Full Stack Data Science & AI Online Training [Real-Time]

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Course fee : 21000 /-

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Full Stack Data Science & AI

Course Overview

The Online Full Stack Data Science & AI Training course offers a thorough and immersive program meticulously crafted to empower participants with the essential knowledge and competencies crucial for success in the dynamic realm of data science and artificial intelligence (AI). Developed with precision, this course spans across a spectrum of critical subjects, encompassing data collection methodologies, preprocessing techniques, advanced analysis methodologies, machine learning principles, deep learning advancements, and practical applications of AI. Delivered in an online training format, participants have the flexibility to engage with course materials from anywhere, at any time, allowing for seamless integration into their schedules and lifestyles. Through a blend of interactive modules, virtual labs, and instructor-led sessions, participants will delve into the intricacies of handling diverse real-world datasets and honing their proficiency in implementing cutting-edge AI algorithms. Whether pursuing a career shift, seeking professional advancement, or simply expanding one's expertise, this online training course provides a comprehensive platform for individuals to embark on their journey towards mastery in data science and AI.

Learn software skills with real experts, either in live classes with videos or without videos, whichever suits you best.

Description

The Full Stack Data Science & AI course begins with an introduction to data science and AI, providing an overview of their applications and importance in various industries. Participants will learn about data collection methods, data preprocessing techniques, and exploratory data analysis (EDA). The course covers topics such as statistical analysis, machine learning algorithms, deep learning models, and AI applications. Practical exercises, projects, and case studies will be used to reinforce theoretical concepts and provide hands-on experience.

Course Objectives

The primary objectives of the Full Stack Data Science & AI course are as follows:

Introduction to Data Science & AI: Provide an overview of data science and AI concepts, methodologies, and applications.
Data Collection and Preprocessing: Gain skills in collecting data from various sources and preprocessing it for analysis.
Exploratory Data Analysis (EDA): Learn how to perform EDA to understand the structure and characteristics of datasets.
Statistical Analysis: Understand basic and advanced statistical techniques for analyzing data and deriving insights.
Machine Learning: Explore supervised, unsupervised, and reinforcement learning algorithms for predictive modeling and pattern recognition.
Deep Learning: Develop an understanding of neural networks, deep learning architectures, and techniques for training and evaluating deep learning models.
AI Applications: Learn about real-world applications of AI, including natural language processing (NLP), computer vision, and recommendation systems.
Model Deployment: Explore techniques for deploying machine learning and deep learning models into production environments.
Ethical and Legal Considerations: Understand ethical and legal issues surrounding data science and AI, including privacy, bias, and fairness.
Project Development: Work on hands-on projects and case studies to apply learned concepts and techniques in real-world scenarios.

Prerequisites

- Basic understanding of Python programming.
- Familiarity with statistics and mathematics.
- Knowledge of data manipulation libraries (e.g., NumPy, Pandas).
- Understanding of data visualization techniques.
- Basic knowledge of machine learning concepts.
- Familiarity with SQL and databases.

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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 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

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

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Who can learn this course

This course is suitable for a diverse range of individuals, including:

Data Scientists: Professionals looking to enhance their skills and knowledge in data science and AI to solve complex problems.
AI Engineers: Individuals interested in developing AI solutions and leveraging machine learning and deep learning techniques.
Software Developers: Those seeking to transition into roles involving data science, AI, and machine learning development.
Analysts: Data analysts and business analysts looking to expand their skill set to include advanced analytics and predictive modeling.
Students and Graduates: Those pursuing degrees in computer science, mathematics, or related fields with an interest in data science and AI.
Entrepreneurs and Startup Founders: Those looking to build AI-powered products and services or launch startups in the AI space.
IT Professionals: System administrators, network engineers, and IT specialists interested in incorporating AI technologies into their organizations.
Technical Enthusiasts: Anyone eager to learn about data science and AI and explore their applications in various domains.

Average package of course (Full Stack Data Science & AI)

100% Avg
salary hike

5.8 - 9.3L Avg
Package

Upcoming Batches

Course Name	Faculty	Date	Time	Mode of Training	Batch Type
Full Stack Data Science & AI	Real-Time Expert	25 Nov	8:00 PM (IST)	online	Online Training
Full Stack Data Science & AI	Real-Time Expert	25 Nov	8:00 PM (IST)	offline	Classroom Training
Full Stack Data Science & AI	Mr. Mayur	22 Nov	7:30 AM (IST)	offline	Classroom Training
Full Stack Data Science & AI	Real-Time Expert	13 Nov	6:00 PM (IST)	offline	Classroom Training
Full Stack Data Science & AI	Mr. Daniel	11 Nov	5:30 PM (IST)	online	Online Training
Full Stack Data Science & AI	Mr. Daniel	11 Nov	5:30 PM (IST)	offline	KPHB

Training Features

Comprehensive Curriculum

Master web development with a full-stack curriculum covering front-end, back-end, databases, and more.

Hands-On Projects

Apply skills to real-world projects for practical experience and enhanced learning.

Expert Instructors

Learn from industry experts for insights and guidance in full-stack development.

Job Placement Assistance

Access job placement assistance for career support and employer connections.

Certification upon Completion

Receive a recognized certification validating your full-stack development skills.

24/7 Support

Access round-the-clock support for immediate assistance, ensuring a seamless learning journey.

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Reviews

Omkar Sir's exceptional data science course at Naresh IT is highly recommended for its engaging teaching style and comprehensive training sessions, preparing students for future careers in the field.

Nikita Parolekar (Nicks)