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Data Science

5.0 Created by potrace 1.15, written by Peter Selinger 2001-2017

5.0 Created by potrace 1.15, written by Peter Selinger 2001-2017

4.6 Created by potrace 1.15, written by Peter Selinger 2001-2017

Best Data Science Training Institute in Hyderabad, Kukatpally & KPHB

SSSIT Computer Education is rated as one of the Best Data Science Training Institutes in KPHB, Kukatpally and Hyderabad by trained students. Here Trainers are highly qualified & experienced in delivering Training and Development delivers the content as per industry expectation from a Data Scientist. The Data Science Training class consists of more project oriented scenarios with the Industry Aligned Curriculum

What is Data Science?

You will be exposed to the following Data Science With AI, ML, Gen.AI & Agentic AI training content

  • Python Programming
    • Introduction to Data Science
    • Python Basics
    • Python Data Types and Utilities
    • Control Structures and Loops
    • Strings
    • Exception Handling
    • Collection Framework
    • List
    • Set
    • Tuple
    • Dictionary and Dictionary Comprehension
    • Functions
    • Inbuilt vs User Defined Functions
    • Function Arguments and Types
    • Global Variable vs Local Variable
    • Packages
    • Lambda Functions
    • Map and Reduce
    • Object Oriented Programming (OOP)
    • Class and Object
    • Methods
    • Decorators
    • Polymorphism
    • File Handling
    • Docstrings
    • Modularization
    • Pickling and Unpickling
    • NumPy
    • Pandas
    • Matplotlib
    • Seaborn
    • SciPy
    • Statsmodels

  • Mathematics
    • Set Theory
    • Data Representation and Database Operations
  • Combinatorics
    • Feature Selection
    • Permutations and Combinations for Sampling
    • Hyperparameter Tuning
    • Experiment Design
    • Data Partitioning and Cross-Validation Probability
    • Basics
    • Theoretical Probability
    • Empirical Probability
    • Addition Rule
    • Conditional Probability
    • Multiplication Rule
    • Total Probability
    • Probability Decision Tree
    • Bayes Theorem
    • Sensitivity and Specificity in Probability
    • Bernoulli Naive Bayes, Gaussian Naive Bayes, Multinomial Naive Bayes
  • Linear Algebra
    • Linear Equations
    • Matrices (Matrix Algebra: Vector-Matrix and Matrix-Matrix Multiplication)
    • Determinant
    • Eigenvalue and Eigenvector
    • Euclidean Distance and Manhattan Distance
  • Calculus
    • Differentiation
    • Max and Min
    • Partial Differentiation
    • Indices and Logarithms

  • Statistics Introduction
    • Types of Data
    • Measures of Central Tendency
    • Descriptive Statistics and Measures of Symmetry
    • Measurement of Spread
    • Levels of Data Measurement
    • Variable
    • Frequency Distribution Table
    • Types of Variables
    • Correlation, Regression, and Collinearity
    • Others
    • Bias and Variance in ML
    • Entropy in ML
    • Information Gain
    • Surprise in ML
    • Loss Function and Cost Function
    • Inferential Statistics

  • SQL Introduction
    • Keys
    • Constraints
    • CRUD Operations
    • SQL Languages
    • SQL Commands
    • Operators
    • Wild Cards
    • Aggregate Functions
    • SQL Joins

  • ML Introduction
    • What is Machine Learning?
    • Types of Machine Learning Methods
    • Classification Problem in General
    • Validation Techniques: CV, OOB
    • Different Types of Metrics for Classification
    • Curse of Dimensionality
    • Feature Transformations
    • Feature Selection
    • Imbalanced Dataset and Its Effect on Classification
    • Bias Variance Tradeoff
  • Important Element of Machine Learning
  • Multiclass Classification
    • One-vs-All
    • Overfitting and Underfitting
    • Error Measures
    • PCA Learning
    • Statistical Learning Approaches
    • Introduction to SKLEARN Framework
  • Data Processing
    • Creating Training and Test Sets, Data Scaling and Normalization
    • Feature Engineering: Adding New Features as Required, Modifying Data
    • Data Cleaning: Treating Missing Values and Outliers
    • Data Wrangling: Encoding, Feature Transformations, Feature Scaling
    • Feature Selection: Filter Methods, Wrapper Methods, Embedded Methods
    • Dimension Reduction: PCA (Sparse PCA, Kernel PCA), Singular Value Decomposition
    • Non-Negative Matrix Factorization
  • Regression
    • Introduction to Regression
    • Mathematics Involved in Regression
    • Regression Algorithms
    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Regression
    • Lasso Regression
    • Ridge Regression
    • Elastic Net Regression
  • Evaluation Metrics for Regression
    • Mean Absolute Error (MAE)
    • Mean Squared Error (MSE)
    • Root Mean Squared Error (RMSE)
    • R2
    • Adjusted R2
  • Classification
    • Introduction
    • K-Nearest Neighbors
    • Logistic Regression
    • Support Vector Machines (Linear SVM)
    • Linear Classification
    • Kernel-Based Classification
    • Non-Linear Examples
    • 2 Features Form Straight Line and 3 Features Form Plane
    • Hyperplane and Support Vectors
    • Controlled Support Vector Machines
    • Support Vector Regression
    • Kernel SVM (Non-Linear SVM)
    • Naive Bayes
    • Decision Trees
    • Random Forest / Bagging
    • AdaBoost
    • Gradient Boost
    • XGBoost
    • Evaluation Metrics for Classification
  • Clustering
    • Introduction
    • K-Means Clustering
    • Finding the Optimal Number of Clusters
    • Optimizing the Inertia
    • Cluster Instability
    • Elbow Method
    • Hierarchical Clustering
    • Agglomerative Clustering
    • DBSCAN Clustering
  • Association Rules
    • Market Basket Analysis
    • Apriori Algorithm
  • Recommendation Engines
    • Collaborative Filtering
    • User-Based Collaborative Filtering
    • Item-Based Collaborative Filtering
  • Time Series and Forecasting
    • What is Time Series Data
    • Different Components of Time Series Data
    • Stationarity of Time Series Data
    • ACF, PACF
    • Time Series Models
    • AR
    • ARMA
    • ARIMA
    • SARIMAX
  • Model Selection and Evaluation
  • Overfitting and Underfitting
    • Bias-Variance Tradeoff
    • Hyperparameter Tuning
    • Joblib and Pickling
  • Others
    • Dummy Variable, One-Hot Encoding
    • GridSearchCV vs RandomizedSearchCV
  • ML Pipeline
  • ML Model Deployment in Flask

  • Artificial Intelligence
    • Introduction to Neural Network
    • Biological and Artificial Neuron
    • Introduction to Perceptron
    • Perceptron Learning Rule and Drawbacks
    • Multilayer Perceptron and Loss Function
  • Artificial Neural Networks (ANN)
    • What is a Neuron
    • ANN Architecture
    • Neural Network Activation Functions
    • Step Function
    • Linear Function
    • Sigmoid Function
    • Tanh Function
    • ReLU Function
    • Backpropagation vs Forward Pass
    • Gradient Descent
    • Fine-Tuning Neural Network Hyperparameters
    • Number of Hidden Layers and Hidden Neurons
    • Optimizer
    • Loss Functions
    • Finding Optimal Hidden Layers and Hidden Neurons in ANN
    • Forward and Backward Propagation, Epoch
    • Training MLP: Backpropagation
    • Cost Function
    • Introduction to PyTorch
    • Regularization
    • Optimizers
    • Hyperparameters and Tuning
  • TensorFlow Framework
    • Introduction to TensorFlow
    • TensorFlow Basic Syntax
    • TensorFlow Graphs
    • Variables and Placeholders
    • TensorFlow Playground
  • Computer Vision
    • Human Vision vs Computer Vision
    • CNN Architecture
    • Convolution, Max Pooling, Flatten Layer, Fully Connected Layer
    • Striding and Padding
    • Max Pooling
    • Data Augmentation
    • Introduction to OpenCV and YOLOv3 Algorithm
  • Image Processing with OpenCV
    • Image Basics with OpenCV
    • Opening Image Files with OpenCV
    • Drawing on Images with OpenCV
    • Face Detection with OpenCV
  • Video Processing with OpenCV
    • Introduction to Video Basics and Object Detection
    • Object Detection with OpenCV
  • RNN (Recurrent Neural Network)
    • Introduction to RNN
    • Backpropagation Through Time
    • Simple RNN Backward Propagation
    • Input and Output Sequences
    • RNN vs ANN
    • Vanishing and Exploding Gradient Problem
    • End-to-End Deep Learning Projects with Simple RNN
    • Different Types of RNN: LSTM, GRU
  • LSTM
    • Why LSTM
    • LSTM Architecture
    • Forget Gate in LSTM
    • Input Gate and Candidate Memory in LSTM
    • Output Gate in LSTM
    • Training Process in LSTM
    • Variants of LSTM
    • GRU RNN In-Depth Intuition
    • LSTM and GRU End-to-End Deep Learning Project

  • Text Processing
    • Introduction, What is a Token, Tokenization
    • Stop Words in spaCy Library
    • Stemming
    • Lemmatization
    • Lemmatization through NLTK
    • Lemmatization using spaCy
    • Word Frequency Analysis
    • Counter
    • Part of Speech and POS Tagging
    • POS using spaCy and NLTK
    • Dependency Parsing
    • Named Entity Recognition (NER)
    • NER with NLTK
    • NER with spaCy
    • Text Cleaning
    • Texts, Tokens
    • Basic Text Classification based on Bag of Words
  • Document Vectorization
    • Bag of Words
    • TF-IDF Vectorizer
    • Topic Modelling using LDA
    • Sentiment Analysis
    • Email Classification
    • Text Clustering

  • Open AI
    • Introduction to Open AI
    • Generative AI
    • ChatGPT (3.5)
    • LLM (Large Language Model)
    • Classification Tasks with Generative AI
    • Content Generation and Summarization with Generative AI
    • Information Retrieval and Synthesis Workflow with Gen AI
  • Time Series and Forecasting
    • Time Series Forecasting using Deep Learning
    • Seasonal-Trend Decomposition using LOESS (STL) Models
    • Bayesian Time Series Analysis
  • Sequence to Sequence Architecture
    • Encoder and Decoder
    • In-Depth Intuition of Encoder and Decoder
    • Sequence to Sequence Architecture
    • Problems with Encoder and Decoder
  • Attention Mechanism
    • Seq2Seq Networks
    • Attention Mechanism Architecture
  • Transformers
    • What and Why to Use Transformers
    • Understanding the Basic Architecture of Encoder
    • Self-Attention Layer Working
    • Multi-Head Attention
    • Feed Forward Neural Network with Multi-Head Attention
    • Positional Encoding
    • Layer Normalization
    • Layer Normalization Examples
    • Complete Encoder Transformer Architecture
    • Decoder Plan of Action
    • Decoder Masked Multi-Head Attention
    • Encoder and Decoder Multi-Head Attention
    • Decoder Final Linear and Softmax Layer
  • Hugging Face Platform and API
    • Introduction to Hugging Face
    • Hands-on with Transformers, HF Pipeline, Datasets, and LLMs
    • Data Processing, Tokenization, and Feature Extraction with Hugging Face
    • Fine-Tuning using Pretrained Models
    • Hugging Face API Key Generation
    • Project: Text Summarization with Hugging Face
    • Project: Text-to-Image Generation with LLMs using Hugging Face
    • Project: Text-to-Speech Generation with LLMs using Hugging Face
    • Hugging Face Platform and its API

  • Getting Started with Gen AI
    • Introduction to Gen AI and LLMs
    • Pretraining and Fine Tuning
    • Real-Time Support Chatbot Use Case
    • RAG, RLHF, LangChain, Few Shot Learning
    • Llama Model
  • Gen AI Practicals
    • Tasks (Sentiment Analysis)
    • Text Summarization, Translation, Question-Answer Tasks
    • Token Classification, Fill Mask, Text Generation
    • Feature Extraction, Zero Shot Classification
    • Selecting the Right Model for the Task, Preprocessing
    • Model Inference, Working of Softmax
    • More about Tokenizer: AutoTokenizer, AutoModel
    • Introduction to Hugging Face, Downloading a Model from Hugging Face, Execute NLP
  • Transformers Internals and Attention Blocks
    • Model Selection
    • Preprocessing: Tokenizer
    • Postprocessing: Softmax
    • Attention Block and Multilayer Perceptron: Decoding Attention Pattern
    • Attention Pattern: Vectors and Matrices
    • Embedding Matrix and Unembedding Matrix
    • Query Matrix and Query Vector, Key Vector and Key Matrix
    • Attention Mechanism: Embedding Contextual Knowledge
    • Multi Layer Perceptron: Feed Forward Layer
    • How MLP Works
  • Training a Model
    • Introduction to the Bigram Model, Understanding Tensors
    • Bigram Model
    • Tensors
    • Pretraining with Matrices
    • Word Generation
    • Precomputed Probabilities
    • Model Quality
    • One Hot Encoding and Forward Pass
    • Backward Pass and Adjusting Weights
  • Model Pre-Training
    • Implementation of Multilayer Perceptron
    • Probability Calculation
    • Tuning Parameters
    • Optimized Industrial Approach: Mini Batches
  • Getting Started with RAG
    • Introduction to RAG
    • RAG Practicals (Using Google Colab)
    • RAG Practicals Using Databricks CE and Local
    • Accessing Gated Models
    • RAG Internals Explained
    • RAG Pipeline: Text to Embeddings
    • RAG Pipeline: Store Embeddings and Retrieve Answers with LLM
  • RAG End-to-End Production Pipeline
    • End-to-End Production Grade Pipeline
    • Generating Embeddings
    • Building Retriever and RAG LangChain Creation
    • Registering LangChain and Creating Serving Endpoint
  • LangChain Essentials
    • Understanding LangChain Framework
    • Inferring Large Models on Cloud
    • Working with OpenAI
    • Message Structure: System, Human, AI Message
    • Use Case: Few Shot Learning
    • Prompt Template
    • Task and Chain: Runnable Lambda, Runnable Sequence
    • Runnable Parallel
  • RAG Application using LangChain
    • Advanced RAG Application Use Case with LangChain
    • Document Loaders
    • Chunking Strategies and Embedding Model Selection
    • Retriever Configs, Search Types
    • Conversational RAG Solution
    • RAG Challenges
  • Query Optimization
    • Query Transformation
    • Query Routing
    • Indexing Strategies
    • Tracing and Debugging with LangSmith
    • More on Query Transformation Techniques: Query Rewrite
    • Query Optimization Techniques: Multi Query
    • RAG Fusion, Reciprocal Ranking
    • Query Decomposition, Sub-Query, Chain of Thought (CoT)
    • Query Decomposition, Multiple Independent Sub-Queries
    • Step-Back Questions, Few Shot Prompting

  • Agentic AI
    • Agentic Behavior in AI
    • Introduction to LangGraph
    • LangGraph Use Case: Natural Language to PySpark DataFrame
    • LangGraph Use Case: PySpark DataFrame to Spark SQL
    • Binding Tools to LLMs Using LangGraph
  • Agentic AI Tools
    • Registering Tools with LLMs in LangGraph
    • Tool Binding with ReAct Architecture
    • State Persistence in LangGraph Using Checkpoints
    • Structured Outputs with Pydantic Models
    • Reducers to Retain Full Conversation History
    • Handling Long Conversations with Message Filtering
    • Dynamic Summarization in LangGraph Agents
    • Persisting LangGraph State with SQLite Checkpointers
  • CRAG and Agentic AI Projects
    • Corrective RAG (CRAG)
    • Building a LangGraph-Based CRAG Application
    • Agentic AI Project: SQL Querying Agent with Tools
    • Designing a Multi-Role LLM System with Agents
  • LLM Fine-Tuning
    • Introduction to LLM Fine-Tuning
    • RAG vs Fine-Tuning
    • Types of Fine-Tuning
    • Full Fine-Tuning and Parameter-Efficient Fine-Tuning (PEFT)
    • LoRA and QLoRA (Reducing Memory and Compute Requirements)
    • Practical Fine-Tuning Demo
    • Hyperparameters

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