Full stack Data Science and AI: Beginner Python to Gen AI, Prompt engineering & Agentic AI -Demo

“Full Stack Data Science and AI: Beginner Python to Gen AI, Prompt engineering & Agentic AI”-Live Training.

Our Full Stack Data Science & AI Course is a comprehensive, industry-oriented training program designed to take you from Python basics to advanced Data Science, Machine Learning, Generative AI, and Prompt Engineering.

This course is ideal for students, working professionals, and career switchers who want to build a strong foundation in Data Science and Artificial Intelligence using Python. You will gain hands-on experience with real-world datasets, practical projects, and modern AI tools used in the industry.

The curriculum starts with Python programming for Data Science, covering essential libraries such as NumPy, Pandas, and Matplotlib. You will then move on to statistics, data analysis, and data visualization, followed by Machine Learning algorithms, model building, and evaluation techniques.

As part of this Full Stack Data Science training, you will also learn Deep Learning concepts, working with neural networks, and understanding AI model workflows. A key highlight of this program is Generative AI training, where you will explore large language models, AI automation concepts, and Prompt Engineering techniques to interact effectively with modern AI systems.

This Data Science & AI course with Python focuses heavily on hands-on learning, real-time use cases, and practical assignments. By the end of the course, you will be confident in applying Data Science, Machine Learning, and Generative AI skills to solve real business problems.

Upon successful completion, you will receive a course completion certificate and guidance on career opportunities in Data Science and AI, including resume building and interview preparation.

What will I Learn by end of this course?:

By enrolling in this Full Stack Data Science & AI course, you will gain end-to-end knowledge and hands-on skills across Python, Data Science, Machine Learning, Deep Learning, NLP, and Generative AI.

🔹 Python Programming for Data Science

Learn Python from basics to advanced, including variables, loops, functions, data structures, and libraries
Work with Lists, Dictionaries, Tuples, Sets, and Strings using real-world examples
Write efficient and optimized Python code for Data Science applications

🔹 Data Analysis & Visualization

Master NumPy and Pandas for data manipulation and analysis
Perform data cleaning, handling missing values, filtering, grouping, and transformations
Create insightful visualizations using Matplotlib, Seaborn, and Plotly

🔹 Exploratory Data Analysis (EDA) & Feature Engineering

Perform univariate, bivariate, and multivariate analysis
Apply statistical techniques to understand data distributions and relationships
Build strong feature engineering pipelines, encoding techniques, and scaling methods

🔹 Statistics & Mathematics for Data Science

Understand Linear Algebra, Calculus, Probability, and Statistics
Learn concepts like eigenvalues, derivatives, distributions, Bayes theorem, and CLT
Apply mathematical foundations directly to Machine Learning models

🔹 Machine Learning with Python

Build and evaluate Supervised and Unsupervised ML models
Work on Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, KNN, Naïve Bayes
Master Ensemble Learning models like XGBoost, CatBoost, Gradient Boosting
Learn model evaluation, hyperparameter tuning, cross-validation, SHAP, and interpretability

🔹 Deep Learning & Neural Networks

Understand Artificial Neural Networks (ANNs) from scratch
Build and train Deep Neural Networks and CNNs using TensorFlow/Keras
Learn regularization, optimization algorithms, and activation functions

🔹 Natural Language Processing (NLP)

Perform text preprocessing, tokenization, embeddings, and sentiment analysis
Work with RNN, LSTM, Autoencoders, and GAN basics
Implement NLP use cases using Python and deep learning models

🔹 Generative AI & Prompt Engineering

Understand Transformer architecture and attention mechanisms
Work with Large Language Models (LLMs) like LLaMA, Mistral, Gemma
Learn Prompt Engineering techniques (Zero-shot, Few-shot, Chain-of-Thought)
Explore RAG, LangChain, LlamaIndex, Agentic AI systems
Build real-world Generative AI applications

About The Instructor:

Meenakshi is a passionate AI and Machine Learning educator with over 9 years of experience, including 4 years in top multinational companies and 5 years dedicated to teaching and mentoring aspiring data professionals. With a strong blend of industry expertise and academic insight, she brings real-world relevance to every concept she teaches – making complex AI topics clear, engaging, and practical.

Her mission is to empower students with the confidence and skills to build successful careers in the fast-growing field of Artificial Intelligence. Known for her approachable teaching style and results-driven guidance, Meenakshi creates an inspiring learning environment where students don’t just learn – they grow, innovate, and thrive.

Sample Videos:

“AI & ML Unlocked: Beginner Python to Machine Learning & Deep Learning” Demo Video

Live Sessions Price:

🔥 Exclusive One-Time Offer – Offer price after discount is ~~350 ~~USD 250 USD~~~~ 229 USD Or ~~35000 INR~~ ~~25000 INR~~ 19900 Rupees.

Enroll For Free Demo

Free Demo Session:

30th March @ 8:00 PM – 9:00 PM (IST) (Indian Timings)

30th March @ 10:30 AM – 11:30 AM (EST) (U.S Timings)

30th March @ 3:30 PM – 4:30 PM (BST) (UK Timings)

Class Schedule:

For Participants in India: Monday to Friday @8:00 PM – 9:00 PM (IST)

For Participants in the US: Monday to Friday @ 10:30 AM – 11:30 AM (EST)

For Participants in the UK: Monday to Friday @ 3:30 PM – 4:30 PM (BST)

What student’s have to say about the Trainer:

The trainer demonstrated excellent subject knowledge and delivered the sessions in a very clear and engaging way. The explanations were easy to understand and encouraged participation throughout the class. – Samir Arora

Amazing course! It covers everything from basics to advanced topics. Highly recommended! – John T

Great value for money! The practical applications and projects were very helpful. – Anurag

Perfect for beginners! The explanations are clear, and the content is comprehensive – Kowsalya

I loved the hands-on approach. It made learning AI and ML much easier and more fun! – Sagar

This course boosted my understanding of AI and ML significantly. The special offer was a steal – Vasavi

Incredible course with detailed content. The price for this quality is unbeatable. – Moksitha

Salient Features:

80+ Hours of Live Training along with recorded videos
Lifetime access to the recorded videos
Course Completion Certificate

Who can enroll in this course?

This Full Stack Data Science & AI training program is suitable for:

Freshers & Students looking to start a career in Data Science and Artificial Intelligence
Software Engineers & IT Professionals aiming to upskill in Python, Machine Learning, and Generative AI
Data Analysts who want to move into Machine Learning and AI roles
Working Professionals seeking career growth in AI, Data Science, and Generative AI
Career Switchers from non-technical backgrounds with an interest in Python and AI
Researchers & Tech Enthusiasts interested in Deep Learning, NLP, and LLMs

👉 No prior Data Science or AI experience is required.
Basic programming knowledge is helpful but Python is taught from scratch.

Course syllabus:

Module 1: Introduction to AI

Definition and Scope of AI
Introduction to programming
Starting with Google Collab

Module 2: Programming with Python – Basics

Why Python?
Data Types and Type Conversion
Variables
Operators
Conditional statements
While Loops
- Infinite While Loops
- Nested While Loops
- Break keyword
For Loops
Python Identifiers
Function
- Inbuilt vs User Defined
- Types of Function Arguments
- Global variable vs Local variable
- Anonymous Function | LAMBDA
- libraries

Module 3: Python-Data Structures and Functions

3.1 List

List creation to store multiple data-type values
Retrieving an item from a list
Item Replacement
List comprehension
List mutable concept
Create nested list
Functions:
- len(), append(), pop(), insert(), remove(), sort(), reverse()
Indexing
- Forward indexing
- Backward indexing
Slicing
- Forward slicing
- Backward slicing
- Step slicing

3.2 Dictionary

Create a dictionary
Creating a CSV file from python dictionary
Keys: Values concept
Important points about python dictionary
Retrieving Keys,Values
Retrieving Key-Value pair together
Update Dictionary
Checking whether an item exists in dictionary
Functions
- len(), keys(), values(), items(), get(), pop(), update(),from_dict(),zip(),clear(),map()

3.3 Tuple

Tuple creation
Finding length of Tuple
Slice a Tuple
Retrieve items using Tuple indexing
Tuple immutable concept
Concatenate Tuples
Unpacking
Functions
- len(), count(), index(),sorted(),zip()
Indexing
- Forward indexing
- Backward indexing

3.4 Set

Set creation
Functions
- len(), add(), remove(), pop(), union(), intersection(), difference()

3.5 String Operations

Common data structure operations on a string
Create a multiline string
Creating Patterns in strings
Zero padding
F-strings
Replace a part of a string with another string item
Mathematical operations inside place holders
Performance of f-literal vs format()
Create a well-punctuated string using the escape character
Functions: strip(),split(),join(),capitalize(),lower(),title(),upper(),istitle(),replace()

Module 4: Numpy

Importance of Numpy in Data Science
Creating Numpy Arrays
- From Lists and Tuples
- Using arange(), linspace(), and logspace()
- Using zeros(), ones(),full()
- From Random Numbers: random(),rand(),randn(),randint()
Array Attributes: shape, size, ndim, dtype, itemsize
Basics Array Manipulation, Mathematical Operations, Indexing & Slicing
Functions:
- add(),subtract(),multiply(), divide(),type(),arange(),linspace(),log(),abs(),reshape(),ravel(), flatten()
Statistics using numpy array
- numpy.mean(), numpy.median(), numpy.std(), numpy.sum(), numpy.min(),corrcoef(),cov()
Comparing performance of list and array
Diagonal of a Matrix
Trace of a Matrix
Identity Matrix
Multiplicative Inverse of a Matrix
Determinant of a Matrix
Adjoint Matrix
Parsing, Adding and Subtracting Matrices

Module 5: Pandas

Introduction
Series
- Creating Panda Series, Empty Series Object, Create series from List/Array/Column from DataFrame, Index in Series, Accessing values in Series
- Statistical Operations on Series
- NaN Value
- Keywords: Values, index, dtypes, size
- Python List vs Numpy Array vs Pandas Series
- Functions: head(), tail(), sum(), count(), nunique(),sort_values(),value_counts()
DataFrame
- Introduction to DataFrames
- Creating DataFrames from Lists ,Dictionaries, Arrays
- Creating DataFrames from CSV, Excel, and Other File Formats
- Common Attributes: shape, size, dtypes, index, columns
- Basic DataFrame Methods: head(), tail(), info(), describe()
- Accessing and Modifying Data: loc, iloc
- Adding and Dropping Columns and Rows
- Renaming Columns and Index
- Identifying Missing Data
- Handling Missing Data: fillna(), dropna()
- Replacing Values
- Filtering Data
- Sorting DataFrames
- Applying Functions: apply(), map()
- Grouping Data: groupby()
- Merging and Joining DataFrames
- Concatenating DataFrames
- Setting and Resetting Index
- MultiIndex (Hierarchical Indexing)
- Mathematical and Statistical Operations
- Aggregation Methods: sum(), mean(), count(), nunique(), etc.
- Pivot Tables and Cross-tabulations
- Transpose of a Dataframe
- Inplace Parameter
- The Ampersand (&) Logical Operator

Module 6: Matplotlib

Introduction to “pyplot”
Basic Plot Functions: plot(), show(), title(), xlabel(), ylabel()
Understanding Figures and Axes
Setting Limits and Tick Labels
Creating Multiple Plots in a Single Figure using subplots
Adding Legends to Plots
Different Types of Plots
- Line plot
- Bar Charts
- Histograms
- Scatter Plot
- Pie Chart

Module 7: Seaborn and Plotly

Creating Basic Plots with Seaborn
Setting Styles and Color Palettes
Plotting with Seaborn
- Categorical Plots: barplot(),countplot(),boxplot() etc
- Distribution Plots: histplot(),pairplot() etc
- Relational Plots: scatterplot(),line plot()
- Regression Plots: regplot()
- Matrix Plots: heatmap()
Annotation
Using hue, size, and style for Multi-variable Plots
Comparison with Matplotlib

Module 8: Exploratory Data Analysis(EDA)

8.1 Introduction to EDA

Definition and Importance
Steps in EDA
Tools (Pandas, Matplotlib, Seaborn, Plotly)

8.2 Data Collection and Preparation

Loading Data (CSV, Excel, Databases)
Data Structure Understanding
Handling Missing Values
Data Cleaning (Duplicates, Outliers, Standardization)

8.3 Data Profiling

Summary Statistics (Mean, Median, Mode, Std Dev, Variance)
Data Distribution
Data Types and Conversion

8.4 Univariate Analysis

Frequency Distribution
Visualizations (Histograms, Bar Plots, Box Plots, Violin Plots, KDE Plots)
Central Tendency and Dispersion (Range, IQR, Std Dev, Variance)

8.5 Bivariate Analysis

Relationship Types (Numerical vs Numerical, Numerical vs Categorical, Categorical vs Categorical)
Visualizations (Scatter Plots, Pair Plots, Heatmaps, Box Plots)
Correlation (Pearson, Spearman, Kendall Tau)
Covariance

8.6 Multivariate Analysis

Techniques (PCA)
Visualizations (HIPLOT,Pair Plot Matrix, Heatmaps, 3D Scatter Plots)

8.7 Feature Engineering

Creating New Features
Encoding Categorical Variables (One-Hot, Label, Target)
Scaling and Normalization (Standardization, Min-Max Scaling, Robust Scaling)

Module 9: Mathematics

9.1 Linear Algebra

Linear Equations
Matrices
Determinant
Eigen Value and Eigenvector
Euclidean Distance & Manhattan Distance

9.2 Calculus

Derivatives (or Differentiation)
Partial Differentiation
Logarithm

Module 10: Statistics

10.1 Descriptive Statistics

Measures of Central Tendency (Mean, Median, Mode)
Measures of Variability (Range, Variance, Standard Deviation)
Quartiles, Percentiles, Interquartile Range (IQR)
Frequency Distribution Tables
Histograms and Box Plots

10.2 Probability Basics

Basic Probability Concepts
Sample Spaces, Events
Conditional Probability
Bayes’ Theorem
Random Variables and Probability Distributions

10.3 Statistical Distributions

Discrete Distributions (Binomial, Poisson)
Continuous Distributions (Normal, Exponential)
Central Limit Theorem and Sampling Distributions

Module 11: Machine Learning

11.1 Introduction to Machine Learning

Types of Machine learning: Supervised and Unsupervised
Discussion on different packages used for ML
Working on Linear Regression: Understanding the regression technique
Related concepts: Splitting the dataset into training and validation
Case study based practical application of the technique on Python

11.2 Simple Linear Regression

Understanding about linear regression
Simple linear regression
Linear Regression Assumptions.
Best Fit line
Cost Function
Loss Optimization, Least squares.
Gradient Descent Algorithm
Evaluation Metrics for Regression
1. Mean Absolute Error (MAE)
2. Mean Squared Error (MSE)
3. Root Mean Squared Error (RMSE)
4. Mean-Squared Log Error (MSLE)
5. R²
6. Adjusted R²
7. Residual (Error) Analysis
8. Homoscedasticity & Heteroscedasticity
9. Multicollinearity
10. Variance Inflation Factor Math
11. Recursive Feature Elimination (RFE)
Multiple Linear Regression

11.3 Logistic Regression

What is Logistic Regression?
Types of Logistic Regression.
Why not Linear Regression for Classification?
The Logistic Model. (Sigmoid curve and activation function)
Interpretation of the co-efficients.
Decision Boundary.
Cost Function of Logistic Regression.
Gradient Descent in Logistic Regression.
Evaluating the Logistic Regression Model(Classification Metrics)
1. Confusion Matrix
2. Accuracy
3. Precision
4. Recall (Sensitivity / True Positive Rate)
5. F1-Score
6. ROC Curve and AUC
7. Classification Report (in Python)

11.4 Support Vector Machine

Support Vector Machine
Introduction to Support Vector Machine
Mathematical Approach
Theory on hyperplane and kernels
Kernel function
Different kinds of kernels
Practical application on Python

11.5 Decision Tree Model

Decision Tree
Introduction to Decision tree
Types of Decision Tree Models
1. Classification
2. Regression
Significance of using Decision Tree
Decision Tree Training Algorithm
1. Entropy
2. Information Gain.
3. Gini Index.
4. Feature Selection & Node Splitting Technique.
Practical application of Decision Tree on Python
Decision Tree Feature Importance & Model evaluation.
Limitations of Decision Tree

11.6 Ensemble Learning: Random Forest Model

Bagging (Bootstrap Aggregation)
Types of Random Forest Models
1. Classification
2. Regression
Advantages of Random Forest Model over decision trees
Python Implementation.
1. Feature Importance, Model evaluation.

11.7 Ensemble Learning : Gradient Boosting Machine

Boosting
1. Bagging vs Boosting
Types of GBM Models
1. Classification
2. Regression
Python Implementation.
1. Feature Importance, Model evaluation etc.

11.8 Ensemble Learning: XGBoost

Introduction to XGBoost Model – a highly efficient gradient boosting framework.
Types of Xgboost Models
1. Classification
2. Regression
Python Implementation.
1. Feature Importance, Model evaluation etc.
Advantages of XGBoost Model.

11.9 Ensemble Learning : CATBoost

Introduction to CATBoost Model –
Types of CATBoost Models
1. Classification
2. Regression
Python Implementation.
1. Feature Importance, Model evaluation etc.

11.10 Machine learning Model Evaluation & Fine Tuning

1. Model Evaluation: ROC Curve & AUC
  1. Benchmarking.
2. K Fold Cross Validation
  1. Stratified K Fold Cross Validation
3. Hyperparameter Tuning
  1. GridsearchCV
  2. RandomizedSearchCV
  3. HyperOpt – An automated, efficient tool for hyperparameter tuning using Bayesian optimization.
  4. Model Calibration
  5. Model Interpretability & Explainability
    1. SHAP (SHapley Additive exPlanations)

11.11 Probabilistic Learning: Naïve Bayes Model

Naïve Bayes
Theory of classification
Concept of probability: prior and posterior
Bayes Theorem
Mathematical concepts
Limitations of Naïve Bayes
Practical application on Python

11.12 K-Nearest Neighbours (Classification Algorithm)

K-Nearest Neighbours
Concept and theory
Distance functions: Euclidean, Hamming, Minkowski
Why should we use KNN?
Mathematical approach
Practical application on Python

11.13 K-Means (Clustering)

Clustering 1D and 2D Data
Elbow Method and Silhouette Score

11.14 Machine Learning Project Life Cycle

Stages of AI Project Life Cycle
1. Requirements and Scope of Work (SOW)
2. Data Collection
3. Data Cleaning & Exploratory Data Analysis
4. Feature Engineering
5. Model Selection & Training
6. Model Fine Tuning
7. Model Deployment

11.15 Feature Engineering

Different ways of doing feature engineering
1. Feature selection using Correlation
2. Feature Selection Using Variance Inflation Factor (VIF) and practical implementation

11.16 Important Elements of Machine Learning

Multiclass Classification
One-vs-All
Overfitting and Underfitting
Error Measures
Principal Component Analysis (PCA)
Statistical Learning Approaches
Regularization Techniques
1. L1 (Lasso) and L2 (Ridge) Regularization
2. Role of Regularization in Preventing Overfitting
Bias–Variance Trade-off
Feature Scaling Techniques
1. Standardization (Z-score Normalization)
2. Min-Max Normalization
3. When and Why to Scale
Handling Imbalanced Datasets

Oversampling (SMOTE) and Undersampling
Class Weights in Models
Evaluation Metrics for Imbalanced Data (Precision-Recall Curve, F1-score emphasis)

Module 12: Deep Learning & Introduction to Advanced Neural Network Architectures

12.1 Artificial Neural Networks

Introduction to ANN
1. Biological Neuron vs Artificial Neuron
2. Structural ML vs Deep Learning
Perceptron
1. Perceptron Model
2. Activation function
Neural Network Structure
1. Multi Layer Perceptron and Deep Neural Network
NN Training process/ working details
1. Data Preparation
2. Weights Initialisation
3. Feed Forward Architecture
4. Forward Propagation
  1. Activation Function
  2. Loss Calculation
5. Back Propagation Algorithm
  1. Chain Rule
6. Cost Function
7. Gradient Descent Algorithm
8. Learning Rate
Different types of Activation functions
1. Sigmoid
2. Tanh
3. ReLU and its variants
4. Softmax Activation
Other NN Training Concepts
1. Over fitting
2. Under fitting
3. Regularization Techniques
  1. Dropout Regularization
  2. L1, L2 Regularization
  3. Batch Normalization
  4. Early Stopping
4. Optimization algorithms
  1. Stochastic Gradient Descent
  2. Adam Optimizer
5. Vanishing Gradient Problem.
6. Exploding Gradient Problem.

12.2 Convolutional Neural Networks (CNNs)

Concept of CNN
1. Understanding spatial data and feature extraction
2. Comparison with fully connected networks
CNN Architecture
1. Input Layer and Image Representation
2. Convolutional Layer (Filters, Feature Maps, Kernel Operations)
3. Stride and Padding concepts
4. Non-linear Activation (ReLU)
5. Pooling Layer (Max Pooling, Average Pooling, Global Pooling)
6. Flattening Layer
7. Fully Connected (Dense) Layer
8. Output Layer and Softmax
Key CNN Concepts
1. Feature hierarchy (edge → texture → object)
2. 1D, 2D, and 3D convolution
Implementation of CNN in Python (Keras/TensorFlow)
1. Building a CNN Model
2. Training and Evaluation on image datasets (e.g., MNIST, CIFAR-10)
3. Visualizing feature maps and filters
Data Augmentation

Module 13 : Natural Language Processing

13.1 Fundamentals of NLP & Text Processing

Tokenization Techniques
Tokenization in Spacy
Text Cleaning (regex, lowercasing, punctuation removal)
Part-of-Speech (POS) Tagging
Stemming & Lemmatization
Stop Words
Text Representations
1. Introduction to Text Representations
2. Label and One hot Encoding
3. Bag of Words
4. TF-IDF
5. N-Grams Based Text representations
6. Word Embeddings :
  1. Word2Vec : Continuous Bag of Words (CBOW), Skip Gram.
  2. Advanced Embeddings : Introduction to Glove, Elmo & Transformer Based Embeddings.
Use Case : Sentiment Classification with Embedding & ANNs.

13.2 Recurrent Neural Networks (RNN)

Concept of RNN
1. Need for handling sequential / time-series data
2. Comparison with fully connected networks
RNN Architecture
1. Input, Hidden, and Output Layers
2. Hidden state and recurrence relation
3. Unfolding through time
Types of RNN
Challenges in RNN

13.3 Long Short-Term Memory (LSTM)

Concept of LSTM
Motivation: overcoming vanishing gradient in RNNs
Introduction to memory cells and gates
LSTM Architecture
1. Cell State and its role in long-term memory
2. Forget Gate
3. Input Gate
4. Output Gate
5. Updating hidden and cell states
Working of LSTM (Step-by-step)
Mathematical formulation
Intuition behind gate operations
Implementation of LSTM in Python (Keras/TensorFlow)
Comparison: RNN vs LSTM vs GRU
Use Case : Sentiment analysis/Text classification with LSTM

13.4 Autoencoders

Concept of Encoder-Decoder
Encoder-Decoder Implementation with Tensorflow
Auto Encoders.
Variational Autoencoders (VAE)
GAN (Generative Adversarial Networks) – basic introduction

Module 14 : Generative AI, Prompt Engineering & Agentic AI

Transformer Architecture : Understanding transformer architecture in depth.
1. 1. Self Attention, Multi Headed Attention.
  2. Positional encoding
  3. Transformer Implementation with Tensorflow:
    1. Machine Translation : English to German translation model implementation.

Large Language Models (LLMs) :
1. Gemma, LLAMA, Mistral, Any other OS LLMs Explorations with Hugging Face.
2. Practical Applications: Working with open-source LLMs for real-world use case
Introduction to Retrieval-Augmented Generation (RAG)
Introduction to Lang Chain, Lang Graph, llama Index,Langsmith
Prompt Engineering :
1. Principles of Prompt Design
2. Few-Shot, Zero-Shot, Chain-of-Thought Prompting
3. Advanced prompting techniques for optimal AI responses
AI Agents: Building intelligent systems and exploring future AI developments

Frequently Asked Questions (FAQs):

1. What is covered in the Full Stack Data Science & AI course?

This Full Stack Data Science & AI course covers Python programming, Data Analysis, Exploratory Data Analysis (EDA), Statistics, Machine Learning, Deep Learning, NLP, and Generative AI with Prompt Engineering. The training includes hands-on projects using real-world datasets.

2. Is this a beginner-friendly Data Science course?

Yes. This Data Science & AI course with Python is designed for beginners. Python is taught from scratch, making it suitable for freshers, students, and working professionals with no prior experience in Data Science or AI.

3. Does the course include Machine Learning and Deep Learning?

Yes. The course includes Machine Learning algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forest, XGBoost, and KNN, along with Deep Learning concepts like ANN, CNN, RNN, and LSTM using Python.

4. Will I learn Generative AI and Prompt Engineering in this course?

Absolutely. This course includes Generative AI training, covering Transformer architecture, Large Language Models (LLMs), Prompt Engineering techniques, Retrieval-Augmented Generation (RAG), LangChain, and Agentic AI concepts.

5. What tools and technologies are used in this Data Science & AI training?

You will work with Python, NumPy, Pandas, Matplotlib, Seaborn, Plotly, TensorFlow, Keras, and modern Generative AI frameworks such as Hugging Face and LangChain.

6. Is this an online or classroom Data Science course?

This Full Stack Data Science & AI training is available in online mode, with instructor-led sessions, practical demonstrations, and interactive learning.

7. Are real-time projects included in this Data Science course?

Yes. The course includes real-time projects and case studies covering Machine Learning, Deep Learning, NLP, and Generative AI applications to help learners gain practical industry experience.

8. Will I get a certificate after completing the course?

Yes. Upon successful completion, you will receive a Data Science & AI course completion certificate from Isha Training Solutions.

9. Who can enroll in this Full Stack Data Science & AI course?

Students, fresh graduates, working professionals, software engineers, data analysts, and career switchers can enroll in this Data Science and AI course with Python.

10. What career opportunities can I expect after completing this course?

After completing this Full Stack Data Science & AI training, you can pursue roles such as Data Scientist, Machine Learning Engineer, AI Engineer, NLP Engineer, and Generative AI Engineer.

11. Why should I choose Isha Training Solutions for Data Science & AI training?

Isha Training Solutions offers industry-oriented Data Science training, experienced trainers, hands-on projects, real-world use cases, and up-to-date AI and Generative AI curriculum aligned with current industry demand.

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