Project Based Certification Platform For Professionals

Full Stack AI and ML Program With Real Work Experience

Learn from experts in live-interactive classes. In addition, get the chance to customize your learning tracks and build relevant work experience by working with top AI companies and startups

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Intro Video

350 Hrs

Online Live classes

15+ Projects

Learn from Projects

03 Feb 2022

Next Cohort Starts

Starting 5,801/month

Easy No cost EMIs

Land in your dream job with real work experience

Key Program Features

Artificial Intelligence and Machine Learning course

Ai and Ml course Project Experience Certificate

Real Work Experience

Dont just learn, apply your learning. Work directly with companies to build relevant industry experience and demonstrate expertise in the job market

Build your own course feature of Ai and Ml programs-skillslash

Build Your Own Course

Build personalized learning tracks to cater to your professional background and career goals under the supervision of your counselor

Ai and Ml program with Guaranteed job referral

Guaranteed Job Referral

Get assured referrals for AI and ML roles in top companies and startups. Also receive guidance on resume building and interview preparation

eligibility criteria of ai and ml course-skillslash

Eligibility criteria

This program is recommended for professionals with at least 1 year of work experience. No prior programming experience is required

Land in your dream job with real work experience

Upskill your career with India's best Industry experience training provider

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Get to know in detail about our

Full Stack AI and ML Program

Basic

₹ 59,000 + GST

AI and Ml Training
  • Get foundational AI and Ml training
  • Receive expert-level job assistance
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Pro

₹ 89,000 + GST

Real Work Experience
  • Earn real-work experience
  • Advanced level AI and Ml training
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Pro Max

₹ 1,30,000 + GST

100% Job Guarantee
  • Pro-level AI and Ml training
  • Avail unlimited job referrals
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Get Real Work Experience Directly From Companies

How to Learn & Get Real Work Experience

complete required modules. for projects

Complete the required modules

Learn the skills needed for your project

Start your project with ai companies

Start your project with AI companies

Go through an internal assessment to start working

GET MENTORED- by skillslash expert

Get mentored on Projects by Skillslash experts

Work under the guidance of our mentors

Ai and Ml course Project Experience Certificate

Complete & Get certified by AI companies

Complete project deliverables and get certified

Get Certificate directly from AI companies

Work on Collaborative projects with companies and get project experience certificate to land in your dream data science job roles.

  • Gain practical experience by working on real-time projects with AI Companies.
  • Work and learn the nuances of projects from scratch to the deployment level.
  • Get hired by learning from best data science institute and crack  interviews with confidence.
Talk to our Expert

Get Certificate directly from AI companies

Full stack artificial intelligence and Machine Learning compnay certification
Full stack artificial intelligence and Machine Learning Industry certification

Learn From Best

Our Experts Are From

Learn and work

Why Real work Experience?

Learn and Work

Why Real Work Experience?

Application-based learning approach focuses on helping learners build relevant experience in the technologies they upskill to demonstrate expertise.

  • Implement what you Learn.
  • Get ready as per the industry level.
  • Learn the skills that top companies want.
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Syllabus

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Artificial intelligence and machine learning course curated by leading faculties and industry leaders to provide pratical learning experience with live interactive classes and projects.

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Program Highlights
  • 350+ Live sessions
  • 15+ Industry Projects
  • Life time accessibility
  • Live project experience
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Edit

Source code Vs bytecode Vs machine code, Compiler Vs Interpreter, C/C++, Java Vs Python.

Different type of code editors in python, Introduction to Anaconda and IDEs

Variable Vs Identifiers, Strings Operators Vs Operand, Procedure oriented Vs Modular programming.

Measures of Central Tendency & dispersion, Inferential statistics and Sampling theory.

Edit

Source code Vs bytecode Vs machine code, Compiler Vs Interpreter, C/C++, Java Vs Python.

Different type of code editors in python, Introduction to Anaconda and IDEs

Variable Vs Identifiers, Strings Operators Vs Operand, Procedure oriented Vs Modular programming.

Measures of Central Tendency & dispersion, Inferential statistics and Sampling theory.

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  • 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
  • Elements of Descriptive Statistics
  • Measures of Central tendency and Dispersion
  • Inferential Statistics fundamentals
  • Sampling theory and scales of measurement
  • Covariance and correlation
  • 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
  • Ingest data
  • Data cleaning
  • Outlier detection and treatment
  • Missing value imputation
  • Capstone project for Business Analysis
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  • Types of Learning - Supervised, Unsupervised and Reinforcement
  • Statistics vs Machine Learning
  • Types of Analysis - Descriptive, Predictive, and Prescriptive
  • Bias Variance Tradeoff - Overfitting vs Underfitting
  • 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
  • Introduction to Classification problems
  • Logistic Regression for Binary problems
  • Maximum Likelihood estimation
  • Data Imbalance and redressal methodology
  • Up sampling, Down sampling and SMOTE
  • Introduction to Unsupervised Learning
  • Hierarchical and Non-Hierarchical techniques
  • K Means Algorithms - Partition based model for clustering
  • Model Evaluation metrics – Clustering
  • Introduction to KNNs
  • KNNs as a classifier
  • Non-Parametric algorithms and Lazy learning ideology
  • Applications in Missing value imputes and Balancing datasets
  • Introduction to regularization
  • Understanding ridge regression
  • Working with Lasso regression
  • Tackling multicollinearitywith regression
  • Nonlinear models for classification
  • Intro to decision trees
  • Why are they called Greedy Algorithms?
  • Information Theory - Measures of Impurity
  • Introduction to Bagging as an Ensemble technique
  • Bootstrap Aggregation and Out of Bag error
  • Random Forests and its Applications in Feature selection
  • How Bagging overcomes the overfitting problem?
  • Scent and Boosting
  • How Boosting overcomes the Bias - Variance Tradeoff
  • Gradient Boosting and Xgboost as regularised boosting

  • Introduction to Expectation— Maximization Algorithms

  • The kernel tricks

  • Linear, Polynomia, and RBF kernels, SVMs for regression and classification

  • Applications in Multiclass classification.

  • Naive Bayes for Text classification

  • Bag of words and TF-IDF algorithm

  • Multinomial and Gaussian Naive Bayes, Bayesian Belief networks and Path models.

  • Intro to Time series
  • 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
  • A regression approach to time series forecasting.
  • Feature engineering & selection techniques
  • Principal Component Analysis
  • Linear Discriminant Analysis
  • Serving the model via Rest API & Keras.
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  • Introduction to Neural Networks
  • Layered Neural networks
  • Activation Functions and their application
  • Backpropagation and Gradient Descent

  • Introduction to TensorFlow

  • Working with TensorFlow

  • Linear regression with TensorFlow

  • Logistic regression with TensorFlow

  • 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

  • Architecture and desig n of a Convolutional network

  • Deep convolutional models & image augmentation.

  • RN N & LSTM structure, Bidirectional RNNs and Applications on Sequential data

  • Advanced Time series forecasting using RNNs with LSTMs

  • LSTMs vs GRUs.

  • Intro to RBMs, Autoencoders

  • Application of RBMs in Collaborative filtering

  • Autoencoders for Anomaly detection

  • Capstone Project -Self-driving cars, Facial recognization.

Edit
  • 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)
Edit
  • 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)
Edit
  • 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
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  • 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
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  • 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.
  • 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 Colours 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.
  • Inserting Excel Shapes.
  • Formatting Excel Shapes.
  • Inserting Images.
  • Working with Excel SmartArt.

  • Entering and selecting values. Using numeric data in excel

  •  

    Working with forms menu, cell references, conditional

  • formatting and data validation, Finding and replacing information from worksheet

  • Inserting & deleting cells, rows and columns.

  • Creating basic formulae in excel

  • Im plementing excel formulae in worksheet

  • Relative cell referencing

  • Absol ute cell referencing

  • Relative vs Absol ute cell references in formulae

  • Understanding the order of operation

  • Entering and Editing text, Fixing errors in your formulae

  • Formulae with several operators, Formulae with cell ranges

  • Quiz.

  • Working with functions like SUM(), AVERAGE() etc

  • Adjacent cells error in excel calculations

  • Use of AutoSum & autofill command

  • Quiz

  • 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.
  • Intro to Pivot Tables
  • Structuring Source Data for Analysis in Excel
  • Creating a PivotTa ble
  • Exploring Pivot Ta ble Analyse & Desig n Options
  • Working with and on pivot tables
  • Dealing with Growing Source Data
  • Enriching data with Pivot table calculated values & fields
  • Formatting and charting a PivotTable
  • Pivot Table Case Study
  • Quiz
  • Intro to Pivot Tables
  • Structuring Source Data for Analysis in Excel
  • Creating a PivotTa ble
  • Exploring Pivot Ta ble Analyse & Desig n Options
  • Working with and on pivot tables
  • Dealing with Growing Source Data
  • Enriching data with Pivot table calculated values & fields
  • Formatting and charting a PivotTable
  • Pivot Table Case Study
  • Quiz

  • Introduction to macros

  • Automating Tasks with Macros

  • Recording a Macro

  • Playing a Macro

  • Assigning a Macro a Shortcut Key.

  • 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.
  • 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.

  • Introduction to database

  • Creating Data base, Dropping Database

  • Using Database

  • Introduction to Tables

  • Data types in SQL

  • Use case of different data

  • Working with tables

  • Coding best practices in SQL

  • SELECT Statement.
  • COUNT.
  • SELECT WHERE.
  • ORDER BY.
  • IN, NOT IN.
  • NULL and NOT_NULL.
  • Comparison Operators (=, >, >=, <=).
  • MySQL Warnings (Understand and Debug).
  • SELECT DISTINCT.
  • LIKE, NOT LIKE, ILIKE.
  • LIMIT.
  • BETWEEN.
  • BETWEEN – AND
  • Multiple INSERT.
  • INSERT INTO.
  • GROUP BY.
  • HAVING.
  • WHERE vs HAVING.
  • UPDATE.
  • DELETE.
  • AS.
  • EXISTS-NOT EXISTS.
  • Aggregator functions.
  • Application of group by.
  • Count function.
  • MIN and MAX.
  • Sum Function.
  • Avg Function.

  • Introduction to JOINs

  • Types of JOINS

  • Usage of different types of JOINS

  • Loading Data

  • Usage of string functions like; CONCAT, SUBSTRING etc

  • INNER join,

    OUTER join, Full join, Left Join, Right Join, UNION.

  • Local, Session, Global Variables

  • Timestamps and Extract, CURRENT DATE & TIME, EXTRACT

  •  

    AGE, TO_CHAR, Mathematical Functions and Operators

  • CEIL & FLOOR, POWER, RANDOM,

     

  • ROUND, SETSEED, Operators and their precedence.

  • Data bases

  • Collection & Documents

  • Shell & MongoDB drivers

  • What is JSON Data

  • Create, Read, Update, Delete

  • Working with Arrays

  • Understanding Schemas and Relations.

  • What is MongoDB?
  • Characteristics, 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.
  • What is TABLEAU?
  • Why to use TABLEAU?
  • Installation of TABLEAU.
  • Connecting to data source.
  • Navigating Tableau.
  • Creating Calculated Fields.
  • Adding Colours.
  • Adding Labels and Formatting.
  • Exporting Your Worksheet.
  • Creating dashboard pages.
  • Different charts on TABLEAU (Bar graphs, Line graphs, Scatter graphs, Crosstabs, Histogram, Heatmap, Tree maps, Bullet graphs, etc.)
  • Dashboard Tricks.
  • Hands on exercises.
  • Pre-attentive processing.
  • Length and position.
  • Reference Lines.
  • Parameters.
  • Tooltips.
  • Data over time.
  • 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.
  • 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.
  • Aggregation and its types
  • level of detail common calculation functions
  • creating parameters

  • Tiled vs Floating

  • Working in views with Dashboard and stories

  • Legends, Quick filters.

     

  • 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.
  • 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

  • Power BI

  • Working with Time series Understanding aggregation and granularity

  • Filters and Slicers in Power BI

  • Maps, Scatterplots and BI Reports

  • Creating a Customer Seq mentation.

  • 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".
  • SUM, AVERAGE, MIN, MAX, SUMX, COUNT, DIVIDE, COUNT, COUNTROOMS, CALCULATE, FILTER, ALL
  • Time Intelligence.
  • Create date table in M.
  • Create date table in DAX.
  • Display last refresh date.
  • SAMEPERIODLASTYEAR.
  • TOTALYTD.
  • DATEADD.
  • PREVIOUSMONTH.

  • Create data table in M and DAX, Display last refresh Date.

  • Create your first report.
  • Modelling basics to advance.
  • Modelling and relationship.
  • Ways of creating relationship.
  • Normalisation – De-normalisation.
  • OLTP vs OLAP.
  • Star schema vs Snowflake schema.

Tools Covered

Industry - partnered capstone projects

Hands-on Projects

Data sets from the industry

15+

Projects
Available

6+

Domain
Covered

Practice with 20+ tools

Designed by Industry Experts

Get Real-world Experience

Edit

Predict credit default application

Project Objective : Develop a prediction model for existing customers to identify probable credit default for a retail bank

Predict-backrupt-of the company skillslash project

Predict bankruptcy of a company

Project Objective: Model to predict whether a company will go bankrupt or not

foreign currencey preidction skillslash project

Currency foreign exchange rates

Project Objective: Time series analysis Forecast value of a currency in global market

Mobile banking project by skillslash

Analyse customer mobile banking

Project Objective: Create clusters of customers on the usage of mobile banking

Predict credit default application

Project Objective : Develop a prediction model for existing customers to identify probable credit default for a retail bank

Predict-backrupt-of the company skillslash project
Predict bankruptcy of a company

Project Objective: Model to predict whether a company will go bankrupt or not

foreign currencey preidction skillslash project
Currency foreign exchange rates

Project Objective: Time series analysis Forecast value of a currency in global market

Mobile banking project by skillslash
Analyse customer mobile banking

Project Objective: Create clusters of customers on the usage of mobile banking

    Edit
    Uber fair prediction skillslash project

    Uber - fare prediction

    Project Objective: Predicting fare price based on demand and supply, weather and other factors

    Mercedes-reduce0-time to market project skillslash

    Reduce time to market

    Project Objective: Reduce the time for a Mercedes-Benz to reach the market by optimizing the testing

    motion prediction project by skillslash

    Motion Prediction For Vehicles

    Project Objective: Build motion prediction models for self driving vehicles

    Predictive maintenance

    Project Objective: The objective is to predict the failure of the machine in advance

    Uber fair prediction skillslash project
    Uber - fare prediction

    Project Objective: Have to analyze the data Identification of COVID-19 surge in cases based on mobility within the country

    Mercedes-reduce0-time to market project skillslash
    Reduce time to market

    Project Objective: Reduce the time for a Mercedes-Benz to reach the market by optimizing the testing

    motion prediction project by skillslash
    Motion Prediction For Vehicles

    Project Objective: Build motion prediction models for self driving vehicles

    Predictive maintenance

    Project Objective: The objective is to predict the failure of the machine in advance

      Edit
      Covid-19 data prediction project

      Google Mobility data Prediction

      Project Objective: Goal is to identify Covid-19 surge in different regions based on mobility within the country

      skillslash-project-ai-heart

      Predict heart failure

      Project Objective: Create a model that could predict heart failure before its occurrence

      skillslash-project-newtworking

      Reddit - Vaccine myths on social media

      Project Objective: Sentiment analysis of vaccine on social media

      Infected or not Infected

      Project Objective: Study the human cell to identify whether it is infected or not infected

      Covid-19 data prediction project
      Google Mobility data Prediction

      Project Objective: Goal is to identify Covid-19 surge in different regions based on mobility within the country

      skillslash-project-newtworking
      Reddit - Vaccine myths on social media

      Project Objective: Sentiment analysis of vaccine on social media

      skillslash-project-ai-heart
      Predict heart failure

      Project Objective: Create a model that could predict heart failure before its occurrence

      Infected or not Infected

      Project Objective: Study the human cell to identify whether it is infected or not infected

        Edit
        Youtube-data prediction project by skillsash

        YouTube trending video analytics

        Project Objective: Analyze daily records of YouTube trending video analytics to generate their comments

        Google Play Store Apps success factors.project by skillslash

        Google Playstore apps success factors

        Project Objective: Predict the factors that contribute to the success of an application on Google play store

        Spotify-song-preidction-project-skillslash

        Spotify – Identify the songs

        Project Objective: Work on the dataset to find a geographical connection with popular songs

        Hr-analytics-project-skillslash

        HR Analytics: To find candidates

        Project Objective: Predict the probability of a candidate looking for a new job

        Youtube-data prediction project by skillsash
        YouTube trending video analytics

        Project Objective: Analyze daily records of YouTube trending video analytics to generate their comments

        Google Play Store Apps success factors.project by skillslash
        Google Playstore apps success factors

        Project Objective: Predict the factors that contribute to the success of an application on Google play store

        Spotify-song-preidction-project-skillslash
        Spotify – Identify the songs related to

        Project Objective: Work on the dataset to find a geographical connection with popular songs

        Hr-analytics-project-skillslash
        HR Analytics: To find candidates

        Project Objective: Predict the probability of a candidate looking for a new job

          Edit
          tele-customer-skillslash-project

          Telecom customer churn prediction

          Project Objective: Predict the behavior of customers to identify the probability of churning off

          Fraud call prediction skillslash project

          Fraud call Prediction model

          Project Objective: Identification of illegal activities like fake profiles, cloning, identity theft for the customers

          telecome price optimization

          Price Optimization by analysing customers

          Project Objective: Price optimization for the telecom services by predicting the LTV, Tarrifs, understanding price elasticity w.r.t factors

          Chatbot developemnt project

          Chatbot design for best in-class support

          Project Objective: Chatbots for operational support and automated self-service

          tele-customer-skillslash-project
          Telecom customer churn prediction

          Project Objective: Predict the behavior of customers to identify the probability of churning off

          Fraud call prediction skillslash project
          Fraud call prediction model

          Project Objective: Identification of illegal activities like fake profiles, cloning, identity theft for the customers

          telecome price optimization
          Price Optimization by analysing customers

          Project Objective: Price optimization for the telecom services by predicting the LTV, Tarrifs, understanding price elasticity w.r.t factors

          Chatbot developemnt project
          Chatbot design for best in-class support

          Project Objective: Chatbots for operational support and automated self-service

            Edit
            IMDB movie rating prediction

            IMDB – predict the rating of a movie

            Project Objective: Predict the rating and success of movies

            NSE stock prediction skilllsash project

            NSE – Stock price prediction

            Project Objective : Predict the stock prices with an increased level of accuracy

            Vinbigdata project by skillslash

            VinBigData Chest X-ray

            Project Objective: Accurate classification of problems to identify and localize findings on chest radiographs

            Amazon Food Reviews

            Project Objective: Classify food reviews based on customer feedback. Here you will use NLP to identify the sentiment of customers

            IMDB movie rating prediction
            IMDB – predict the rating of a movie

            Project Objective: Predict the rating and success of movies

            NSE stock prediction skilllsash project
            NSE – Stock price prediction

            Project Objective : Predict the stock prices with an increased level of accuracy

            Vinbigdata project by skillslash
            VinBigData Chest X-ray

            Project Objective: Accurate classification of problems to identify and localize findings on chest radiographs

            Amazon Food Review

            Project Objective: Classify food reviews based on customer feedback. Here you will use NLP to identify the sentiment of customers

              Student reviews

              Why pratical learning experience is vital?

              Online courses designed for creative minds and made for everyone. Take our course. Experience the new era of education.

              Student reviews

              Why pratical learning experience is vital?

              Online courses designed for creative minds and made for everyone. Take our course. Experience the new era of education.

              Thanks a ton Skillslash Family. As a part of the Full stack course in Data Science i had a great learning experience. I was able to successfully move into a data science role in 7 months , which was amazing.

              Pragyan Prakash

              AI and ML full stack program is too good and helpful for working professionals, I have done BCA so I was well versed in Java, C, basic SQL and C++. At Skillslash I learnt Python, core SQL, R, math - stats, ML and More.

              Tilak Rao

              One of the best platform for working professionals. Although a new startup but training quality is really good. For our batch, instructor is Rahul (and co-founder) and he teaches statistics and ML concepts in depth.

              Gautam

              One of the best course providers is Skillslash, their data science course has helped me become the data scientist I am today. There are tons of differences between studying data science and working as a data scientist.

              Mrinal Sahay

              Skillslash is truly one of the best institutes to study machine learning, I thank my brother for suggesting me this course. The course has amazing perks for working professionals like live classes, faculty of industry professionals

              Sammer Ahmed

                The benefits of Skillslash

                Talk with our expert
                Edit
                Certification from top AI startups by skillslash
                1. We assist our students to work on real-time projects from AI companies as part of Full stack Artificial Intelligence and Machine Learning program.
                2. This is advanced project experience certification that adds relevant value to our students profile.
                3. Our students work on these real projects by working from data cleaning to deployment of the project.
                4. This can be used by team decision makers to complete a POC. Besides, it is also possible to gain better resources for resolving project issues.
                Edit
                Build Your own course feature by skillslash
                1. interact with our experts to construct customized learning routes based on your work goals and previous expertise.
                2. These are specialized courses available with an emphasis on industry training. 
                3. Modules can be selected based on your preferred learning style. 
                4. Choose your correct learning path to become an expert with our Full stack Artificial Intelligence and Machine Learning program.
                Edit
                Bring your own project and get expert guidance by skillslash
                1. Work on and learn with our real time projects specific to your domain that makes you an expert data science professional.
                2. This artificial intelligence and machine learning course in Bangalore is built to provide you with advanced experience with projects.
                3. Under this ai and machine learning course in Bangalore, students are allowed to bring their own projects to learn data science with their most relevant domain experience.
                4. The domain relevant project experience provides the right boost to the student’s career.
                Edit
                Certification from top AI startups by skillslash
                1. We assist our students to work on real-time projects from AI companies as part of Full stack Artificial Intelligence and Machine Learning program.
                2. This is advanced project experience certification that adds relevant value to our students profile.
                3. Our students work on these real projects by working from data cleaning to deployment of the project.
                4. This can be used by team decision makers to complete a POC. Besides, it is also possible to gain better resources for resolving project issues.
                Edit
                Build Your own course feature by skillslash
                1. Interact with our experts to construct customized learning routes based on your work goals and previous expertise.
                2. These are specialized courses available with an emphasis on industry training. 
                3. Modules can be selected based on your preferred learning style. 
                4. Choose your correct learning path to become an expert with our Full stack Artificial Intelligence and Machine Learning program.
                Edit
                Bring your own project and get expert guidance by skillslash
                1. Work on and learn with our real time projects specific to your domain that makes you an expert data science professional.
                2. This artificial intelligence and machine learning program is built to provide you with  advanced experience with projects.
                3. Students are allowed to bring their own projects to learn data science with their most relevant domain experience.
                4. The domain relevant project experience provides the right boost to the students career.

                How to apply?

                Follow these 3 simple steps in the admission process

                Step 1: Fill Enquiry From

                Apply for your profile review
                by filling the form

                Step 2: Talk to Expert

                Get your career counseling report from the expert

                Step 3: Get Started

                Join the AI and ML program by enrolling

                Fill Enquiry Form
                Apply for your profile review

                by filling the form

                Talk to Expert
                Get your career counseling report

                from the expert

                Get Started
                Join the AI and ML program

                by enrolling

                Upcoming Cohort Deadline

                The admission closes once the required number of applicants enroll for the upcoming cohort. Apply early to secure your seats and get started on your professional AI and ML training.

                3rd February 2022

                Apply Now

                Finance

                Program Fees & Financing

                The Full stack Artificial Intelligence and Machine Learning course fee start from INR 59,000 (Excluding GST). We aim to deliver to you quality education considering the aspect of feasibility.

                Course feasibility

                We are driven by the idea of program affordability. So, we give you several financial options to manage and budget the course expenses. Because we believe in fair reachability and access to all our carefully curated programs. Therefore, you get options such as EMI to pay the course fees.

                Program Features

                Job Assistance

                Live Class Subscription

                LMS Subscription

                Job Referrals

                Industry Projects

                Capstone Projects

                Domain Training

                Project Certification from Companies

                Job Guarantee

                Basic

                ₹ 59,000 + GST

                Data Science Training

                1 Year

                Lifetime

                3+

                7+

                1

                Enroll Now

                Pro

                ₹ 89,000 + GST

                Real Work Experience

                3 Years

                Lifetime

                5+

                15+

                3

                Enroll Now

                Pro Max

                ₹ 1,30,000 + GST

                100% Job Guarantee

                3 Years

                Lifetime

                Unlimited

                15+

                3

                Enroll Now

                Basic

                ₹59,000 +GST

                1 Year

                Lifetime

                3+

                7+

                1

                Pro

                Price

                ₹89,000 +GST

                Job Assitance

                Live Class Subscription

                3 Year

                LMS Subscription

                Lifetime

                Job Referrals

                5+

                Industry Projects

                15+

                Capstone Projects

                3

                Domain Training

                Project Certification from companies

                Job Guarantee

                Pro Max

                ₹1,30,000 +GST

                3 Year

                Lifetime

                Unlimited

                15+

                3

                Talk to Expert

                Batch Details

                Program Cohorts

                Artificial Intelligence and Machine Learning Next 2022 Cohort
                next first batch details skillslash
                Weekday Batch

                📅 03 Feb, 2022
                🕒 08:00 - 10:00 AM

                next second batch details skillslash
                weekend Batch

                📅 16 Jan, 2022
                🕒 09:00 - 12:00 AM

                Enroll Now
                Artificial Intelligence and Machine Learning Next 2022 Cohort

                Date

                Time

                Batch type

                next first batch details skillslash

                Weekday batch

                next second batch details skillslash

                Weekend batch

                03 Feb 2022

                16 Jan 2022

                08:00 – 10:00 AM

                09:00 – 12:00 AM

                Weekday (Mon – Fri)

                Weekend (Sat-Sun)

                Enroll Now

                Got Questions regarding next cohort date?

                Talk to an admission counselor

                Frequently Asked Questions

                Go through the FAQ’s to know more about our Full stack Artificial Intelligence and Machine Learning program, fees and project details.

                Most importantly, in the majority of cases, we offer both offline and online learning choices. Aside from that, we provide a blended learning program designed specifically for working professionals. In addition, you can participate in live online sessions in hybrid learning mode to attend all theoretical courses in our Full stack Artificial Intelligence and Machine Learning course. Besides, you will also be performing some hands-on work on the industrial project site.

                That said, we can only offer fully online classes via live sessions due to the outbreak.This means you can talk to your instructor in real time, just like in a traditional face-to-face session. Additionally, at this time, all practical sessions will be conducted using cloud-based services.

                 

                Most importantly, we offer live online classes. Also, all of our students have access to recorded versions of those classes. In addition, we also give you unlimited access to these recorded sessions, to these recorded sessions, so you can go back to them whenever you need theoretical help in your AI and ML career. As a result, you won’t be disappointed if you miss any of the live classes. However, we strongly advise you to participate in all live classes.

                 

                Most importantly, if you do not understand an entire module under the Full stack artificial intelligence and machine learning course, you can repeat the same class with another batch. Thus, leaving no chance for you to remain in confusion about the learning modules. 

                First of all, every candidate will be given the opportunity to take a 20-minute online aptitude exam. If a candidate passes the aptitude exam with a score of more than 65 percent, he or she will be eligible for a 30 percent discount on the course fees. Besides, candidates who lost their jobs as a result of the COVID situation, as well as mothers who want to begin their careers, can receive up to a 100% scholarship based on their exam score.

                If you are considered eligible for a scholarship, you may be able to save up to 30% on your course fees. Moreover, we also offer scholarships to Covid-affected candidates, unemployed candidates, and mothers returning to the workforce after a vacation.

                The hitch is that if you take the artificial and machine learning course, you will not just obtain any type of certification or academic degree. Instead, we offer a globally recognized project management certificate. The firm with which you completed your industrial project can also give you direct certifications.

                For queries, feedback & assistance

                Get Free Career Counselling

                (7AM -12 AM)

                For Working Professionals
                1800-419-0840
                For Freshers
                022-6608-9514

                For queries, feedback & assistance

                Get Free Career Counselling

                (7AM -12 AM)

                For Working Professionals
                1800-419-0840
                For Freshers
                022-6608-9514