems, Q-Learning

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Project Session

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Introduction to Gen AI & Huggingface transformers platform

Introduction to AI, Hype vs. Reality, Business Applications, Ethical Considerations, Introduction to Generative AI, From Text Generation to Multimodal Models, Potential and Challenges

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Introduction to open source Huggingface transformers platform, Review of NLP Basics & Text Pre-processing, Introduction to NLP Concepts: Language Representations, Tokenization, Part-of-Speech Tagging, Text Preprocessing

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Feature Engineering: Normalization, Stemming, Lemmatization, Stop Word Removal, Understanding key NLP Applications using Huggingface platform

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Sentiment analysis, Sentence classification, Generating text, Extracting an answer from text

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Language Models and Transformer Models

Understanding language models, Probability-based language models, Unsupervised learning language representations, Introduction to transformer models, What are transformer models

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Types of models: encoder –decoder, decoder only, Attention mechanism, Tasks that transformer models can do: translation, text summarization, Q&A, text generation, Zero shot, few shot text classification

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Large Language Models (LLMs)

Introduction to Large Language Models (LLMs), - Structure of popular models - Types of Models: text to text, text to image, text to video, multimodal

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Other types of Generative AI algorithms, - GANs ( Generative Adverserial Networks), - Variational Autoencoders (VAEs), Diffusion Models, Mixture of Experts, - Diffferent models available currently for image ( DALLE-2, Midjourney)

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Hands on practice of NLP tasks using Huggingface library and opensource language models such as Bloom for finetuning a LLM, zero and few shot classification, -Applications of Generative AI in business

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- Customer Insights & Sentiment Analysis - Personalized Marketing & Content Creation - Chatbots: Automating Customer Service and Support - Document Processing Automation

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Langchain, AI Application Stack and Ethical Considerations

Langchain, Applied use case for Gen AI – hands on exercise - Designing a custom chatbot - Data analytics using Gen AI model such as OpenAI API

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AI Application Stack: Infrastructure & foundation layer - Overview of AI infrastructure: cloud platforms, GPU, and distributed computing, - Setting up an AI environment for generative models - Infrastructure considerations for scalable AI applications - Retrieval augmentation generation or RAG

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Hallucination, Data Privacy, Ethics, and Environmental Impact of AI & future of Work - Importance of data privacy in AI applications - Ethical considerations in AI development and Deployment - Environmental Impact and Sustainability in AI - The Future of Work: How AI Will Reshape Roles and Responsibilities

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Project Session

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Announcements Reviews Course Info

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Advanced Certification in Data Science & Gen AI with Visualizations Tools Introduction to Text Mining, Text Preprocessing, Document-Term Matrix (DTM) and TF-IDF, Exploratory Text Analysis, Sentiment Analysis

Advanced Certification in Data Science & Gen AI with Visualizations Tools

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