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
Watch Intro Video
Intro Video
Get Project certification
And Real Work experience Directly From Industries
350 Hrs
Online Live classes
15+ Projects
Learn from Projects
25 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

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

100% Job Guarantee
Get 100% job guarantee in top companies and startups. Also, receive guidance on resume building and interview preparation.

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
Get to know in detail about our
Full Stack AI and ML Program
Basic
₹ 59,000 + GST
- Get foundational AI and Ml training
- Receive expert-level job assistance
Pro
₹ 89,000 + GST
- Earn real-work experience
- Advanced level AI and Ml training
Pro Max
₹ 1,30,000 + GST
- Pro-level AI and Ml training
- Avail unlimited job referrals
Get Real Work Experience Directly From Companies
How to Learn & Get Real Work Experience

Complete the required modules
Learn the skills needed for your project

Start your project with AI companies
Go through an internal assessment to start working

Get mentored on Projects by Skillslash experts
Work under the guidance of our mentors

Complete & Get certified by AI companies
Complete project deliverables and get certified
Get Hired
Work on live projects to get hired at:







































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.
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.

Syllabus
Artificial intelligence and machine learning course curated by leading faculties and industry leaders to provide pratical learning experience with live interactive classes and projects.
Program Highlights
- 350+ Live sessions
- 15+ Industry Projects
- Life time accessibility
- Live project experience
Request more Information
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.
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.
- 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
- 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.
- 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.
- 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)
- 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
- 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.
Industry - partnered capstone projects
Hands-on Projects
Data sets from the industry
Practice with 20+ tools
Designed by Industry Experts
Get Real-world Experience

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

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

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

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

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

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

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

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

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

Project Objective: Build motion prediction models for self driving vehicles

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

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

Project Objective: Sentiment analysis of vaccine on social media

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

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

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

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

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

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

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

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

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

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

Project Objective: Predict the rating and success of movies

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

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

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

- We assist our students to work on real-time projects from AI companies as part of Full stack Artificial Intelligence and Machine Learning program.
- This is advanced project experience certification that adds relevant value to our students profile.
- Our students work on these real projects by working from data cleaning to deployment of the project.
- 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.

- interact with our experts to construct customized learning routes based on your work goals and previous expertise.
- These are specialized courses available with an emphasis on industry training.
- Modules can be selected based on your preferred learning style.
- Choose your correct learning path to become an expert with our Full stack Artificial Intelligence and Machine Learning program.

- Work on and learn with our real time projects specific to your domain that makes you an expert data science professional.
- This artificial intelligence and machine learning course in Bangalore is built to provide you with advanced experience with projects.
- 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.
- The domain relevant project experience provides the right boost to the student’s career.

- We assist our students to work on real-time projects from AI companies as part of Full stack Artificial Intelligence and Machine Learning program.
- This is advanced project experience certification that adds relevant value to our students profile.
- Our students work on these real projects by working from data cleaning to deployment of the project.
- 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.

- Interact with our experts to construct customized learning routes based on your work goals and previous expertise.
- These are specialized courses available with an emphasis on industry training.
- Modules can be selected based on your preferred learning style.
- Choose your correct learning path to become an expert with our Full stack Artificial Intelligence and Machine Learning program.

- Work on and learn with our real time projects specific to your domain that makes you an expert data science professional.
- This artificial intelligence and machine learning program is built to provide you with advanced experience with projects.
- Students are allowed to bring their own projects to learn data science with their most relevant domain experience.
- 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.
25th February 2022
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
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
Batch Details
Program Cohorts
Artificial Intelligence and Machine Learning Next 2022 Cohort
Artificial Intelligence and Machine Learning Next 2022 Cohort
25 Feb 2022
16 Jan 2022
08:00 – 10:00 PM
09:00 – 12:00 AM
Weekday (Mon – Fri)
Weekend (Sat-Sun)
Got Questions regarding next cohort date?
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 experience certificate. The firm with which you completed your industrial project can also give you direct certifications.
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