Training Type

Select faculty

Select Date

Dur: 4 months
Course fee : 18000 /-

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:

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

Prerequisites
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)
    • 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

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

  1. Data Scientists: Professionals looking to enhance their skills and knowledge in data science and AI to solve complex problems.
  2. AI Engineers: Individuals interested in developing AI solutions and leveraging machine learning and deep learning techniques.
  3. Software Developers: Those seeking to transition into roles involving data science, AI, and machine learning development.
  4. Analysts: Data analysts and business analysts looking to expand their skill set to include advanced analytics and predictive modeling.
  5. Students and Graduates: Those pursuing degrees in computer science, mathematics, or related fields with an interest in data science and AI.
  6. Entrepreneurs and Startup Founders: Those looking to build AI-powered products and services or launch startups in the AI space.
  7. IT Professionals: System administrators, network engineers, and IT specialists interested in incorporating AI technologies into their organizations.
  8. 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)

50% Avg
salary hike
50L Avg
Package
Upcoming Batches
Live Training Batches Timetable
Course Name Faculty Date Time Mode of Training Batch Type Meeting Link
Full Stack Data Science & AI Mr. Daniel 20 May 5:30 PM (IST) online Online Training
Full Stack Data Science & AI Mr. Daniel 20 May 5:30 PM (IST) offline KPHB
Full Stack Data Science & AI Mr. Omkar 15 May 9:00 AM (IST) online Online Training
Full Stack Data Science & AI Mr. Omkar 15 May 9:00 AM (IST) offline Classroom Training
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.

Our Trainees are Working with
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...

Top 5 Technologies to learn Register for the Course !

By Providing your contact details, you agree to our Terms of use & Privacy Policy