Job focused program with guaranteed referrals

Full Stack AI and ML Program With Live Industry Experience

Learn In-depth and work on industry collaborated projects to get relevant experience. Crack interview in top MNCs for AI Expert.









Full stack AI & ML Program Features

Program Duration

9 months of faculty led live online classes by industry expert. 300+ hrs sessions with 15+ live industry projects.

Program Eligibility

Working professionals having 1+ year of work experience in any IT/NON-IT domain.,MCA, MBA Preferred.

Course Fees

INR. 89,000/- (+ 18% GST)
6 months NO-COST EMI on all major credit cards | *Interest free loan option

Guaranteed Job Referral

We provide career support with guaranteed job referrals along with resume preparation tips and mock interviews to our students.

Crafted for working professionals

Full Stack AI and ML Program With Live Industry Experience

Learn In-depth and work on industry collaborated projects to get relevant experience. Crack interview in top MNCs for AI Expert.











Full stack AI & ML Program Features

Program Duration

9 months of faculty led live online classes by industry expert. 300+ hrs sessions with 15+ live industry projects.

Program Eligibility

Working professionals having 1+ year of work experience in any IT/NON-IT domain.,MCA, MBA Preferred.

Project Details

Work on latest AI & ML projects from companies and get relevant project experience certificate.

Course Fees

INR. 89,000/- (+ 18% GST)
6 months NO-COST EMI on all major credit cards | *Interest free loan option

Guaranteed Job Referral

We provide career support with guaranteed job referrals along with resume preparation tips and mock interviews to our students.

Program Certification

Get Project experience certificate directly  from companies and showcase relevant experience in AI/ML domain.

Talk to An Expert Now

Know More About Your Personalised
Career Transition Steps in Full stack AI & ML program

    Submit this information and our expert counsellor will call you on shortly

    Work Directly With Companies on Live Projects

    Project Experience Certificate

    Don't get Academic Degree , Get Project experience certificate from Industry which is more helpful in career transition for professionals.

    Get Relevant Industry Experience

    Work on Collaborative projects with comapnies and get project experience certificate.

    Get Hired in Product Based MNCs

    Learn in-depth and crack interview in top tier-1 product based MNCs with confidence.

    Pay Less and You Get More

    Training with project Experience that get you hired

    You Invest


    Course Curriculum

    Milestone 0

    Learn Programming Basics

    Learn Fundamentals of Programming. This will help candidates from Non-Programming and Non-tech domain.

    Learn Math’s Fundamental

    Revise high school math’s and basic statistics which are important for learning data science and AI. Learn basics of linear algebra, probability concepts.

    Domain & Cloud Basics

    We will cover introduction to various domain like BFSI, Healthcare, Retail, Manufacturing. Detail domain training will be covered later in milestone 4. We will cover cloud introduction.

    artificial intelligence course in bangalore Curriculum
    Milestone 1

    Learn Python in Depth

    Learn Core python and data analytics modules like Numpy, Pandas and Matplotlib & Seaborn (Data Visualization).

    Learn Statistics, EDA & Storytelling

    Learn Statistics and Probability concepts in depth. Learn Exploratory Data Analytics techniques (EDA) by working on multiple case studies and projects.

    Case Studies & Projects

    In This milestone, We will work on multiple case studies on data analytics  using python. There will be 1 capstone project and 5 case studies which involves python , data analytics end to end.

    Skillslash’s collaborative and live interactive artificial intelligence training in Hyderabad
    Milestone 2

    Learn ML in depth with Projects

    Learn all machine learning algorithms – Supervised, unsupervised, clustering with live projects & case studies from each algo.

    Learn Time Series & modelling techniques

    Learn time series forecasting using Python . Learn advance modelling techniques like feature engineering and model selection & model tuning.

    Capstone & Guided Projects

    We will cover introduction to various domain like BFSI, Healthcare, Retail, Manufacturing. Detail domain training will be covered later in milestone 4. We will cover cloud introduction.

    Ai and ML course in bangalore Curriculum
    Milestone 3

    Deep learning & computer vision Advance

    Learn Deep learning algorithms and computer vision in advance with 1 capstone project and multiple case studies.

    Natural Language Processing & Auto AI

    Learn NLP and text analytics. We are going to cover advance text analytics and working on a chatbot project from scratch using python NLTK library.

    Reinforcement Learning & Model Deployment

    Learn Reinforcement learning and model deployment using AWS and GCP.

    Milestone 4

    Resume Preparation

    With Skillslash you will get help from professionals in resume preparation which will help you to Get Notice by Interviewer.

    Interview guidance and Mock test With Experts

    Interviews are your chance to sell your skills and abilities. With us, You will get several Mock tests based on important interview questions which will help you to Enhance your Interview skills.

    Referral for relevant roles

    We provide several job referrals for relevant roles in MNCs and startups.

    Resume Preparation and Inteview Guidance for artificial intelligence course in bangalore

    Our Unique Program Features And Advantage

    Domain specialization

    Domain Training from industry experts, Elective tracks for functional and industry
    specializations, Project experience serves as the perfect compliment

    Land AI and ML jobs in top MNCs

    Mentorship from experts for a smooth career transition, Outcome driven learning tracks, additional elective track on data structures for product based companies

    Project Experience Certification

    Relevant Industry experience is a crucial factor in cracking interviews, Collaboration with top AI startups, Focus on application based learning

    Student Recommendation

    Student Community

    The Skillslash Advantages

    Artificial intelligence work with startups
    1. Do a real-time project with startups under the supervision of SkillSlash.
    2. Achieve an eye catchy project portfolio with a project experience certificate
    3. Choose a project based on your domain preferences. 
    4. Work on trendy business problems and get treated, especially at the interview table.
    5. In case you are a fresher, we provide you internship opportunities.
    A girl student in front of a laptop getting artificial intelligence training online
    1. Professionals from different background need to approach data science differently.
    2. We help you curate a personalized learning track based on your professional background and career goals.
    3. With all the customizability, the entire curriculum is delivered In live instructor led sessions.
    4. The sheer breadth and depth of topics that the program covers allows us to build tailor made learning tracks for all range of professionals
    Work on Live projects in bangalore's Best artificial intelligence course
    1. This is an extension of the Project Certification which is more relevant for professionals with more than 10 yrs of experience.
    2. Learners have an option to bring their own projects which can be worked upon by fellow students in our guidance to reach to a certain goal.
    3. The purpose behind this is to help you get live project experience in a relevant space.
    4. As decision makers in teams, professionals use this as an opportunity to get a POC done or to find better solutions to the problems they are solving.

    Course Details

    Chapter 1: Introduction to Programming ( 3 hrs )
    • What is a programming language ?
    • Source code Vs bytecode Vs machine code
    • Compiler Vs Interpreter
    • C/C++, Java Vs Python
    Chapter 2: Jupyter notebook basics (1 hrs)
    • Different type of code editors in python
    • Introduction to Anaconda and jupyter notebook
    Chapter 3: Python Programming Basics (2 hrs
    • Variable Vs identifiers
    • Strings Operators Vs operand
    • Procedure oriented Vs modular programming
    Chapter 4: Statistics basics (4 hrs)
    • Introduction to statistics
    • Measures of Central Tendency
    • Measures of dispersion
    • Inferential statistics and Sampling theory
    Chapter 5: Introduction to probability (4 hrs)
    • Introduction to probability
    • permutations and combinations
    • Addition and Multiplication Rule
    • Conditional Probabilities
    Introduction to Programming
    • What is Programming Language?
    • Why do we need a Programming Language?
    • Different types of Programming Language
    • Why do we prefer to code in a High-Level Programming
    • What is Compiler? What is an Interpreter?
    • Compile Time vs Run Time
    • Compile Time Error vs Run Time Error.
    Introduction to Python
    • What is Python?
    • Why do we need Python?
    • Python is Compiled or Interpreted?
    • How a Python Program runs on our system?
    • Features of Python
    • Memory Management in Python
    • Different Implementations of Python
    Conditional and Loops
    • Conditional Statement

    Use of “if” statement


    Use of “if-else” statement


    Use of “if-elif-else” statement

    • Loop Statement

    For loop


    When to use for loop


    For-else loop


    While Loop


    When to use while loop


    While-else loop


    Infinite loop


    Break, Continue and Pass

    Python Programming Components
    • Writing your First Python Program
    • Linting Python code
    • Formatting Python code
    • Understanding
    • Few important function
    • Command Line Arguments
    • Python Operators
    Data Types in Python
      • Fundamental Data Types
          • Strings
          • Numbers
          • None Type
          • Boolean Type
    • Derived Data Structure
    • Introduction to List
    • List creation and importance of eval() while taking list as input
      from the user
    • List comprehension
    • List properties and some basic operations
    • What is a function?
    • Function as a first-class citizen
    • What is the use of function? What is the DRY Principle?
    • How to define a function.
    • Function call vs Function referencing
    • What are inputs and outputs to the function?
    • Parameters vs Arguments
    • Types of Arguments
    • Return statement
    • Recursion
    • Namespace vs Scope
    • Anonymous Function and Lambda Expression
    • Filter, map, sort and reduce function
    • Closure Concept
    • Iterator Concept
    Exception Handling
    • What is an Exception?
    • Why do we need Exception Handling?
    • Type of Errors
    • Exception Handling Keywords
    • Nested try-except block
    • Default except for block
    • Try with multiple except block
    Modules in Python
          • What is Module?
          • Introduction to Modular Programming
          • Module Search path
          • Importing Modules and different import statement
          • Types of Modules
            • Builtin Modules
            • User Defined Modules
            • Package
    File Handling
    • What is File Handling?
    • Why do we need File Handling?
    • Type of Files
    • File Operation

    Regular Expression

    • What is Regular Expression?
    • Why do we need Regular Expression?
    • Importing regex module
    • What is Raw string and why do we need it?
    • Sample regex pattern and it’s interpretation
    • Important Methods
    Numpy in Python
    • What is Numpy?
    • Why is Numpy required?
    • What is an array?
    • Why do we need an array when we have a list?
    • Array Operations
      • Creating array using numpy
      • Printing an array
      • Indexing and Slicing
      • Basic operations
      • Universal Functions
      • Arrays with Structured Data
      • Changing shape of an array
      • Array Broadcasting
      • Vectorization
      • Iterating over an array
      • Splitting an array
      • View vs Copy
      • Vector Stacking
      • Miscellaneous Functions and Methods
      • Numpy and Scipy
      • Numpy and Pandas
    Visualization of Data in Python
    • Matplotlib
      • Lines and Markers
      • Figures and Axes
      • Figures and Subplots
      • Watermark
      • Shapes
      • Polygon and Arrows
      • Beizer Curves
      • Curves
      • Annotations
      • Scales
      • Twin Axis
      • Boxplot and Violin Plots
      • Visualize Titanic Dataset with Box and Violin plots
      • Pie Charts
      • Stacked Plots
      • Color Maps
      • Autocorrelation
    Pandas in Python
    • What is Pandas?
    • Why do we need Pandas?
    • Numpy vs Pandas
    • Pandas Data Structure
    • Series
      • Creating Series
      • Indexing and Slicing operation
    • Data Frame
      • Creating Data Frame
      • Indexing and Selection in Data Frame
      • Addition and Deletion of rows and Columns
      • Iterating over DataFrame
      • Reshaping Data Frame
      • Handling Missing Data in data Frame
      • Grouping Data Frame
      • Sorting Data Frame
      • Stacking and Unstacking
      • Concatenating and Merging Data Frame
      • Pandas Time Series
      • Exporting Dataframe to CSV and Excel
      • EDA using Pandas
    Part 1 – Introduction to Probability theory and Statistical
    • Introduction to Probability Principles
    • Random Variables and Probability principles
    • Discrete Probability Distributions – Binomial , Poisson etc
    • Continuous Probability Distributions – Gaussian, Normal, etc
    • Joint and Conditional Probabilities
    • Bayes theorem and its applications
    • Central Limit Theorem and Applications
    Part 2 – Statistics and Foundation
    • Elements of Descriptive Statistics
    • Measures of Central tendency and Dispersion
    • Inferential Statistics fundamentals
    • Sampling theory and Scales of Measurement
    • Covariance and correlation
    Part 3 – Hypothesis Testing and its Applications
    • Basic Concepts – Formulation of Hypothesis , Making a decision
    • Advanced Concepts – Choice of Test – t test vs z test
    • Evaluation of Test – P value and Critical Value approach
    • Confidence Intervals , Type 1 and 2 errors
    • Chi-squared and F tests
    • Industry Applications – Two sample mean , A/B testing
    Part 4 – Exploratory Data Analysis and the Art of Storytelling
    • Ingest data
    • Data cleaning
    • Outlier detection and treatment
    • Missing value imputation
    • Impact of Data Visualization
    • Univariate Analysis
    • Bivariate Analysis and ANOVA
    • The science of Storytelling
    • Sliding like a management consultant
    Project – Exploratory analysis on Credit card data
    • Capstone Project for Business Analysis
    Part 0 – A primer on Machine Learning
    • Types of Learning – Supervised, Unsupervised and Reinforcement
    • Statistics vs Machine Learning
    • Types of Analysis – Descriptive, Predictive, and Prescriptive
    • Bias Variance Tradeoff – Overfitting vs Underfitting
    Part 1 – Regression – Linear Regression
    • Correlation vs Causation
    • Simple and Multiple linear regression
    • Linear regression with Polynomial features
    • What is linear in Linear Regression?
    • OLS Estimation and Gradient descent
    • Model Evaluation Metrics for regression problems – MAE , RMSE, MSE,
      and MAP
    Part 2- Classification – Logistic Regression
    • Introduction to Classification problems
    • Logistic Regression for Binary Problems
    • Maximum Likelihood estimation
    • Data imbalance and redressal methodology
    • Upsampling, Downsampling and SMOTE
    • one vs Rest (OVR) for multinormal classification.
    • Model Evaluation Metrics for classification-confusion matrix/
    • Misclassification error, Precision, Recall, F1 score, and AUC-ROC
    • Choosing the best error metric for a problem
    Part 3 – Clustering – K means
    • Introduction to Unsupervised Learning
    • Hierarchical and Non-Hierarchical techniques
    • K Means Algorithms – Partition based model for clustering
    • Model Evaluation metrics – Clustering
    Part 4 – KNN
    • Introduction to KNNs
    • KNNs as a classifier
    • Non Parametric algorithms and Lazy learning ideology
    • Applications in Missing value imputes and Balancing datasets
    Part 5 – Advanced Regression Models
    • Introduction to regularization
    • Ridge regression
    • Lasso regression
    Part 6 – Decision tree
    • Nonlinear models for classification
    • Intro to decision trees
    • Why are they called Greedy Algorithms
    • Information Theory – Measures of Impurity
    • Stopping criteria for trees
    • Susceptibility to overfitting and high variance
    • Prevention of overfitting with Pruning or Truncation
    Part 7- Ensemble techniques
    • introduction to Bagging as an Ensemble technique
    • Bootstrap Aggregation and out of bag error
    • Random Forests and its Application in Feature selection
    • Scent and Boosting
    • How Boosting overcomes the Bias – Variance Tradeoff
    • Gradient Boosting and Xgboost as regularized boosting
    Part 8 – Support Vector Machines
    • Introduction to Expectation–Maximization Algorithms
    • The kernel trick
    • Linear, Polynomial, and RBF kernels and their use cases
    • SVMs for regression and classification
    • Applications in Multiclass classification
    Part 9 – Bayesian Family Algorithms and Intro to Text
    • Naive Bayes for Text classification
    • Bag of words and TF-IDF algorithm
    • Multinomial and Gaussian Naive Bayes
    • Bayesian Belief networks and Path models
    Part 10 – Time-series Analysis
    • Intro to Time series and its decomposition
    • Autocorrelation and ACF/PACF plots
    • The Random Walk model and Stationarity of Time Series
    • Tests for Stationarity – ADF and Dickey-Fuller test
    • AR, MA, ARIMA, SARIMA models for univariate time series
    • A regression approach to time series forecasting
    Part 11 – How to Build and Deploy a Machine Learning pipeline
    • Loading data
    • Feature engineering techniques
    • Principal Component Analysis for Dimensionality reduction
    • Linear Discriminant Analysis
    • Feature Selection Techniques – Forward and Backward
    • elimination, RFE
    • Model Tuning and Selection
    • Deploying a Machine Learning Model
    • Serving the model via Rest API
    Part 12 – AutoML
    • Introduction to AutoML
    • Auto learn
    • TPOT models
    • Auto Keras
    Part 1 – Neural Networks
    • introduction to Neural Networks
    • Layered Neural Network
    • Activation function and their application
    • Backpropagation and Gradient descent
    Part 2 – Tensorflow
    • Introduction to TensorFlow
    • Linear and Logistic regression with Tensorflow
    Part 3 – Artificial Neural Networks (ANNs)
    • Multi-layer perceptrons and Feedforward networks
    • Activation Functions
    • Intro to Deep Learning and deep neural networks
    • Adam and Batch normalisation
    Part 4 – Deep Neural Networks
    • Designing a deep neural network
    • Optimal choice of Loss Function
    • Tools for deep learning models – Tflearn and Pytorch
    • The problem of Exploding and Vanishing gradients
    Part 5 – Convolutional Neural networks
    • Architecture and design of a Convolutional network
    • Pooling and Flattening layer
    • Basics of digital images and image augmentation
    • Deep convolutional models
    Part 6 – Recurrent Neural networks and LSTMs
    • RNN network structure
    • Bidirectional RNNs and Applications on Sequential data
    • LSTM cell structure and its variants
    • Applying RNN/LSTMs to language and character modelling
    • Advanced Time series forecasting using RNNs with LSTMs
    • LSTMs vs GRUs – Key takeaways
    Part 7 – Restricted Boltzmann Machines and Autoencoders
    • Intro to RBMs and their training
    • Application of RBMs in Collaborative filtering
    • Intro to Autoencoders
    • Autoencoders for Anomaly detection
    Capstone Project
    • Self driving cars
    • Facial recognition
    Part 1 – Language modeling and Sequence tagging
    • Intro to the NLTK library
    • N-gram Language models: Perplexity and Smoothing
    • Introduction to Hidden Markov models
    • Viterbi algorithms
    • MEMMs and CRFs for named entity recognition
    • Neural Language models
    • Application of LSTMs to predict the next word
    Part 2 – Vector space models of Semantics for Contextual
    • Distributional semantics
    • Explicit and Implicit matrix factorization
    • Word2vec and Doc2vec models
    • Introduction to topical modeling
    Part 3 – Sequence to Sequence tasks
    • Introduction to Machine translation
    • Word Alignment models and Encoder-Decoder Architecture
    • How to implement a conversational Chatbot
    Capstone Project
    • Fully functional Chatbot
    • What is RL ? – High-level overview
    • The multi-armed bandit problem and the explore-exploit dilemma
    • Markov Decision Processes (MDPs)
    • Dynamic Programming
    • Monte Carlo Control
    • Temporal Difference (TD) Learning (Q-Learning and SARSA)
    • Approximation Methods (i.e. how to plug in a deep neural
    • network or other differentiable model into your RL algorithm)
    • Mathematics for Computer Vision
    • Intro to Transfer Learning
    • R-CNN and RetinaNet models for Object detection using Tensorflow
    • FCN architecture for Image segmentation
    • IoU and Dice score for model evaluation
    • Face detection with OpenCV
    • Ethical Risk Analysis – Identification and Mitigation
    • Managing Privacy risks
    • Modeling personas with minimal private data sharing
    • Homomorphic encryption and Zero-Knowledge protocols
    • Managing Accountability risks with a Responsibility Assignment Matrix
    • Managing Transparency and Explainability risks
    Excel for Business
    Excel Fundamentals:
    • Introduction to Excel interface
    • Customizing Excel Quick Access Toolbar
    • Structure of Excel Workbook
    • Excel Menus
    • Excel Toolbars: Hiding, Displaying, and Moving Toolbars
    • Switching Between Sheets in a Workbook
    • Inserting and Deleting Worksheets
    • Renaming and Moving Worksheets
    • Protecting a Workbook
    • Hiding and Unhiding Columns, Rows and Sheets
    • Splitting and Freezing a Window
    • Inserting Page Breaks
    • Advanced Printing Options
      Opening, saving and closing Excel document
    • Common Excel Shortcut Keys
    • Quiz
    Worksheet Customization
    • Adjusting Page Margins and Orientation
    • Creating Headers, Footers, and Page Numbers
    • Adding Print Titles and Gridlines
    • Formatting Fonts & Values
    • Adjusting Row Height and Column Width
    • Changing Cell Alignment
    • Adding Borders
    • Applying Colors and Patterns
    • Using the Format Painter
    • Formatting Data as Currency Values
    • Formatting Percentages
    • Merging Cells, Rotating Text
    • Using Auto Fill
    • Moving and Copying Data in an Excel Worksheet
    • Inserting and Deleting Rows and Columns
    Images and Shapes into Excel Worksheet
    • Inserting Excel Shapes
    • Formatting Excel Shapes
    • Inserting Images
    • Working with Excel SmartArt
    Basic work on Excel
    • Entering Values in a Worksheet and Selecting a Cell Range
    • Working with Numeric Data in Excel
    • Entering Text to Create Spreadsheet Titles
    • Entering Date Values and using AutoComplete
    • Moving and Copying Cells with Drag and Drop
    • Using the Paste Special Command
    • Inserting and Deleting Cells, Rows, and Columns
    • Using Undo, Redo, and Repeat
    • Checking Your Spelling
    • Finding and Replacing Information from worksheet & workbook
    • Inserting Cell Comments
    • Working with Cell References
    • Working with the Forms Menu
    • Sorting, Subtotaling & Filtering Data
    • Copy & Paste Filtered Records
    • Using Conditional Formatting
    • Use of Data Validation
    Excel Formulas
    • Creating Basic Formulas in Excel
    • Relative V/s Absolute Cell References in Formulas
    • Understanding the Order of Operation
    • Entering and Editing Text and Formulas
    • Fixing Errors in Your Formulas
    • Formulas with Several Operators and Cell Ranges
    • Quiz
    Excel Functions
    • Introduction to excel functions
    • Working with the SUM() Function
    • Working with the MIN() and MAX() Functions
    • Working with the AVERAGE() Function
    • Working with the COUNT() Function
    • Adjacent Cells Error in Excel Calculations
    • Use of AutoSum command
    • AutoSum shortcut key
    • Using the AutoFill Command to Copy Formulas
    • Quiz
    Working with Charts / Graphs
    • Creating a column Chart
    • Working with the Excel Chart Ribbon
    • Adding and Modifying Data on an Excel Chart
    • Formatting an Excel Chart
    • Moving a Chart to another Worksheet
    • Resizing a Chart
    • Changing a Chart’s Source Data
    • Adding Titles, Gridlines, and a Data Table
    • Formatting a Data Series and Chart Axis
    • Using Fill Effects
    • Changing a Chart Type and Working with Pie Charts
    • Quiz
    Data Analysis & Pivot Tables
    • Why Pivot Tables
    • Structuring Source Data for Analysis in Excel
    • Creating a PivotTable
    • Navigating & Manipulating the Pivot Table Field List
    • Exploring Pivot Table Analyze & Design Options
    • Selecting, Clearing, Moving & Copying Pivot Tables
    • Refreshing & Updating Pivot Tables
    • Dealing with Growing Source Data
    • Formatting Data with Pivot Tables
    • Enriching data with Pivot table calculated values & fields
    • Formatting and Charting a PivotTable
    • Pivot Table Case Study
    • Quiz
    Basic Macros
    • Automating Tasks with Macros
    • Recording a Macro
    • Playing a Macro and Assigning a Macro a Shortcut Key
    SQL & MongoDB for Business
    Introduction to SQL
    • What is a Database?
    • Why SQL?
    • All about SQL
    • Difference between SQL & MongoDB
    • Different Structured Query languages
    • Why MySQL?
    • Installation of MySQL
    • DDL
    • SQL Keywords
    • DCL
    • TCL
    • Database Vs Excel Sheets
    • relational and database schema
    • Foreign and Primary Keys
    • database manipulation, management, and administration
    2. NoSQL Databases :
    • Topics – What is HBase?
    • HBase Architecture
    • HBase Components,
    • Storage Model of HBase,
    • HBase vs RDBMS
    • Introduction to Mongo DB, CRUD
    • Advantages of MongoDB over RDBMS
    • Use cases
    First Step in SQL Database
    • Creating Database
    • Dropping Database
    • Using Database
    • Introduction to Tables
    • Data types in SQL
    • Creating a table
    • Dropping table
    • Coding best practices in SQL
    SQL Fundamental Statements
    • SELECT Statement
    • COUNT
    • ORDER BY
    • IN, NOT IN
    • NULL and NOT_NULL
    • Comparison Operators (=, >, >=, <, <=)
    • MySQL Warnings (Understand and Debug)
    Refining Selection
    • LIMIT
    SQL Intermediate Statements
    • Multiple INSERT
    • GROUP BY
    • HAVING
    • UPDATE
    • DELETE
    • AS
    Aggregator Functions
    • Application of Group By
    • Count Function
    • MIN and MAX
    • Sum Function
    • Avg Function
    • Introduction to JOINs
    • INNER Join
    • OUTER Join
    • Full Join
    • Left Join
    • Right Join
    • UNION
    SQL String Functions
    • Loading Data
    • CONCAT
    Advance SQL
    • Local, Session, Global Variables
    • Timestamps and Extract
    • TO_CHAR
    • Mathematical Functions and Operators
    • Operators and their precedence
    • String Functions and Operators
    • SubQuery
    • Self-Join
    • ALTER table
    • CASE
    • CAST
    • NULLIF
    • Check Constarints
    • Views
    • Import & Export
    Basics and CRUD Operation
    • Databases, Collection & Documents
    • Shell & MongoDB drivers
    • What is JSON Data
    • Create, Read, Update, Delete
    • Working with Arrays
    • Understanding Schemas and Relations
    • What is MongoDB?
    • Charateristics, Structure and Features
    • MongoDB Ecosystem
    • Installation process
    • Connecting to MongoDB database
    • What are Object Ids in MongoDb
    • Data Formats in MongoDB
    • MongoDB Aggregation Framework
    • Aggregating Documents
    • What are MongoDB Drivers?
    • Finding, Deleting, Updating, Inserting Elements
    TABLEAU for Business
    Introduction to TABLEAU
    • What is TABLEAU?
    • Why use TABLEAU?
    • Installation of TABLEAU
    • Connecting to data source
    • Navigating Tableau
    • Creating Calculated Fields
    • Adding Colors
    • Adding Labels and Formatting
    • Exporting Your Worksheet
    • Creating dashboard pages
    • Different charts on TABLEAU (Bar graphs, Line graphs, Scatter graphs, Crosstabs, Histogram, Heatmap, Treemaps, Bullet graphs, etc.)
    • Dashboard Tricks
    • Hands on exercises
    Data Types in Tableau
    • Aggregation and Granularity
    • Preattentive Processing
    • Length and Position
    • Reference Lines
    • Parameters
    • Tooltips
    • Data Over Time – Tableau
    • Implementation
    • Advance Table Calculations
    • Creating multiple joins in Tableau
    • Relationships vs Joins
    • Calculated Fields vs Table Calculations
    • Creating Advanced Table Calculations
    • Saving a Quick Table Calculation
    • Writing your own Table Calculations
    • Adding a Second Layer Moving Average
    • Trendlines for Power-Insights
    Mapping & Analytics
    • Getting Started With Visual Analytics
    • Geospatial Data
    • Mapping Workspace
    • Map Layers
    • Custom Territories
    • Common Mapping Issues
    • Creating a Map, Working with Hierarchies
    • Coordinate points
    • Plotting Latitude and Longitude
    • Custom Geocoding
    • Polygon Maps
    • WMS and Background Image
    • Creating a Scatter Plot, Applying Filters to Multiple Worksheets
    • Analytics Pane
    • Sorting and grouping
    • Working with sets, set action
    • Filters: Ways to filter, Interactive Filters
    • Regression Models
    • Trend Lines
    • Forecasting and Clustering
    • Control Charts
    • Box Plots
    • Hands-on: deployment of Predictive model in visualization
    • Calculated Fields
    • Calculation Syntax
    • Creating Calculated Fields
    • Aggregation & Aggregation Types
    • Common Calculation Functions
    • Basic Aggregate Functions
    • String Functions
    • Logical Functions
    • Date Functions
    • Table Calculations
    • Addressing & Partitioning
    • Level of Detail (LOD) Expressions
    • Choosing an LOD Type
    • Creating Parameters
    Dashboard and Stories
    • Working in Views with Dashboards and Stories
    • Dashboard Layout
    • Dashboard Sizing
    • Tiled vs. Floating
    • Dashboard Objects
    • Formatting
    • Working with Sheets
    • Fitting Sheets
    • Legends and Quick Filters
    • Floating Objects
    • Stories
    • Sharing Dashboards
    Power BI for Business
    Introduction to Power BI
    • Why Power BI?
    • Account Types
    • Installing Power BI
    • Understanding the Power BI Desktop Workflow
    • Exploring the Interface of the Data Model
    • Understanding the Query Editor Interface
    Query Editor
    • Connecting Power BI Desktop to Source Files
    • Keeping & Removing Rows
    • Removing Empty Rows
    • Create calculate columns
    • Make first row as headers
    • Change Data type
    • Rearrange the columns
    • Remove duplicates
    • Unpivot columns and split columns
    • Working with Filters
    • Appending Queries
    • Working with Columns
    • Replacing Values
    • Splitting Columns
    • Formatting Data & Handling Formatting Errors
    • Pivoting & Unpivoting Data
    • Query Duplicates vs References
    • Append Queries
    • Merging Queries
    • DIM-Region Table
    • Understanding “Extract”
    • Basic Mathematical Operations
    • Introduction
    • Line Charts
    • Pie Chart
    • Bar Charts
    • Stacked bar Chart
    • Clustered Column Chart
    • Combo Chart
    • Treemap Chart
    • funnel Chart
    • Scatter Chart
    • Gauge Card
    • Matrix
    • Table
    • Slicers
    • KPIs
    • Maps
    • Text boxes – Shapes – Images
    Working with Power BI
    • Working with Time series
    • Understanding aggregation and granularity
    • Filters and Slicers in Power BI
    • Maps, Scatterplots and BI Reports
    • Creating a Customer Segmentation
    • Analyzing the Customer
    • Segmentation Dashboard
    • Waterfall, Map Visualization
    • Pie and Tree Map
    • Include and Exclude
    • Categories with no Data
    Data Models: Data and Relationship
    • Understanding Relationships
    • Many-to-One & One-to-One
    • Cross Filter Direction & Many-to-Many
    • M-Language vs DAX (Data Analysis Expressions)
    • Basics of DAX
    • DAX Data Types
    • DAX Operators and Syntax
    • Importing Data for DAX Learning
    • Resources for DAX Learning
    • M vs DAX
    • Understanding IF & RELATED
    • Create a Column
    • Rules to Create Measures
    • Calculated Columns vs Calculated Measures
    • Understanding CALCULATE & FILTER
    • Understanding “Data Category”
    Time Intelligence
    • Create Date Table in M
    • Create Date Table in DAX
    • Display Last Refresh Date
    • Creating your first report
    • Modelling Basics to Advance
    • Modelling and Relationship
    • Ways of creating relationship
    • Normalization – De Normalization
    • OLTP vs OLAP
    • Star Schema vs Snowflake Schema

    Enchase your skills by Working on live projects

    25+ Industry level projects included in this course which will help you a lot.

    Expert Recommendation

    Recommended By
    Industry Experts


    Enquire for Program to know more on career transition into  data science and AI domain

      Full Stack AI and Ml Certification Program

      89,000 + 18% GST

      Our AI and ML Certification Course is especially designed for working professionals  to crack interviews in product based companies and startups as AI and ML expert. This course will benefit you to master data science skills and will help you to handle interview in product based MNCs with more confidence if you are looking for job in data science domain.


      Additional information

      Select Batch

      Weekend Batch: 27th November, Saturday, Weekday Batch: 8th November, Monday

      Frequently Asked Questions

      What are the eligibility requirements for joining Full-stack AI and ML courses?

      We believe every passionate data science aspirant is eligible for a successful data science career switch. No, we have not created any strong eligibility criteria. But keeping the learning module of the data science domain in mind,  we check for candidates eligibility on 

      • Statistical knowledge
      • Programming skills

      But this eligibility checking is only to provide the appropriate degree of guidance. If a candidate belongs to a non-statistical and non-programming background, we offer them additional support with module 0. In module 0, we teach them from the very basic level. 

      Other than that, a candidate needs to meet the following criteria.

      • 1+ years of professional experience either in IT or non IT domain.
      • Bachelor degree in technology or engineering in any discipline or a Masters degree in Computer Applications, Technology or Business Administration. 

      What modes of learning do Skillslash offer?

      Usually, we offer both offline and online modes of learning. Apart from that, we have provided a hybrid learning mode mainly for working professionals. Under hybrid learning mode, you can attain all the theoretical sessions via our live online session, but you will be working directly on the industrial project site for practical projects. 

      However, due to the pandemic situation, we are only providing full online classes but through live sessions. 

      That means you can instantly interact with your instructor while learning, just like offline, face-to-face classes. 

      As of now, all of the practical sessions will also be carried out through cloud-based services.

      What is the duration of the course, and what modules of data science does it cover?

      Depending on your chosen schedule (weekdays/ weekends), the learning duration may vary between 8 to 10 months, including the three real-time industrial projects.

      The course module is designed based on extensive industrial market demand research. These course modules include basic to advanced level learning approaches for the following.

      • Applied mathematics
      • Statistical concepts 
      • Python, C/C++, Java, and R programming
      • Data visualisation using Matplotlib and Pandas
      • ML modelling and algorithms
      • Basics of AutoML
      • Hypothesis testing with ANOVA
      • Tricks and Tips of successful data Storytelling
      • Tensorflow based deep learning
      • NLP, Artificial Neural Networks
      • Computer vision ,etc.

      Apart from this, our courses cover training on all of the top demanding AI tools such as,

      • Power BI
      • Tableau
      • AWS
      • Advance Excel
      • MongoDB, etc.

      To know more about the courses, download the course brochure of the Full-Stack AI and ML Course. 

      Are the instructor’s academic scholars (data science) or working professionals?

      We aimed to make all of our students appreciably competent in the current job market. So, we recruit only the candidates as instructors who are working professionals and hold at least six years of working experience on the targeted learning modules.

      How much does the course cost?

      At present, we are going to start our next batch for the Full-stack AI and ML program for a reasonable course fee of 89,000 INR (excluding taxes). 

      Are there any discounts available on course fees?

      We have a data science scholarship programme. If you proved eligible for this programme, then you can avail of up to 30% discount on course fees.

      Besides, we provide special scholarships to Covid affected candidates/ candidates who lost their job/ moms wanting to get back to the workforce after a career break. 

      What is the Skillslash Data Science Scholarship program?

      Every candidate will get a chance to attain a 20 mins online aptitude test. If a candidate cracks the aptitude test and scores beyond 65%, then he/she will get a chance to avail of a 30% scholarship on the course fees. 

      Candidates who lost a job due to the COVID scenario and mothers planning to restart their career can achieve up to 100% scholarship based on the test score.

      Do I need to pay the fees at a time during registration?

      We offer two alternative options for paying the fees.

      • One-time payment during the final registration
      • The monthly payment on an instalment basis for the entire course duration. 
      • Other than these, you can avail instant EMI option and education loan on popular credit cards too.

      What is a ‘job guarantee or money back’ program?

      This add-on feature is available on our Full-Stack AI and ML course for working professionals. Our mission is to provide end-to-end job assistance support to all eligible and promising candidates. 

      To avail of this feature, you need to meet the minimum score criteria. After meeting such criteria, if you remain unable to secure your targeted role in any startup or  MNC within nine months of your course completion, we’ll refund the entire course fees. 

      What are the features associated with the placement assistance program?

      Our placement assistance program includes

      • High scored resume building assistance 
      • Mock interview session to make you prepare for your target job role interview.
      • Assured interview call from product-based MNCs and startups through referrals.

      Is the placement programme similar to a university/college campusing?

      At present, we don’t offer any campusing benefits like colleges. What we offer is complete placement guidance. That means we prepare you for a specific job role through real-time industrial project experience and mock interview test. Once you become data science job market competent, we start referring you to tire one product-based MNC and startups.

      Can I get a compensatory class if I extreme situation I miss one?

      Although we provide live online classes, each of our students can access the recorded versions of the same at any time. Furthermore, we give lifetime access to these recorded sessions so that you can stream whenever you feel theoretical guidance throughout your data science career. So, it to worry if you miss any of the live class.

      However, we strictly suggest you try not to miss any live classes. 

      What type of certification does the course award?

      The course does not award you any so-called certification or academic degree. Rather what we provide is a highly creditable project experience certificate. Directly issued by the company you did your industrial project. 

      What is the ‘domain specialization tracking’ feature in the Industrial project experience program?

      Our industry experts faculty will help you target the right job role according to your domain knowledge, depending on which you become able to choose the right industrial project that will utilize your prior working experience.  

      How many projects are includes in this programme?

      The course is associated with 15+ live projects from which you can choose a few for a case study based learning depending on your domain specification.  

      Apart from that, you will get a chance to do a total of 3 industry projects (capstone project)  with different MNCs and startups on the following modules.

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