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Full stack Artificial Intelligence and Machine Learning course for professionals

Get expert training in live-interactive classes by enrolling in our Full stack Artificial Intelligence and Machine Learning course. In addition, get the chance to customize your courses and gain real-time project experience. Also, avail direct project certification from top startups.









Program features of Full stack AI and ML course

Project certification

Associate with real-time projects and gain direct company certification from top startups. 

Customize your courses

Self-design your learning modules with expert guidance under our Full stack AI and ML course. 

Course subscription

Get a three-year membership to access our artificial intelligence and machine learning course.

Career support

Receive free career counselling. Also, get resume preparation prospective job assistance. 

Get certified under expert training

Full stack Artificial Intelligence and Machine Learning course for professionals

Get expert training in live-interactive classes by enrolling in our Full stack Artificial Intelligence and Machine Learning course. In addition, get the chance to customize your courses and gain real-time project experience. Also, avail direct project certification from top startups.











Program features of Full stack Artificial Intelligence and Machine Learning course

Program Duration

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

Project certification

Associate with real-time projects and gain direct company certification from top startups. Moreover, bring your own projects and get assistance to complete them. 

Customize your courses

Self-design your learning modules with expert guidance. Meet your individual learning requirements under our Full stack ai and machine learning course. 

Course Fees

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

Course subscription

Get a three-year membership to access our artificial intelligence and machine learning online course. Thus, you can come back  and revise the modules.

Career support

Firstly, you can receive free career counselling. Besides, we also train you in resume preparation and give you prospective job assistance. 

Contact us to clear your queries

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    Get industry experience and gain certifications

    Project certification under our artificial intelligence and machine learning course

    Make your career switch easier by signing up for the best Full stack artificial intelligence and machine learning course. Besides, final year graduating students can avail project assistance too. Also, work in real-time projects in artificial intelligence and machine learning to earn direct company certifications to boost your portfolio.

    Get top-level industry experience

    Collaborate and work with well-known startups in AI and ML projects over different domains.

    Get placed in top-tier companies

    Receive company certifications for projects. Also, equip yourself to ace interviews to get placed.

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    Secure by career by signing up in the Full stack Artificial Intelligence and Machine Learning course online

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    Course curriculum of Full stack Artificial Intelligence and Machine Learning course

    Milestone 0

    Improve your programming skills

    Understand the fundamentals of programming. Furthermore, applicants from both the tech and non-tech domains can benefit from this, regardless of the IT/Non-IT domains.

    Learn the principles of mathematics

    Learn the fundamentals of statistics, calculus, and probability. Furthermore, this facilitates the understanding of data science and AI.

    Get skilled in domain and cloud computing

    The cloud will be introduced first, followed by domain training. In fact, we will cover a variety of topics in the latter, including manufacturing and healthcare.

    artificial intelligence course in bangalore Curriculum
    Milestone 1

    Learn python inside and out

    Acquire useful python and analytics expertise. For instance, Pandas, Numpy, Seaborn, and Matplotlib.

    Study about EDA, statistics, and storytelling

    Work on a variety of projects to gain experience with exploratory data analysis (EDA). In addition, gain a thorough understanding of statistics and probability.

    Case studies and projects

    Participate in a variety of python and data analytics case studies. Additionally, participate in one capstone project to get experience.

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

    Learn machine learning in-depth

    Study everything there is to know about ML. Especially, machine learning algorithms, supervised and unsupervised learning. Also, learn it alongside projects and case studies.

    Get skilled in time series and modelling

    Learn more about time series forecasting using Python. Also, get practical experience with sophisticated modelling approaches such as model tweaking and feature engineering.

    Capstone projects

    You will have the opportunity to work on capstone projects. In addition, you will have a better understanding of cloud computing. You will also receive domain training in areas such as retail and healthcare.

    Ai and ML course in bangalore Curriculum
    Milestone 3

    Learn more about computer vision and deep learning.

    Deep learning techniques are combined with advanced computer vision training. Also, get your hands on several case studies as well as a capstone project.

    Study about NLP and Auto AI.

    Learn more about advanced text analytics and natural language processing (NLP). Also, work on a chatbot project utilizing the Python NLTK library from the beginning.

    Model deployment and reinforcement learning

    Know how to use GCP and AWS to learn about reinforcement learning and model deployment.

    Milestone 4

    Preparation of resumes

    In addition to mastering core artificial intelligence and machine learning, you will receive great career training. Also, learn how to improve your resumes to get first preferences in hiring.

    Interview guidance and expert driven mock tests

    We make sure you are ready to face any interview questions straightaway. Besides, you will get trained with mock exams and practice answering critical interview questions.

    Job referrals

    Sign up for our Full stack AI and ML with job guarantee to get employment references. Thus, making it simpler to get employed with top startups and MNCs.

    Resume Preparation and Inteview Guidance for artificial intelligence course in bangalore
    Reasons why you should enroll in the program

    Specific program features of Skillslash’s Full stack Artificial Intelligence and Machine Learning course

    Elements that distinguish Skillslash Full stack AI and ML course from others

    Project certification

    You will be able to bring your own projects and work on them with the assistance of a professional. Furthermore, companies you associate with for projects provide direct project certification. In addition to theory, you will have expert industrial training.

    Get hired by MNC’s

    At Skillslash, you will receive expert-level training. In addition, the coaching will ensure that you are well-prepared for and successful at interviews. Furthermore, you will also receive career counselling and study programmes.

    Domain training

    You will have the option of taking elective courses in industrial and functional specializations. You will also receive domain training, which will assist you in adjusting to your new role. Particularly in data science, artificial intelligence and machine learning domains.

    Student Feedback on our Full stack AI and ML course

    Reviews from student community on Skillslash’s
    Full stack Artificial Intelligence and Machine Learning course

    The benefits of Skillslash’s Full stack Artificial Intelligence and Machine Learning course

    Artificial intelligence work with startups
    1. With project certificates, you can create  appealing project portfolios. And, also,  put your problem-solving skills to the test in a variety of real-world circumstances.
    2. To raise your profile, obtain credentials from top-tier companies.
    3. You have the option of choosing a project that matches your domain preferences. Furthermore, if you have shareable certificates, you may receive interview preferences.
    4. Besides, internships with industrial training are also available for newcomers in the field.
    A girl student in front of a laptop getting artificial intelligence training online
    1. There are specialized courses available with an emphasis on industry training. Make use of programmes that are taught by an instructor as well.
    2. Using the platform, you can construct customized learning routes based on your work goals and previous expertise.
    3. Modules can be selected based on your preferred learning style. Besides, select chapters that are relevant to your profession 
    4. The Full Stack Artificial Intelligence and Machine Learning course covers a wide range of topics and allows you to choose your learning path.
    Work on Live projects in bangalore's Best artificial intelligence course
    1. With our assistance, students enrolled in our Full stack Artificial Intelligence and Machine Learning course can work on their own projects and reap great results.
    2. It is a little more advanced sort of project certification. In addition, those with suitable experience are being sought for.
    3. You will be able to take part in live project training in a relaxed environment.
    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.

    Course details of Full stack Artificial Intelligence and Machine Learning course

    Chapter 1: Introduction to Programming ( 3 hrs )
    • What is a programming language ?
    • Source code Vs bytecode Vs machine code

    Also, learn about –

    • 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

    And also,

    • Procedure oriented Vs modular programming
    Chapter 4: Statistics basics (4 hrs)
    • Introduction to statistics
    • Measures of Central Tendency

    Further, it covers –

    • Measures of dispersion
    • Inferential statistics and Sampling theory
    Chapter 5: Introduction to probability (4 hrs)
    • Introduction to probability
    • permutations and combinations

    Also, including –

    • 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

    In addition, it includes –

    • Why do we prefer to code in a High-Level Programming
    • What is Compiler? What is an Interpreter?

    Also, it covers –

    • 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?

    Further, it includes –

    • 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”, “if-else”, “if-elif-else” statement

    • Loop Statement

    For loop

    When to use for loop

    For-else loop

    While Loop

    Also, covering –

    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

    Also, it covers –

    • Understanding
    • Few important function

    Besides, it contains –

    • Command Line Arguments
    • Python Operators
    Data Types in Python
      • Fundamental Data Types
          • Strings
          • Numbers
          • None Type
          • Boolean Type

    Also, covering –

    • Derived Data Structure
    • Introduction to List

    Further, the module has –

    • 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.

    Also, the module covers –

    • Function call vs Function referencing
    • What are inputs and outputs to the function?
    • Parameters vs Arguments
    • Types of Arguments

    Further, covering –

    • Return statement
    • Recursion
    • Namespace vs Scope
    • Anonymous Function and Lambda Expression

    Besides, the module has –

    • 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

    Besides, it has –

          • Types of Modules
            • Builtin Modules
            • User Defined Modules
            • Package

    File Handling

    • What is File Handling?
    • Why do we need File Handling?

    And, also covering –

    • Type of Files
    • File Operation

    Regular Expression

    • What is Regular Expression?
    • Why do we need Regular Expression?
    • Importing regex module

    Also, it contains –

    • 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

    In addition, the module has –

      • Arrays with Structured Data
      • Changing shape of an array
      • Array Broadcasting
      • Vectorization
      • Iterating over an array
      • Splitting an array
      • View vs Copy

    And, also –

      • 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

    Further, the module has –

      • Beizer Curves
      • Curves
      • Annotations
      • Scales
      • Twin Axis

    And, also –

      • 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

    Also, covering –

    • 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

    Also, it covers –

      • Reshaping Data Frame
      • Handling Missing Data in data Frame
      • Grouping Data Frame
      • Sorting Data Frame
      • Stacking and Unstacking

    Besides, it has –

      • 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

    Also, it covers –

    • 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

    Further, the module has –

    • 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

    Also, it covers –

    • 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

    In addition, it has –

    • Impact of Data Visualization
    • Univariate Analysis
    • Bivariate Analysis and ANOVA

    Further, it contains –

    • 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

    Also, it contains –

    • 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

    Moreover, it contains –

    • 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

    In addition, it has –

    • Data imbalance and redressal methodology
    • Upsampling, Downsampling and SMOTE

    In addition, it has –

    • one vs Rest (OVR) for multinormal classification.
    • Model Evaluation Metrics for classification-confusion matrix/
    • Misclassification error, Precision, Recall, F1 score, and AUC-ROC

    And, also –

    • Choosing the best error metric for a problem
    Part 3 - Clustering - K means
    • Introduction to Unsupervised Learning
    • Hierarchical and Non-Hierarchical techniques

    And also,

    • 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

    And also,

    • 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

    In addition. It covers –

    • Information Theory – Measures of Impurity
    • Stopping criteria for trees
    • Susceptibility to overfitting and high variance

    And also,

    • 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

    Further, covering –

    • Random Forests and its Application in Feature selection
    • Scent and Boosting

    And also,

    • 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

    Further, it has –

    • 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

    And, also –

    • 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

    And, also –

    • 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

    In addition, covering –

    • Linear Discriminant Analysis
    • Feature Selection Techniques – Forward and Backward
    • elimination, RFE

    Also, it contains-

    • Model Tuning and Selection
    • Deploying a Machine Learning Model
    • Serving the model via Rest API

    Part 12 – AutoML

    • Introduction to AutoML
    • Auto learn

    Also, covering –

    • TPOT models
    • Auto Keras

    Part 1 – Neural Networks

    • introduction to Neural Networks
    • Layered Neural Network
    • Activation function and their application

    Also, it has –

    • 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

    Also, it has –

    • 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

    And, also –

    • The problem of Exploding and Vanishing gradients

    Part 5 – Convolutional Neural networks

    • Architecture and design of a Convolutional network
    • Pooling and Flattening layer

    Further, it contains –

    • 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

    Additionally, it has –

    • Applying RNN/LSTMs to language and character modelling
    • Advanced Time series forecasting using RNNs with LSTMs
    • LSTMs vs GRUs – Key takeaways

    Additionally, it has –

    Part 7 – Restricted Boltzmann Machines and Autoencoders

    • Intro to RBMs and their training
    • Application of RBMs in Collaborative filtering
    • Intro to Autoencoders

    And, also –

    • 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

    Further, it contains –

    • Viterbi algorithms
    • MEMMs and CRFs for named entity recognition
    • Neural Language models

    And, also –

    • 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

    And also,

    • Introduction to topical modeling
    Part 3 – Sequence to Sequence tasks
    • Introduction to Machine translation
    • Word Alignment models and Encoder-Decoder Architecture

    And, also it has –

    • 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)

    Further, it has –

    • Dynamic Programming
    • Monte Carlo Control

    And, also it covers –

    • Temporal Difference (TD) Learning (Q-Learning and SARSA)
    • Approximation Methods (i.e. how to plug in a deep neural

    And, also it covers –

    • network or other differentiable model into your RL algorithm)
    • Mathematics for Computer Vision
    • Intro to Transfer Learning

    Besides, the module covers –

    • R-CNN and RetinaNet models for Object detection using Tensorflow
    • FCN architecture for Image segmentation

    And also,

    • 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

    Also, it has –

    • 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

    Further, the module contains –

    • Excel Toolbars: Hiding, Displaying, and Moving Toolbars
    • Switching Between Sheets in a Workbook
    • Inserting and Deleting Worksheets

    Also, covering –

    • Renaming and Moving Worksheets
    • Protecting a Workbook
    • Hiding and Unhiding Columns, Rows and Sheets
    • Splitting and Freezing a Window
    • Inserting Page Breaks

    Besides, it contains –

    • 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

    Further, the module has –

    • Adjusting Row Height and Column Width
    • Changing Cell Alignment
    • Adding Borders
    • Applying Colors and Patterns

    Besides, it contains –

    • Using the Format Painter
    • Formatting Data as Currency Values
    • Formatting Percentages

    Also, covering –

    • 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

    And, also –

    • 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

    Besides, there are –

    • Entering Date Values and using AutoComplete
    • Moving and Copying Cells with Drag and Drop
    • Using the Paste Special Command

    Also, it has –

    • Inserting and Deleting Cells, Rows, and Columns
    • Using Undo, Redo, and Repeat
    • Checking Your Spelling

    Further, it has –

    • 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

    Also, it has –

    • 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

    And, also –

    • Fixing Errors in Your Formulas
    • Formulas with Several Operators and Cell Ranges
    • Quiz
    Excel Functions
    • Introduction to excel functions
    • Learn about SUM() Function
    • Work with the MIN() and MAX() Functions
    • Get your hands on the AVERAGE() Function

    Further, it has –

    • Working with the COUNT() Function
    • Adjacent Cells Error in Excel Calculations
    • Use of AutoSum command
    • AutoSum shortcut key

    And, also –

    • Using the AutoFill Command to Copy Formulas
    • Quiz
    Working with Charts / Graphs
    • Creating a column Chart
    • Working with the Excel Chart Ribbon

    Also, the module has-

    • Adding and Modifying Data on an Excel Chart
    • Formatting an Excel Chart
    • Moving a Chart to another Worksheet
    • Resizing a Chart

    Besides, there are –

    • Changing a Chart’s Source Data
    • Adding Titles, Gridlines, and a Data Table
    • Formatting a Data Series and Chart Axis
    • Using Fill EffectsAnd, also –

    And, also –

    • Changing a Chart Type and Working with Pie Charts
    • Quiz
    Data Analysis & Pivot Tables
        • Why Pivot Tables
        • Structuring Source Data for Analysis in Excel

    In addition, it contains –

        • Creating a PivotTable
        • Navigating & Manipulating the Pivot Table Field List
        • Exploring Pivot Table Analyze & Design OptionsAdditionally, there is –
          • 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

          And, also –

          • 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

          And also, it covers –

          • Difference between SQL & MongoDB
          • Different Structured Query languages
          • Why MySQL?
          • Installation of MySQL

          Besides, there are –

          • DDL
          • SQL Keywords
          • DCL
          • TCL
          • Database Vs Excel Sheets
          • relational and database schema

          Also, it has –

          • Foreign and Primary Keys
          • database manipulation, management, and administration
    2. NoSQL Databases :
      • Topics – What is HBase?
      • HBase Architecture
      • HBase Components,
      • Storage Model of HBase,

      And, also –

      • HBase vs RDBMS
      • Introduction to Mongo DB, CRUD
      • Advantages of MongoDB over RDBMS
      • Use cases
      First Step in SQL Database
      • Creating Database
      • Dropping Database
      • Also, it has –
      • Using Database
      • Introduction to Tables
      • Data types in SQL

      Further, the module has –

      • 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
      • BETWEEN
      • BETWEEN – AND
      SQL Intermediate Statements
      • Multiple INSERT
      • GROUP BY
      • HAVING

      Further, it has –

      • WHERE vs HAVING
      • UPDATE
      • DELETE
      • And, also –
      • AS
      Aggregator Functions
      • Application of Group By
      • Count Function

      And, also –

      • MIN and MAX
      • Sum Function
      • Avg Function
      • Introduction to JOINs
      • INNER Join
      • OUTER Join
      • Full Join

      Also, it contains –

      • Left Join
      • Right Join


    SQL String Functions
    • Loading Data
    • CONCAT

    Besides, there are –


    And, also covering –

    Advance SQL
    • Local, Session, Global Variables
    • Timestamps and Extract
    • TO_CHAR

    Further, including –

    • Mathematical Functions and Operators
    • Operators and their precedence
    • String Functions and Operators

    Further, it has –

    • SubQuery
    • Self-Join
    • ALTER table

    Additionally, there are –

    • CASE
    • CAST
    • NULLIF

    And, also –

    • Check Constarints
    • Views
    • Import & Export
    Basics and CRUD Operation
    • Databases, Collection & Documents
    • Shell & MongoDB drivers
    • What is JSON Data

    And also,

    • 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

    Also, there is –

    • What are Object Ids in MongoDb
    • Data Formats in MongoDB
    • MongoDB Aggregation Framework
    • Aggregating Documents

    Besides, there is –

    • 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

    Also, there is –

    • Creating Calculated Fields
    • Adding Colors
    • Adding Labels and Formatting
    • Exporting Your Worksheet
    • Creating dashboard pages

    Additionally, there are –

    • 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

    Besides this, there is –

    • Data Over Time – Tableau
    • Implementation
    • Advance Table Calculations
    • Creating multiple joins in Tableau
    • Relationships vs Joins

    And, also –

    • 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

    Besides, it covers –

      • Map Layers
      • Custom Territories
      • Common Mapping Issues
      • Creating a Map, Working with Hierarchies
      • Coordinate points

    Further, it has

    Power BI for Business
    Introduction to Power BI
    • Why Power BI?
    • Account Types
    • Installing Power BI

    And, it also has –

    • 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

    Further, it contains –

    • Create calculate columns
    • Make first row as headers
    • Change Data type
    • Rearrange the columns
    • Remove duplicates

    Besides, it covers –

    • Unpivot columns and split columns
    • Working with Filters
    • Appending Queries
    • Working with Columns
    • Replacing Values
    • Splitting Columns

    And, also –

    • Formatting Data & Handling Formatting Errors
    • Pivoting & Unpivoting Data
    • Query Duplicates vs References
    • Append Queries

    Moreover, it contains –

    • Merging Queries
    • DIM-Region Table
    • Understanding “Extract”
    • Basic Mathematical Operations
    • Introduction
    • Line Charts
    • Pie Chart
    • Bar Charts
    • Stacked bar Chart

    Also, it covers –

    • Clustered Column Chart
    • Combo Chart
    • Treemap Chart

    Further, covering –

    • funnel Chart
    • Scatter Chart
    • Gauge Card
    • Matrix

    Additionally, covering –

    • Table
    • Slicers
    • KPIs
    • Maps

    In addition, it has –

    • 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

    Further, it includes –

    • Creating a Customer Segmentation
    • Analyzing the Customer
    • Segmentation Dashboard
    • Waterfall, Map Visualization

    Also, it has –

    • Pie and Tree Map
    • Include and Exclude
    • Categories with no Data
    Data Models: Data and Relationship
    • Understanding Relationships
    • Many-to-One & One-to-One

    Besides, it has –

    • Cross Filter Direction & Many-to-Many
    • M-Language vs DAX (Data Analysis Expressions)
    • Basics of DAX
    • DAX Data Types

    Also, it contains –

    • DAX Operators and Syntax
    • Importing Data for DAX Learning
    • Resources for DAX Learning

    Further, including –

    • M vs DAX
    • Understanding IF & RELATED
    • Create a Column
    • Rules to Create Measures
    • Calculated Columns vs Calculated Measures
    • Understanding CALCULATE & FILTER

    Further, including –

    • Understanding “Data Category”
    Time Intelligence
    • Create Date Table in M
    • Create Date Table in DAX
    • Display Last Refresh Date

    And, also –

    • Creating your first report
    • Modelling Basics to Advance
    • Modelling and Relationship
    • Ways of creating relationship

    Further, it contains –

    • Normalization – De Normalization
    • OLTP vs OLAP
    • Star Schema vs Snowflake Schema

    Work in live projects and gain expertise

    This Full stack Artificial Intelligence and Machine Learning course comprises 15+ industry-level projects.

    Expert feedback on our program

    Recommendations from top industry experts on our
    Full stack Artificial Intelligence and Machine Learning course


    Contact us to know more about the Full stack Artificial Intelligence and Machine Learning course

      Full Stack AI and Ml Certification Program

      89,000 + 18% GST

      The Full stack Artificial Intelligence and Machine Learning course is specifically designed for working professionals. That said, the Full Stack Artificial Intelligence and Machine Learning course is specifically curated for those who wish to get hired as data science professionals in product-based companies and startups. In addition, earn valid certification.


      Additional information

      Select Batch

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

      Frequently Asked Questions

      What are the various learning options offered under Skillslash’s artificial intelligence and machine learning course?

      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 full 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.

      What is Full stack Artificial Intelligence and Machine Learning course duration and what modules are covered?

      The Full stack Artificial Intelligence and Machine Learning course duration is 9 months. Depending on your schedule (weekdays/weekends), the amount of time you spend learning will vary. Besides, you will also be able to learn a number of topics through various courses. Most importantly, starting with the principles of programming, such as python and cloud basics, and progressing to Tensorflow based deep learning, NLP and Artificial Neural Networks. In addition, you will also get familiarized with tools such as AWS, Advance Excel, MongoDB, Power BI and Tableau.

      Are the lecturers university researchers (data science) or professionals?

      We strive to ensure that all of our students are highly employable in today’s work market. As a result, we only hire teachers who have taught the courses in question for at least six years.

      What are the prerequisites for enrolling in a Full stack Artificial Intelligence and Machine Learning course? Who can learn artificial intelligence and machine learning?

      Every enthusiastic data science aspirant, we feel, is qualified to make a successful data science career move. So, No, we haven’t established any stringent eligibility requirements. However, we verify candidates’ suitability on the learning module based on their knowledge of statistics and skills in programming.

      However, the purpose of this eligibility check is to provide the right level of guidance. Module 0 provides further assistance to candidates with non-statistical and non-programming backgrounds. Besides,  in module 0, we start from the beginning and teach them the fundamentals. Aside from that, a candidate must have at least one year of professional experience in any field. And, a bachelor’s degree in technology or engineering in any discipline or a Masters degree in Computer Applications, Technology or Business Administration.

      What is the Full stack Artificial Intelligence and Machine Learning course fee at Skillslash?

      At Skillslash, we have a data science course fee of INR 89,000 + GST. Furthermore, there is a scholarship programme that you can avail under our Full stack Artificial Intelligence and Machine Learning course.

      Is it required that I pay all of the costs at the same time when I register?

      Most importantly, we provide two different payment options. During the final registration procedure, you will be required to make a one-time payment. Aside from that, you can receive a reduced EMI and an education loan by using well-known credit cards. Which means, you can give a token amount and get a loan approved and pay the rest of the amount with zero EMI.

      What is the Full stack artificial intelligence and machine learning scholarship programme from Skillslash?

      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 expenses. 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.

      Are there any course fee discounts available with Skillslash’s ai and ml course?

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

      What makes Skillslash's placement assistance programme stand out from the rest?

      Our placement assistance package includes resume writing assistance. In addition, mock interview sessions are also available to assist you in preparing for your chosen career role’s interview. Furthermore, you may expect to receive an interview call from product-based MNCs and startups with the help of referrals

      Is the placement programme akin to attending a university or college on campus?

      We currently do not provide any campusing perks comparable to those provided by institutions. However, we can assist you in finding work in a variety of ways. To put it another way, we will give you real-world industrial project experience as well as a mock interview to assist you prepare for a certain job role. So, once you have completed your Full Stack Artificial Intelligence and Machine Learning course, we will start pitching you to product-based MNCs and startups.

      Will there be a make-up class available if I have to miss a session due to an emergency?

      Most importantly, we offer live online classes. Also, all of our students have access to recorded versions of those classes. In addition, w e also give you unlimited access 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.

      What if I did not understand an entire module? Will I be able to repeat the class?

      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. 

      What is the purpose of the industrial project experience program's 'domain specialisation tracking' element?

      Our industry experts faculty will assist you in identifying the ideal employment role based on your subject knowledge. Furthermore, you will be able to select the appropriate industrial project based on your prior work experience.

      What kind of certification does the Full stack Artificial Intelligence and Machine Learning course provide?

      The hitch is that if you take the artificial and machine learning course, you will not 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.

      How many projects are included in this online course in artificial intelligence and machine learning?

      The ai and ml course includes 15+ real-world projects from which you can select a couple for case study learning based on your domain’s needs. Aside from that, you will have the opportunity to work on three industry projects (capstone projects) with a variety of MNCs and startups during the following modules.

      What are the varieties of projects included under the Full Stack artificial intelligence and machine learning course?

      Under the Full Stack Artificial Intelligence and Machine Learning course, you will get to work in a variety of real-time projects in AI and ML spread across different domains. Such as – healthcare, finance, manufacturing etc. Like heavy machine predictive maintenance, prediction of customer satisfaction levels in e-commerce, prediction of cardiovascular diseases (CVDs) etc.

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