Best Full Stack Data Science AI Training in KPHB, Kukatpally

Full Stack Data Science with Generative AI

Python || Statistical Modeling || Machine Learning || Deep Learning || NLP || Computer Vision || SQL || Power BI || MLOps

Course Overview

In today’s fast-paced world, data science and AI are transforming every industry. As businesses rely more on intelligent automation, professionals with expertise in Full Stack Data Science with Generative AI are in high demand. This course equips you with the skills to analyze data, build AI models, and deploy AI-driven applications.

Moreover, you will gain hands-on experience with Python, SQL, Machine Learning, and Deep Learning. Additionally, you will explore cutting-edge technologies like Large Language Models (LLMs), ChatGPT, Google Gemini, and AI-powered automation.

By the end of this training, you will have a strong foundation in AI, data science, and model deployment. More importantly, you will work on real-world projects, preparing you for high-paying roles in AI and Data Science.

Course Details:

Course Price:

with videos : ₹ 27,000/-
with out videos : ₹ 21,000/-

Lesson Duration

4 - 5 Months

Language:

English

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Description

Our Full Stack Data Science with Generative AI Training in KPHB is designed for learners at all levels. Whether you are a beginner or an experienced professional, this course will help you master data science and AI. First, you will start with the basics of Python, SQL, and Statistics. Then, you will progress to advanced topics like Machine Learning, Deep Learning, and NLP.

Furthermore, the course includes practical training in Generative AI tools, LLMs, and cloud-based AI deployment. Not only will you learn AI model fine-tuning, but you will also explore real-world AI applications. Most importantly, every module includes hands-on practice, ensuring you gain industry-relevant skills.

With structured guidance and expert mentorship, you will develop confidence in working with AI-powered solutions. Thus, by the end of this program, you will be job-ready with a strong portfolio of AI projects.

Course Objectives

Learn Python, SQL, and Statistics for AI-driven applications.
Gain expertise in Machine Learning and Deep Learning with hands-on projects.
Explore NLP, Generative AI, and Large Language Models (LLMs) like ChatGPT and Google Gemini.
Develop AI-powered solutions using Prompt Engineering and Model Fine-Tuning.
Implement Cloud-based AI Deployment and DevOps strategies for scalability.
Work on real-world case studies and AI automation projects.
Build a strong foundation in data science, AI ethics, and model optimization.

Prerequisites

A basic understanding of Python and SQL is beneficial, but not mandatory.
Some familiarity with mathematics and statistics can be helpful.
A keen interest in Artificial Intelligence, Machine Learning, and Data Science.
No prior experience in coding or AI? No problem! Our structured approach makes learning easy.

Course Curriculum

Introduction to Data Science
- Introduction to Data Science
- Discussion on Course Curriculum
- Introduction to Programming
Python Basics
- Introduction to Python: Installation and Running (Jupyter Notebook, .py file from terminal, Google Colab)
- Data types and type conversion
- Variables
- Operators
- Flow Control : If, Elif, Else
- Loops
- Python Identifier
- Building Funtions (print, type, id, sys, len)
Python - Data Types & Utilities
- List, List of Lists and List Comprehension
- List creation
- Create a list with variable
- List mutable concept
- len() || append() || pop()
- insert() || remove() || sort() || reverse()
- Forward indexing
- Backward Indexing
- Forward slicing
- Backward slicing
- Step slicing
Set
- SET creation with variable
- len() || add() || remove() || pop()
- union() | intersection() || difference()
Tuple
- TUPLE Creation
- Create Tuple with variable
- Tuple Immutable concept
- len() || count() || index()
- Forward indexing
- Backward Indexing
Dictionary and Dictionary comprehension
- create a dictionary using variable
- keys:values concept
- len() || keys() || values() || items()
- get() || pop() || update()
- comparision of datastructure
- Introduce to range()
- pass range() in the list
- range() arguments
- For loop introduction using range()
Functions
- Inbuilt vs User Defined
- User Defined Function
- Function Argument
- Types of Function Arguments
- Actual Argument
- Global variable vs Local variable
- Anonymous Function | LAMBDA
Packages
Map Reduce
OOPs
Class & Object
- what is mean by inbuild class
- how to creat user class
- crate a class & object
- __init__ method
- python constructor
- constructor, self & comparing objects
- instane variable & class variable
Methods
- what is instance method
- what is class method
- what is static method
- Accessor & Mutator
Python DECORATOR
- how to use decorator
- inner class, outerclass
- Inheritence
Polymorphism
- duck typing
- operator overloading
- method overloading
- method overridding
- Magic method
- Abstract class & Abstract method
- Iterator
- Generators in python
Python - Production Level
- Error / Exception Handling
- File Handling
- Docstrings
- Modularization
Pickling & Unpickling
Pandas
- Introduction, Fundamentals, Importing Pandas, Aliasing, DataFrame
- Series – Intro, Creating Series Object, Empty Series Object, Create series from List/Array/Column from DataFrame, Index in Series, Accessing values in Series
- NaN Value
- Series – Attributes (Values, index, dtypes, size)
- Series – Methods – head(), tail(), sum(), count(), nunique() etc.,
- Date Frame
- Loading Different Files
- Data Frame Attributes
- Data Frame Methods
- Rename Column & Index
- Inplace Parameter
- Handling missing or NaN values
- iLoc and Loc
- Data Frame – Filtering
- Data Frame – Sorting
- Data Frame – GroupBy
- Merging or Joining
- Data Frame – Concat
- DataFrame - Adding, dropping columns & rows
- DataFrame - Date and time
- DataFrame - Concatenate Multiple csv files
Numpy
- Introduction, Installation, pip command, import numpy package, Module Not Found Error, Famous Alias name to Numpy
- Fundamentals – Create Numpy Array, Array Manipulation, Mathematical Operations, Indexing & Slicing
- Numpy Attributes
- Important Methods- min(),max(), sum(), reshape(), count_nonzero(), sort(), flatten() etc.,
- adding value to array of values
- Diagonal of a Matrix
- Trace of a Matrix
- Parsing, Adding and Subtracting Matrices
- "Statistical Functions: numpy.mean()
- numpy.median()
- numpy.std()
- numpy.sum()
- numpy.min()"
- Filter in Numpy
Matplotlib
- Introduction
- Pyplot
- Figure Class
- Axes Class
- Setting Limits and Tick Labels
- Multiple Plots
- Legend
- Different Types of Plots
- Line Graph
- Bar Chart
- Histograms
- Scatter Plot
- Pie Chart
- 3D Plots
- Working with Images
- Customizing Plots
Seaborn
- catplot() function
- stripplot() function
- boxplot() function
- violinplot() function
- pointplot() function
- barplot() function
- Visualizing statistical relationship with Seaborn relplot() function
- scatterplot() function
- regplot() function
- lmplot() function
- Seaborn Facetgrid() function
- Multi-plot grids
- Statistical Plots
- Color Palettes
- Faceting
- Regression Plots
- Distribution Plots
- Categorical Plots
- Pair Plots
Scipy
- Signal and Image Processing (scipy.signal, scipy.ndimage):
- Linear Algebra (scipy.linalg)
- Integration (scipy.integrate)
- Statistics (scipy.stats)
- Spatial Distance and Clustering (scipy.spatial)
Statsmodels
- Linear Regression (statsmodels.regression)
- Time Series Analysis (statsmodels.tsa)
- Statistical Tests (statsmodels.stats)
- Anova (statsmodels.stats.anova)
- Datasets (statsmodels.datasets)

Set Theory
- Data Representation & Database Operations
Combinatorics
- Feature Selection
- Permutations and Combinations for Sampling
- Hyper parameter Tuning
- Experiment Design
- Data Partitioning and Cross-Validation
Probability
- Basics
- Theoretical Probability
- Empirical Probability
- Addition Rule
- Multiplication Rule
- Conditional Probability
- Total Probability
- Probability Decision Tree
- Bayes Theorem
- Sensitivity & Specificity in Probability
- • Bernouli Naïve Bayes, Gausian Naïve Bayes, Multinomial Naïve Bayes
Distributions
- Binomial, Poisson, Normal Distribution, Standard Normal Distribution
- Guassian Distribution, Uniform Distribution
- Z Score
- Skewness
- Kurtosis
- Geometric Distribution
- Hyper Geometric Distribution
- Markov Chain
Linear Algebra
- Linear Equations
- Matrices(Matrix Algebra: Vector Matrix Vector matrix multiplication Matrix matrix multiplication)
- Determinant
- Eigen Value and Eigen Vector
Euclidean Distance & Manhattan Distance
Calculus
- Differentiation
- Partial Differentiation
- Max & Min
Indices & Logarithms

Introduction
- Population & Sample
- Reference & Sampling technique
Types of Data
- Qualitative or Categorical – Nominal & Ordinal
- Quantitative or Numerical – Discrete & Continuous
- Cross Sectional Data & Time Series Data
Measures of Central Tendency
- Mean, Mode & Median – Their frequency distribution
Descriptive statistic Measures of symmetry
- skewness (positive skew, negative skew, zero skew)
- kurtosis (Leptokurtic, Mesokurtic, Platrykurtic)
Measurement of Spread
- Range, Variance, Standard Deviation
Measures of variability
- Interquartile Range (IQR)
- Mean Absolute Deviation (MAD)
- Coefficient of variation
- Covariance
Levels of Data Measurement
- Nominal, Ordinal, Interval, Ratio
Variable
- Types of Variables.
- Categorical Variables - Nomial variable & ordinal variables
- Numerical Variables: discreate & continuous
- Dependent Variable
- Independent Variable
- Control Moderating & Mediating
Frequency Distribution Table
- Nominal, Ordinal, Interval, Ratio
Types of Variables
- Categorical Variables - Nomial variable & ordinal variables
- Numerical Variables: discreate & continuous
- Dependent Variable
- Independent Variable
- Control Moderating & Mediating
Frequency Distribution Table
- Relative Frequency, Cumulative Frequency
- Histogram
- Scatter Plots
- Range
- Calculate Class Width
- Create Intervals
- Count Frequencies
- Construct the Table
Correlation, Regression & Collinearity
- Pearson & Spearman Correlation Methods
- Regression Error Metrics
Others
- Percentiles, Quartiles, Inner Quartile Range
- Different types of Plots for Continuous, Categorical variable
- Box Plot, Outliers
- Confidence Intervals
- Central Limit Theorem
- Degree of freedom
Bias and Variance in ML
Entropy in ML
Information Gain
Surprise in ML
Loss Function & Cost Function
- Mean Squared Error, Mean Absolute Error – Loss Function
- Huber Loss Function
- Cross Entropy Loss Function
Inferential Statistics
- Hypothesis Testing: One tail, two tail and p-value
- Formulation of Null & Alternate Hypothesis
- Type-I error & Type-II error
- Statistical Tests
- Sample Test
- ANOVA Test
- Chi-square Test
- Z-Test & T-Test

Introduction
- DBMS vs RDBMS
- Intro to SQL
- SQL vs NoSQL
- MySQL Installation
Keys
- Primary Key
- Foreign Key
Constraints
- Unique
- Not NULL
- Check
- Default
- Auto Increment
CRUD Operations
- Create
- Retrieve
- Update
- Delete
SQL Languages
- Data Definition Language (DDL)
- Data Query Language
- Data Manipulation Language (DML)
- Data Control Language
- Transaction Control Language
SQL Commands
- Create
- Insert
- Alter, Modify, Rename, Update
- Delete, Truncate, Drop
- Grant, Revoke
- Commit, Rollback
- Select
SQL Clause
- Where
- Distinct
- OrderBy
- GroupBy
- Having
- Limit
Operators
- Comparison Operators
- Logical Operators
- Membership Operators
- Identity Operators
Wild Cards
Aggregate Functions
SQL Joins
- Inner Join & Outer Join
- Left Join & Right Join
- Self & Cross Join
- Natural Join

EDA
- Univariate Analysis
- Bivariate Analysis
- Multivariate Analysis
Data Visualisation
- Various Plots on different datatypes
- Plots for Continuous Variables
- Plots for Discrete Variables
- Plots for Time Series Variables
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
- Imabalanced 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
- Introduce to SKLEARN FRAMEWORK
Data Processing
- Creating training and test sets, Data scaling and Normalisation
- Feature Engineering – Adding new features as per requirement, Modifying the data
- Data Cleaning – Treating the missing values, Outliers
- Data Wrangling – Encoding, Feature Transformations, Feature Scaling
- Feature Selection – Filter Methods, Wrapper Methods, Embedded Methods
- Dimension Reduction – Principal Component Analysis (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 forms straight line & 3 features forms plane
- Hyperplane and Support vectors
- Controlled support vector machines
- Support vector Regression
- Kernel SVM (Non-Linear SVM)
- Naives Bayes
- Decision Trees
- Random Forest / Bagging
- Ada Boost
- Gradient Boost
- XG Boost
- 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
- Recommendation Engines
Time Series & Forecasting
- What is Time series data
- Different components of time series data
- Stationary of time series data
- ACF, PACF
- Time Series Models
- AR
- ARMA
- ARIMA
- SARIMAX
Model Selection & Evaluation
Over Fitting & Under Fitting
- Biance-Variance Tradeoff
- Hyper Parameter Tuning
- Joblib And Pickling
Others
- Dummy Variable, Onehotencoding
- gridsearchcv vs randomizedsearchcv
ML Pipeline
ML Model Deployment in Flask

Introduction
- Power BI for Data scientist
- Types of reports
- Data source types
- Installation
Basic Report Design
- Data sources and Visual types
- Canvas and fields
- Table and Tree map
- Format button and Data Labels
- Legend,Category and Grid
- CSV and PDF Exports
Visual Sync, Grouping
- Slicer visual
- Orientation, selection process
- Slicer: Number, Text, slicer list
- Bin count,Binning
Hierarchies, Filters
- Creating Hierarchies
- Drill Down options
- Expand and show
- Visual filter,Page filter,Report filter
- Drill Thru Reports
Power Query
- Power Query transformation
- Table and Column Transformations
- Text and time transformations
- Power query functions
- Merge and append transformations
DAX Functions
- DAX Architecture,Entity Sets
- DAX Data types,Syntax Rules
- DAX measures and calculations
- Creating measures
- Creating Columns

Deep learning at Glance
- Introduction to Neural Network
- Biological and Artificial Neuron
- Introduction to perceptron
- Perceptron and its learning rule and drawbacks
- Multilayer Perceptron, loss function
- Neural Network Activation function
Training MLP: Backpropagation
Cost Function
Gradient Descent Backpropagation - Vanishing and Exploding Gradient Problem
Introduce to Py-torch
Regularization
Optmizers
Hyperparameters and tuning of the same
TENSORFLOW FRAMEWORK
- Introduction to TensorFlow
- TensorFlow Basic Syntax
- TensorFlow Graphs
- Variables and Placeholders
- TensorFlow Playground
ANN (Artificial Neural Network)
- ANN Architecture
- Forward & Backward Propagation, Epoch
- Introduction to TensorFlow, Keras
- Vanishing Gradient Descend
- Fine-tuning neural network hyperparameter
- Number of hidden layers, Number of neurons per hidden layer
- Activation function
- INSTALLATION OF YOLO V8, KERAS, THEANO
PY-TORCH Library
RNN (Recurrent Neural Network)
- Introduction to RNN
- Back Propagation through time
- Input and output sequences
- RNN vs ANN
- LSTM (Long Short-Term Memory)
- Different types of RNN: LSTM, GRU
- Biirectional RNN
- Sequential-to-sequential architecture (Encoder Decoder)
- BERT Transformers
- Text generation and classification using Deep Learning
- Generative-AI (Chat-GPT)
Basics of Image Processing
- Histogram of images
- Basic filters applied on the images
Convolutional Neural Networks (CNN)
- ImageNet Dataset
- Project: Image Classification
- Different types of CNN architectures
- Recurrent Neural Network (RNN)
- Using pre-trained model: Transfer Learning

Natural Language Processing (NLP)
- Text Cleaning
- Texts, Tokens
- Basic text classification based on Bag of Words
Document Vectorization
- Bag of Words
- TF-IDF Vectorizer
- n-gram: Unigram, Bigram
- Word vectorizer basics, One Hot Encoding
- Count Vectorizer
- Word cloud and gensim
- Word2Vec and Glove
- Text classification using Word2Vec and Glove
- Parts of Speech Tagging (PoS Tagging or POST)
- Topic Modelling using LDA
- Sentiment Analysis
Twitter Sentiment Analysis Using Textblob
- TextBlob
- Installing textblob library
- Simple TextBlob Sentiment Analysis Example
- Using NLTK’s Twitter Corpus
Spacy Library
- 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, Part of Speech Tagging
- Pos by using spacy and nltk
- Dependency Parsing
- Named Entity Recognition(NER)
- NER with NLTK
- NER with spacy

Human vision vs Computer vision
- CNN Architecture
- Convolution – Max Pooling – Flatten Layer – Fully Connected Layer
- CNN Architecture
- Striding and padding
- Max pooling
- Data Augmentation
- Introduction to OpenCV & YoloV3 Algorithm
Image Processing with OpenCV
- Image basics with OpenCV
- Opening Image Files with OpenCV
- Drawing on Images, Image files with OpenCV
- Face Detection with OpenCV
Video Processing with OpenCV
- Introduction to Video Basics, Object Detection
- Object Detection with OpenCV
Reinforcement Learning
- Introduction to Reinforcement Learning
- Architecture of Reinforcement Learning
- Reinforcement Learning with Open AI
- Policy Gradient Theory
Open AI
- Introduction to Open AI
- Generative AI
- Chat Gpt (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
MakerSuite Google
- PaLM API
- MUM models
Azure ML

Who can learn this course

Beginners who want to start a career in Full Stack Data Science with Generative AI.
Software Developers & Engineers looking to transition into AI.
Data Analysts & BI Professionals aiming to upgrade their AI skills.
Students & Graduates eager to explore AI-powered data science careers.
IT Professionals & AI Enthusiasts who want to build real-world AI applications.

Enroll Now!

In conclusion, our Full Stack Data Science with Generative AI Training in KPHB, Hyderabad is the perfect program to launch your AI career. Not only will you gain theoretical knowledge, but you will also apply your skills through hands-on projects.

So why wait? Join today and start your journey in AI and Data Science with expert guidance!

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FAQ's

Full Stack Data Science with Generative AI refers to the end-to-end process of collecting, analyzing, and visualizing data, along with using AI models to generate insights, automate tasks, and create new data-driven solutions. It includes:

Data Engineering – Data collection, cleaning, and processing.
Machine Learning & AI – Building predictive models and generative AI solutions.
Data Visualization & BI – Communicating insights using dashboards and reports.
Model Deployment – Deploying AI models in production using cloud and web technologies.

It is important because:

Generative AI enhances traditional data science – AI models can generate text, images, and code.
Businesses need AI-driven insights – Helps in decision-making and automation.
End-to-end skills are in demand – Companies prefer professionals who can handle data pipelines, AI modeling, and deployment.
Cloud-based AI solutions are growing – Knowledge of AWS, Azure, and Google Cloud is crucial.

Full Stack Data Science with Generative AI prepares you to build, deploy, and manage AI-driven data solutions.

A Full Stack Data Science professional works with:

Data Engineering & Processing:

Python, SQL – For data manipulation and querying.
Pandas, NumPy – For data analysis and numerical computing.
Apache Spark, Hadoop – For big data processing.

Machine Learning & AI:

Scikit-Learn, TensorFlow, PyTorch – For building AI models.
NLP Libraries – OpenAI’s GPT, Hugging Face, and Transformers for text-based AI.
Stable Diffusion, DALL·E – For image generation.

Data Visualization & Business Intelligence:

Power BI, Tableau, Matplotlib, Seaborn – For creating dashboards and reports.

Model Deployment & Cloud Technologies:

Flask, FastAPI, Streamlit – For building and deploying AI applications.
AWS, Google Cloud, Azure – For cloud-based AI solutions.
Docker, Kubernetes – For scalable AI model deployment.

These technologies help in building AI-powered data applications.

Full Stack Data Science with Generative AI is transforming industries:

Healthcare – AI-powered diagnosis, medical image analysis, and patient predictions.
Finance & Banking – Fraud detection, algorithmic trading, and risk analysis.
Retail & E-commerce – Customer recommendation engines and automated chatbots.
Marketing & Advertising – AI-generated content, personalized recommendations.
Cybersecurity – AI-powered threat detection and anomaly analysis.
Software Development – AI-generated code and automation.

Generative AI is enhancing automation, decision-making, and customer engagement across industries.

Learning this field provides several advantages:

High-Paying Jobs – AI and data science professionals are among the top earners.
Hands-on AI Application Development – Learn how to build and deploy AI-driven applications.
Career Growth in AI & Cloud Computing – Helps in landing jobs in AI, ML, and data engineering.
Versatility – Work in healthcare, finance, cybersecurity, e-commerce, and automation.
Future-Proof Skills – Generative AI is shaping the future of software development and business intelligence.

With these skills, you can build AI-powered applications and enterprise data solutions.

To begin, you should have:

Basic Python Knowledge – Understanding of syntax, loops, and data structures.
SQL & Database Concepts – Knowledge of MySQL, PostgreSQL, or MongoDB.
Understanding of Machine Learning – Basics of supervised and unsupervised learning.
Cloud Computing Basics (Optional) – AWS, GCP, or Azure for deploying AI models.
Familiarity with Data Visualization – Using Power BI, Tableau, or Matplotlib.

If you’re a beginner, start with Python and machine learning, then progress to Generative AI and cloud deployment.

After mastering this field, you can apply for:

Data Scientist – Analyzes and processes data using AI and ML models.
Machine Learning Engineer – Develops and deploys AI models for automation.
AI Engineer – Specializes in generative AI models for text, image, and video generation.
Big Data Engineer – Works with large datasets and cloud-based solutions.
BI Developer – Builds data dashboards and business intelligence reports.
AI Product Manager – Manages AI-driven software solutions for businesses.
Cloud AI Engineer – Deploys AI models on cloud platforms like AWS, Azure, and GCP.

Generative AI skills are in high demand across IT, finance, healthcare, and software industries.

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Full Stack Data Science with Generative AI

Python || Statistical Modeling || Machine Learning || Deep Learning || NLP || Computer Vision || SQL || Power BI || MLOps

Course Overview

Course Details:

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Language:

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