Data Science with Generative AI

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Course Content

Introduction to Data Science

Data Science with real-world Examples

Applications using Data Science

Applications using Machine Learning

Applications using Deep learning

Applications using computer vision

Applications using Gen AI

Python

SQL

PowerBI

Tableau

Excel

Machine Learning

Deep Learning

NLP

Gen AI

OpenCV

Computer Vision

Web Scrapping

AWS

DevOps

Deployment

Data types

varibales

operators

print

precision and fied width

Python Installation and Running

Installation and Running

Jupyter Notebook

.py file from terminal

Google Colab

String

list

tuple

sets

dictionary

Forloops

while loops

if

else

elif

break

continue

functions

lambda function

comprehensions

Error / Exception Handling

File Handling

JSON module

OS Module

Pickle Module

Datetime Module

Copy Module

oops concept

class

object

constructor

inheritance

polymorphisam

abstraction

excapsulation

what is process

what is multiprocessing

what is multithreading

start

join

kill

terminate

Matrix Algebra

Vector Matrix

multiplication Matrix

Eigen Values and Eigen vectors

Regression lines etc

Numpy

Pandas

Data Visualization Library: Matplotlib

Seaborn

Descriptive Statistics

Measure of central tendency

Measure of dispersion

outliers

covariance

correlation

testing

hypothesis testing

mean

median

mode etc..

Conditional Probability

Bayes Rule

Probability Distribution: Discrete and Continuous

Normal Distribution etc..

Types of Machine Learning Methods

Supervised Machine learning

Unsupervised Machine learning

Reinforcement Machine learning

Linear Regression

Simple Linear Regression

Multiple Linear Regression

Polynomial Linear Regression

Loss Function

MSE

MAE

RMSE

Optimizers

Gradient Descent

Regularization Parameters

L1 and L2 Regularization

Mini Project - 1 Developing Regression Model to find car cost status

Mini Project -2 finding best Regresson Line using Gradient Descent Optimizers

KNN

Logistic Regression

Naive Bayes

Decision Tree

Ensemble: Bagging Random Forest Classifier

Ensemble: Boosting Gradient Boosting

AdaBoost

XG Boosting

Support Vector Machine (SVM)

Creation of new features from existing data

Selection of relevant features for the model

Handling missing values

Handling categorical data

Normalization and scaling

Encoding categorical variables

Polynomial features

Log transformations

Date-time feature extraction

Domain-specific transformations

Dimensionality reduction techniques

Text feature extraction

Image feature extraction

Feature crossing

Feature splitting

Handling outliers

Feature discretization

Sequence feature extraction

Time-series feature extraction

Feature standardization

Feature normalization

Filter methods

Wrapper methods

Embedded methods

Univariate feature selection

Recursive feature elimination (RFE)

Feature importance from models

L1 regularization (Lasso)

L2 regularization (Ridge)

Variance thresholding

Mutual information

Chi-square test

Correlation matrix

Principal component analysis (PCA)

Feature selection with cross-validation

Sequential feature selection

Information gain

Gain ratio

SelectFromModel

SelectKBest

SelectPercentile

Clustering

Dimensionality reduction

Anomaly detection

Association analysis

Density estimation

Feature learning

Similarity measurement

Data preprocessing

Visualization of high-dimensional data

Manifold learning

Centroid initialization

Number of clusters (k)

Euclidean distance

Cluster assignment

Centroid update

Inertia (within-cluster sum of squares)

Elbow method

Silhouette score

K-means++ initialization

Convergence criteria

Scalability

Handling large datasets

Assumption of spherical clusters

Sensitivity to outliers

Agglomerative clustering

Divisive clustering

Dendrogram

Linkage criteria

single

complete

average

Distance metrics

Euclidean

Manhattan

Merging clusters

Determining the number of clusters

Cutoff threshold

Hierarchical tree structure

Visualization of clusters

Scalability

Interpretability

Dimensionality reduction

Principal components

Eigenvalues

Eigenvectors

Covariance matrix

Explained variance

Projection of data

Orthogonal transformation

Scree plot

Cumulative explained variance

Standardization of data

Interpretation of principal components

Data compression

Noise reduction

Density-based clustering

Core points

Border points

Noise points (outliers)

Epsilon (ε) parameter

MinPts parameter

Density reachability

Density connectivity

Cluster formation

Handling of noise

Ability to find arbitrarily shaped clusters

Scalability

Sensitivity to parameter selection

Computational complexity

Neurons

Synapses

Weights

Biases

Activation functions

Input layer

Hidden layers

Output layer

Feedforward networks

Backpropagation

Gradient descent

Learning rate

Loss function

Epochs

Batch size

Training data

Validation data

Test data

Overfitting

Underfitting

Regularization

Dropout

Weight initialization

Normalization

Standardization

Hyperparameters

Model evaluation

Cross-validation

Sigmoid

Tanh

ReLU (Rectified Linear Unit)

Leaky ReLU

ELU (Exponential Linear Unit)

Softmax

Swish

Hard Sigmoid

Parametric ReLU (PReLU)

Thresholded ReLU

Mean Squared Error (MSE)

Mean Absolute Error (MAE)

Binary Cross-Entropy

Categorical Cross-Entropy

Sparse Categorical Cross-Entropy

Hinge Loss

Kullback-Leibler Divergence

Huber Loss

Log-Cosh Loss

Cosine Proximity

Stochastic Gradient Descent (SGD)

Mini-batch Gradient Descent

Momentum

Nesterov Accelerated Gradient (NAG)

Adagrad

Adadelta

RMSprop

Adam

Adamax

Nadam

L1 regularization

L2 regularization

Dropout

DropConnect

Early stopping

Data augmentation

Batch normalization

Layer normalization

Accuracy

Precision

Recall

F1-score

ROC-AUC

Confusion matrix

Precision-Recall curve

Mean Average Precision (mAP)

Log loss

R-squared.

Convolutional layers

Filters (kernels)

Feature maps

Receptive field

Stride

Padding

Pooling layers

Max pooling

Average pooling

Global pooling

Flattening

Fully connected layers

Softmax activation

Cross-entropy loss

Backpropagation

Gradient descent

Batch normalization

Layer normalization

Dropout

VGG 16

VGG19

ResNet

TensorFlow

Keras

PyTorch

Image reading and writing

Image resizing

Image rotation

Image translation

Image filtering

Edge detection

Contour detection

Color space conversion

Image thresholding

Geometric transformations

Image blending

Feature detection

Object tracking

Face detection

Video processing

Viola Jones

HOG

Yolo v1

Yolo v2

Yolo v3

Yolo v4

Yolo v7

Yolo v8

Unet Segmentation

v7 Segmentation

POS Estimation

Tokenization

Lowercasing

Stop word removal

Stemming

Lemmatization

Text normalization

Removing punctuation

Removing special characters

Handling contractions

Removing numbers

Spell checking and correction

Handling negations

N-gram generation

Part-of-speech tagging

Named entity recognition (NER)

NLTK

Spacy

Recurrent Neural Networks (RNNs)

Hidden state

Sequence modeling

Backpropagation through time (BPTT)

Vanishing gradient problem

Exploding gradient problem

Long Short-Term Memory (LSTM)

Gated Recurrent Unit (GRU)

Bidirectional RNN

Sequence-to-sequence models

Encoders and Decoders

Attention all you need

Transformers

Bert

Distilled Bert

RobertA

Hugging Face

GPT Models

Diffusion Models

Stable Difusion Models

Lang chains

RAG

Zero Shot Learning

Single Short learning

Few Short Learning

Deployment on AWS

Azure

Google Cloud

Serverless computing

Managed services Like AWS SageMaker

PowerBI Introduction

PowerBI Installations

Data Explanation

Visualizations

Bar chart

Line chart

Pie chart

Area chart

Scatter plot

Bubble chart

Treemap

Funnel

Card

Geographic map

Filled map

Line chart

Area chart

Histogram

Donut chat

Model View Explanation

DAX Formulas

Power Query

Tableau Introduction

Tableau Installation

What is Data Source

What is sheet

What is DashBoard

What is Story telling

Tableau visualizations

Tableau alignments

Tableau Parameters

Tableau sets

Tableau GroupBy

Include Exclude Bar Chart

Line Chart

Pie Chart

Map

Scatter Plot

Histogram

Gantt Chart

Bubble Chart

Heat Map

Tree Map

Box Plot

Area Chart

Dual-Axis Chart

Waterfall Chart

Highlight Table

Packed Bubbles

Word Cloud

Funnel Chart

Ascending order

Descending order

Transform

Publish Data Reports

SELECT statement

FROM clause

WHERE clause

JOINs INNER JOIN

LEFT JOIN

RIGHT JOIN

FULL JOIN

GROUP BY clause

HAVING clause

ORDER BY clause

Aggregate functions (SUM AVG COUNT MIN MAX)

Subqueries (Nested queries)

UNION and UNION ALL

Views (CREATE VIEW ALTER VIEW DROP VIEW)

Indexes (CREATE INDEX DROP INDEX)

Transactions BEGIN TRANSACTION

COMMIT

ROLLBACK

Constraints PRIMARY KEY

FOREIGN KEY

UNIQUE

CHECK

Data manipulation INSERT

UPDATE

DELETE

Formulas and Functions

math formulas

LOOKUP

INDEX-MATCH

PivotTables (Creating Filtering Grouping)

Charts and Graphs

Column chart

Line chart

Pie chart etc

Conditional Formatting

Data Validation

Named Ranges

Excel Tables

Functions for Text Manipulation

LEFT

RIGHT

CONCATENATE etc

Functions for Date and Time

DATE

TODAY

MONTH etc

Data Import and Export

Instructor

Kamal

Data Science and Generative AI Trainer

4.4 Rating
10k+ Students Trained
100+ Batches Completed

Computer Vision Engineer at E&Y Experience in Neural Networks Currently working on Computer Vision Use Cases Artificial Intelligence Trainer and Mentor

Enroll for Free Demo

100% Paid Internship and Job Support

Lectures50+
Skill LevelBasic-Advanced
LanguageEnglish
QuizzesYes
CertificateYes
Pass Percentage95%
Resume BuildingYes

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