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Master ML & AI -100% Job Oriented Certification Course in Chandigarh

Unlock your potential in tech with our Machine Learning and Artificial Intelligence Course in Chandigarh. Our Software Training Institute offers practical training to help you succeed in the rapidly changing AI landscape.

Join us to stay ahead in technology and innovation, and become a leader in the AI revolution.

4.8 (868 ratings)
Rated 4.8 out of 5

35+ Modules Series

Earn a Certification that demonstrates your expertise.

Beginner Level

No previous experience with coding is required

4 months

1.5 hours/day class 

Flexible Schedule

Online/ Offline both modes of classes available.

Enroll Now

Machine Learning & AI Course Description

What is Machine Learning?

Machine Learning is considered the branch of computer science and AI artificial intelligence because it focuses on the use of algorithms and data. So they can learn, imitate human learning, and also improve accuracy.

Machine learning is a computer algorithm that can improve by itself with the help of past experiences and user data. In addition, it is used in email filtering, voice recognition, computer vision, and several other self-learned operations.

Future Scope of Machine Learning & AI

The future scope of Machine Learning is vast, with increasing demand across industries like healthcare, finance, and technology. As businesses embrace AI-driven solutions, expertise in Machine Learning ensures a thriving career in data science, automation, and innovation. Stay ahead with skills that are shaping the future of technology.

How Much Does a Machine Learning Engineer Make?

The national average salary for a Machine Learning engineer in India, based on salary estimates submitted anonymously to Glassdoor by over 1,977 employees.

₹11,00,000 Avg. Annual Salary
India (Glassdoor, 2026)

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  • Interview Prepration
  • ISO certified Facility

Why Choose Netmax for Machine Learning Training In Chandigarh

With over 23+ years of expertise, Netmax Technologies is recognized as one of the top Machine Learning Training Institute in Chandigarh

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Learn Machine Learning & AI from skilled mentors who work on real industry projects.

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Choose flexible AI and Machine Learning Training schedules for all learners.

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100% Practical Training

Practice ML & AI concepts through real datasets, coding tasks, and live demos.

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Learn updated ML & AI tools, algorithms, and models used in modern applications.

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Affordable Fees

Get AI and Machine Learning Training at budget-friendly fees with practical modules.

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Join live expert sessions covering real AI and Machine Learning Training topics.

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Hands on training with real world projects . Designed for students, Freshers and working professionals

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Build a powerful professional presence with a profile optimized to attract top recruiters and industry leaders.

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Get a high-impact, ATS-friendly resume designed to showcase your technical skills and project experience effectively.

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Mock Interviews

Gain confidence with simulated interview sessions and personalized feedback from industry experts to ace your real rounds.

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100% Job Assistance

Benefit from dedicated placement support, including exclusive lead updates and direct connections to hiring partners.

Industry Success: Top Companies Hiring Netmax Alumni

We at Netmax Technology provide Job Assistance and most of the students have secure their job in various companies. We have tieups with many companies in Tricity. 

Join Our Machine Learning and Artificial Intelligence Course in Chandigarh

During the Machine Learning and Artificial Intelligence Course in Chandigarh, you’ll learn about key concepts such as feature engineering, predictive analytics, and cloud-based AI solutions. Our Software Training Institute emphasizes experiential learning, empowering students to develop and deploy their own machine learning models. With expert guidance, you’ll gain the technical proficiency needed to excel in the fast-paced fields of data science and artificial intelligence in our Machine Learning & AI Course.

What you will Master- Key Skills

  • Python Programming and ML Libraries
  • Model Evaluation, Deployment, and Optimization
  • Reinforcement Learning and Decision-Making
  • Machine Learning: Scikit-Learn, XGBoost, LightGBM
  • Computer Vision: OpenCV, YOLO
  • Data Preprocessing and Feature Engineering
  • Ensemble Methods and Boosting Techniques
  • Big Data Analytics and Scalable Machine Learning
  • Deep Learning: TensorFlow 2.0, Keras, PyTorch
  • NLP & GenAI: NLTK, SpaCy, Hugging Face Transformers, OpenAI API

Detailed Curriculum: From Python Basics to Advance AI

  • Overview – What is python?
  • Evolution of python.
  • IDE installation 
  • Introduction to variables.
  • Declaring & consume a variable.
  • Different types of data type in python.
  • Mapping Types
  • Literals- Numeric, boolean & String
  • Introduction to conditional statements
  • Decision control instructions
  • Conditional expression
  • If, If- else, Nested if – else
  • Repetition control
  • Accessing strings
  • Basic Operations
  • String Slices
  • Function and Methods
  • Creating Lists using range
  • Updating the elements of lists
  • Concepts of concatenation, repetition
  • Aliasing and cloning
  • What is procedure oriented approach
  • Encapsulation
  • Abstraction
  • Inheritance – constructors, methods
  • Creating class
  • Variable and constructor
  • Instance, class, static methods
  • Errors in coding
  • User defined exceptions
  • Grammar of python
  • Define module.
  • Calling a module using import
  • Symbol table
  • Use user-define module.
  • Definitions, history, and applications of ML and AI
  • TYPES OF Machine learning ,
  • AI VS ML VS DL
  • Linear regression
  • Logistic regression
  • Decision trees and random forests
  • Support vector machines
  • K-means clustering
  • Principal component analysis
  •  
  • Perceptrons and multi-layer neural networks
  • Activation functions and backpropagation
  • Convolutional neural networks
  • Recurrent neural networks
  • Transfer learning and pre-trained models
  •  
  • Text preprocessing and feature extraction
  • Sentiment analysis
  • Language models (e.g., BERT, GPT)
  • Machine translation
  • Chatbots and conversational AI
  • Advantages of Database over files
  • Types of databases used with python
  • Installation of MySQL
  • Image classification and object detection
  • Semantic and instance segmentation
  • Generative adversarial networks (GANs)
  • Reinforcement learning in computer vision
  • Healthcare (e.g., disease prediction, drug discovery)
  • Finance (e.g., fraud detection, stock trading)
  • Autonomous vehicles
  • Recommendation systems
  • Robotics and control system
  • Bias, fairness, and transparency in ML/AI
  • Privacy and data protection
  • Explainable AI
  • Governance and regulation of AI systems
  • Implementing ML algorithms from scratch
  • Deploying ML models in real-world applications
  • Exploring state-of-the-art AI techniques
  • BATCH
  • ONLINE 
  • INSTANCE BASED ML
  • INSTALLATIONS
  • TENSORS
  • FRAME A PROBLEM
  •  MACHINE LEARNING DEVELOPMENT LIFE CYCLE
  • WORKING WITH TYPES OF DATA
  • JSON, CSV, SQL, API, WEB SCRAPING
  • UNDERSTANDING THE DATA
  • EDA
  • BIVARIATE, MULTIVARIATE AND UNIVARIATE
  • PANDAS PROFILING
  • FEATURE SCALING
  • STANDARDIZATION NORMALISATION
  • ENCODING DATA
  • ONE HOT, LABEL ENCODER, ORDINAL ENCODER
  • COLUMN TRANSFORMERS AND PIPELINES
  • FUNCTION TRANSFORMERS
  • LOG TRANSROM, POWER TRANSFORMER, BOX-COX
  • BINNING, BINARIZATION AND DISCRETIZATION
  • HANDLING MIXED VARIABLES
  • HANDLING DATE AND TIME VARIABLES
  • HANDLING MISSING NUMERICAL DATA
  • CCA, MEAN MEDIAN MODE IMPUTATION
  • HANDLING MISSING CATEGORICAL DATA
  • MODE IMPUTATION, CONSTANT IMPUTATION
  • HANDLING MISSING DATA
  • EOD IMPUTATION, RANDOM SAMPLE IMPUTATION
  • KNN, MICE IMPUTATION
  • HANDLING OUTL
  • Z-SCORE METHOD, IQR RULE, PERCENTILE RULE
  • FEATURE CONSTRUCTION AND SPLITTING
  •  DIMENSIONALITY REDUCTION
  • CURSE OF DIMENSIONALITY 
  • INTRO TO PCA
  • PCA GEOMETRIC INTUTION
  • PCA PROBLEM FORMULATION AND CODE EXAMPLE
  • SIMPLE LINEAR REGRESSION INTUTION
  • SIMPLE LINEAR REGRESSION MATHEMATICS AND PRACTICAL
  • TYPES OF METRICS (ROC, MAE, MSE, PRECISON, RECALL)
  • MULTIPLE LINEAR REGRESSION (GEOMETRIC INTUTION)
  • MULTIPLE LINEAR REGRESSION MATHEMATICS AND CODE
  • ASSUMPTIONS OF LINEAR REGRESSION 
  • BATCH GRADIENT DESCENT MATHEMATICS WITH CODE
  • STOCHASTIC GRADIENT DESCENT MATHMEATICS WITH CODE
  • POLYNOMIAL REGRESSION
  • BIAS VARIANCE TRADE OFF (OVER AND UNDER FITTING)
  • RIDGE REGRESSION GEOMETRIC INTUTION
  • RIDGE REGRESSION CODE AND GRADIENT DESCENT
  • KEY POINTS OF RIFGE REGRESSION
  • LASOO REGRESSION CODE AND INTUTION
  • WHY LASOO REGRESSION CREATES SPARSITY
  • ELASTIC NET REGRESSION CODE AND INTUTION
  • SOFTMAX AND MULTINOMIAL REGRESSION
  • POLYNOMIAL REGRESSION FEATURES AND HYPERPARAMETERS
  • DECISION TREES GEOMETRIC INTUTION
    DECISION TREES HYPERPARAMTERS AND OVERFITTING
  • REGRESSION TREES AND VISULAISING THE TREES
  • ENSEMBLE LEARNING
  • VOTING ENSEMBLE INTRO, REGRESSION AND CLASIFICATION
  • BAGGING INTRO, CLASSIFICATION, REGRESSION
  • RANDOM FOREST INTUTION AND MATHS
  • WHY RANDOM FOREST WORKS WELL
  • RANDOM FOREST VS BAGGING
  • RF HYPERARAMETERS AND TUNING THE HYPERPARAMETERS
  • OUT OF THE BAG EVALUATION RANDOM FOREST
  • FEATURE IMPORTANCCE
  • AGGLOMERATIVE CLUSTERING
  • KNN FROM SCRATCH MATHS AND CODE
  • ASSUMPTIONS OF LINEAR REGRESSION SVM GEOMETRIC INTUTION
  • KERNEL TRICK IN SVM AND MATHEMATICS
  •  
  • NAIVE BAYES
    NAIVE BAYES MATHEMATICS + CODE
  • XG BOOST INTRODUCTION
  • XG BOOST FOR CLASSIFICATION
  • XGBOOST FOR REGRESSION
  • XGBOOST MATHEMATICS
  • ASSOCIATION RULE MINING
  • APRIORI AND ECLAT
  • REINFORCEMENT LEARNING
  • UCB AND THOMSON SAMPLING

Netmax Technologies · Chandigarh

Machine Learning & Artificial Intelligence (Basics to Advanced Level) 35+ Modules

Phase 1 · Python Fundamentals 8 topics
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What is Python? Understand why Python is the world's leading programming language for AI, Machine Learning, automation, and software development.
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Evolution of Python Learn how Python evolved into one of the most widely used programming languages across industries.
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IDE Installation & Setup Configure a professional development environment using industry-standard Python tools.
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Python Features & Advantages Explore the powerful features that make Python easy to learn, scalable, and ideal for modern applications.
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Python Execution Process Understand how Python code is interpreted, executed, and converted into bytecode before running.
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Interactive Shell & Script Mode Learn the difference between interactive coding and script execution with practical examples.
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Python Applications Explore how Python is used in AI, Machine Learning, Data Science, Web Development, Automation, and IoT.
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Writing Your First Python Program Create and execute your first Python program while understanding the basic structure of Python code.
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Introduction to Variables Understand the purpose of variables and how data is stored and accessed in Python programs.
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Variable Declaration & Assignment Learn to declare, initialize, assign, and update variables using Python syntax.
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Python Data Types Explore built-in data types including int, float, complex, string, boolean, list, tuple, set, and dictionary.
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Numeric, Boolean & String Literals Understand different literal values used to represent numbers, text, and logical conditions.
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Mapping & Sequence Data Types Learn the fundamentals of dictionaries, lists, tuples, and other built-in collection types.
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Dynamic Typing in Python Understand how Python automatically determines data types during program execution.
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Type Casting & Type Conversion Convert values between different data types using Python's built-in conversion functions.
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Multiple Assignment & Variable Swapping Assign multiple variables efficiently and swap values using Python's simplified syntax.
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Naming Conventions & Best Practices Follow industry-standard variable naming conventions for writing clean and maintainable code.
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Introduction to Conditional Statements Understand how decision-making works in Python using conditional logic.
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Comparison & Logical Operators Learn relational and logical operators to evaluate conditions effectively.
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Decision Control Statements Build programs that make decisions based on different input conditions.
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if, if...else & elif Statements Implement single, multiple, and chained conditional statements with practical examples.
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Nested if Statements Handle complex decision-making by placing one condition inside another.
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Combining Multiple Conditions Use and, or, and not to solve real-world decision problems.
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Real-World Decision Making Programs Develop practical programs such as grading systems, login validation, and eligibility checkers.
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Introduction to Arrays Understand the concept of arrays and their role in storing collections of similar data.
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Python Lists vs Arrays Learn the differences between Python lists and arrays, and when to use each.
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Creating & Initializing Arrays Create arrays using Python's array module and initialize them with different data types.
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Array Indexing & Slicing Access individual elements and extract subsets of data using indexing and slicing.
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Array Operations Perform insertion, deletion, updating, searching, and traversal operations on arrays.
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Built-in Array Functions & Methods Work with commonly used functions and methods for efficient array manipulation.
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Introduction to NumPy Arrays Explore NumPy arrays for high-performance numerical computing and AI applications.
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Practical Array Programs Solve real-world problems using array operations, searching, sorting, and basic data processing.
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Introduction to Lists & Tuples Understand Python's most commonly used sequence data structures and their real-world applications.
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Creating Lists & Tuples Create lists and tuples using different techniques, including the range() function.
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Mutable vs Immutable Objects Learn the key differences between mutable lists and immutable tuples.
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List Operations Perform concatenation, repetition, membership testing, and element updates.
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Built-in List & Tuple Methods Work with commonly used methods for searching, counting, sorting, reversing, and organizing data.
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Packing & Unpacking Tuples Store and retrieve multiple values efficiently using tuple packing and unpacking.
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Aliasing, Cloning & Copying Understand shallow copy, deep copy, aliasing, and cloning techniques for Python collections.
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Nested Lists & Practical Applications Work with multidimensional lists and solve real-world data organization problems.
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Introduction to Object-Oriented Programming Understand the principles of OOP and its importance in building scalable software applications.
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Procedural vs Object-Oriented Programming Compare procedural and object-oriented approaches to software development.
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Classes & Objects Create classes and objects to model real-world entities using Python.
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Constructors & Instance Methods Initialize objects using constructors and implement instance methods for object behavior.
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Encapsulation Protect object data using access control and encapsulation techniques.
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Inheritance Reuse and extend existing classes through single and multiple inheritance.
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Polymorphism Implement method overriding and achieve flexible object-oriented designs.
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Abstraction Simplify complex applications using abstract classes and interfaces.
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Practical OOP Projects Apply object-oriented concepts by building real-world Python applications.
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Introduction to Errors & Exceptions Understand runtime errors, exceptions, and why exception handling is essential in Python.
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Types of Python Exceptions Learn common built-in exceptions such as NameError, TypeError, ValueError, IndexError, and KeyError.
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try, except, else & finally Handle exceptions gracefully using Python's built-in exception handling blocks.
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Handling Multiple Exceptions Manage different exception types efficiently within a single program.
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Raising Exceptions Generate exceptions explicitly using the raise statement for custom program validation.
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User-Defined Exceptions Create custom exception classes to handle application-specific error scenarios.
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Assertions & Debugging Use assertions and debugging techniques to identify and resolve programming errors.
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Exception Handling Best Practices Write reliable, maintainable Python programs by following industry-standard error handling practices.
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Introduction to Modules Understand the purpose of modules and how they help organize and reuse Python code.
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Importing Modules Learn to import modules using import, from, and as statements.
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Built-in Python Modules Explore commonly used standard library modules such as math, random, datetime, and os.
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Creating User-Defined Modules Create reusable Python modules and import them into different programs.
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Packages & Package Structure Organize related modules into packages for better project management.
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Module Search Path Understand how Python locates and loads modules using the module search path.
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Namespaces & Aliasing Manage naming conflicts using namespaces and module aliases.
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Working with Python Libraries Use external libraries and packages to build scalable Python applications.
Phase 2 · ML & AI Core 7 topics
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What is Machine Learning? Understand how machines learn patterns from data without being explicitly programmed.
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What is Artificial Intelligence? Explore the broader concept of AI and how it powers modern intelligent systems.
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AI vs ML vs Deep Learning Clarify the relationship between AI, Machine Learning, and Deep Learning with practical examples.
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Types of Machine Learning Learn the differences between supervised, unsupervised, and reinforcement learning approaches.
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Machine Learning Workflow Understand the complete process from data collection to model deployment.
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Real-World Applications of AI & ML Explore how AI and ML are transforming healthcare, finance, retail, and more.
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Python Libraries for ML Get introduced to essential libraries like NumPy, Pandas, and Scikit-learn used in every ML project.
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Linear Regression Predict continuous values using one of the most widely used ML algorithms in the industry.
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Logistic Regression Learn classification techniques used in fraud detection, spam filtering, and medical diagnosis.
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Decision Trees Build interpretable models used extensively in banking, healthcare, and risk assessment.
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Random Forest Combine multiple decision trees to build powerful, accurate ensemble models.
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K-Nearest Neighbors (KNN) Understand a simple yet effective algorithm used in recommendation systems.
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Support Vector Machines (SVM) Learn a powerful classification technique used in image and text classification.
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Naive Bayes Explore a probabilistic algorithm widely used in spam filters and sentiment analysis.
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Model Evaluation Metrics Measure model performance using accuracy, precision, recall, and F1-score.
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Introduction to Neural Networks Understand how neural networks are inspired by the human brain to process information.
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Perceptrons & Multi-Layer Perceptrons Learn the fundamental building blocks behind every deep learning model.
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Activation Functions Explore functions like ReLU and Sigmoid that enable networks to learn complex patterns.
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Forward Propagation Understand how data flows through a neural network to generate predictions.
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Backpropagation & Gradient Descent Learn the core training technique that powers every deep learning breakthrough.
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Loss Functions & Optimizers Understand how models measure and reduce prediction errors during training.
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Introduction to Deep Learning Frameworks Get hands-on exposure to industry-standard frameworks like TensorFlow and PyTorch.
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Introduction to NLP Understand how computers process and understand human language.
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Text Preprocessing Clean and prepare raw text data for machine learning models.
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Tokenization Break down text into words and sentences, the first step in every NLP pipeline.
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Stemming & Lemmatization Reduce words to their root forms to improve text analysis accuracy.
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Bag of Words & TF-IDF Convert text into numerical features that machine learning models can understand.
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Sentiment Analysis Analyze customer opinions and emotions from text data used across industries.
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Text Classification Build models that categorize text automatically, powering spam filters and news apps.
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Introduction to Transformers & LLMs Get introduced to the technology behind ChatGPT and modern language models.
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Introduction to Computer Vision Understand how machines interpret and analyze visual information.
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Image Processing Basics Learn fundamental techniques to manipulate and enhance digital images.
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OpenCV Fundamentals Work with one of the most popular libraries used for computer vision tasks.
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Convolutional Neural Networks (CNN) Explore the architecture that powers modern image recognition systems.
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Image Classification Build models that automatically identify and categorize images.
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Object Detection Learn techniques used in security systems, retail analytics, and autonomous vehicles.
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Real-World CV Applications Explore applications in facial recognition, medical imaging, and self-driving cars.
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AI in Healthcare Explore how AI is used in diagnostics, drug discovery, and patient care.
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AI in Finance Understand how banks and fintech companies use AI for fraud detection and risk analysis.
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AI in Retail & E-commerce Learn how AI powers personalized recommendations and demand forecasting.
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AI in Marketing Explore how businesses use AI for customer segmentation and targeted campaigns.
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Recommendation Systems Understand the technology behind Netflix, Amazon, and Spotify recommendations.
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Building an End-to-End AI Project Apply your skills by building a complete AI project from scratch.
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Industry Case Studies Analyze real business problems solved using AI and machine learning.
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Introduction to AI Ethics Understand why ethical considerations are critical in building AI systems.
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Bias in AI Learn how bias enters AI models and strategies to minimize it.
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Fairness & Transparency Explore techniques to build fair and transparent AI systems.
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Data Privacy & Security Understand how to protect user data while building AI applications.
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Explainable AI Learn techniques to make AI decisions understandable to humans.
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Regulatory Frameworks Know the compliance standards shaping AI policy worldwide.
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Responsible AI Practices Adopt best practices followed by leading tech companies for ethical AI development.
Phase 3 · Data & EDA 8 topics
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Introduction to Databases Understand how structured data is stored and managed in real-world applications.
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MySQL with Python Connect Python applications to relational databases used across the industry.
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SQLite Work with a lightweight database ideal for mobile apps and small-scale projects.
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CRUD Operations using Python Learn to create, read, update, and delete data programmatically.
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ORM Basics (SQLAlchemy) Write database queries using Python objects instead of raw SQL.
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Connecting Python to Cloud Databases Learn to integrate Python applications with cloud-based database services.
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Anaconda Installation Set up the standard data science environment used by analysts worldwide.
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Jupyter Notebook Basics Learn to use the tool every data scientist relies on to test and present work.
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VS Code for Data Science Configure a professional coding environment for data science projects.
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Google Colab Run Python code in the cloud with free access to GPUs for ML projects.
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Virtual Environments Manage project dependencies the way professional developers do.
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Managing Python Packages Install and manage libraries efficiently using pip and conda.
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Supervised Learning Used when labeled historical data is available, like credit scoring.
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Unsupervised Learning Used to find hidden patterns, like customer segmentation in marketing.
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Reinforcement Learning Explore the technique behind game-playing AI and robotics control systems.
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Semi-Supervised Learning Learn how models combine labeled and unlabeled data for better results.
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Choosing the Right ML Approach Understand how to select the appropriate learning method for real-world problems.
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Machine Learning Development Life Cycle Understand the exact process companies follow to build production AI models.
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Structured vs Unstructured Data Learn the difference between spreadsheet data and text, images, or audio.
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Data Collection Methods Explore techniques used to gather data from surveys, APIs, and web sources.
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Data Cleaning Fundamentals Learn to identify and fix common data quality issues before analysis.
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Data Formats (CSV, JSON, Excel) Work with the most common file formats used in real-world data projects.
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Introduction to Exploratory Data Analysis Learn the first step every data scientist takes before building a model.
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Pandas Profiling Instantly understand any dataset the way analysts do before modeling.
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Handling Missing Values Handle real-world messy data, since no dataset is ever perfectly clean.
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Outlier Detection Catch fraud and anomalies the same way banks and insurers do.
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Data Visualization for EDA Use charts and graphs to uncover patterns hidden in raw data.
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Correlation Analysis Identify relationships between variables to guide feature selection.
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Introduction to Feature Engineering Learn why the right features matter more than the algorithm itself.
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Encoding Categorical Data Convert categories into numbers the way every ML model requires.
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Feature Scaling Apply a step that can make or break model accuracy in production.
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Handling Imbalanced Data Learn techniques used in fraud detection to handle rare-event datasets.
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Feature Selection Techniques Focus only on the data that actually improves prediction results.
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Dimensionality Reduction Simplify massive datasets without losing the insights that matter.
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Principal Component Analysis (PCA) Use a technique to speed up and simplify complex ML models.
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Feature Extraction Derive new, meaningful features from raw data to improve model performance.
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Feature Importance Explain model decisions the way businesses demand for AI transparency.
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Handling Multicollinearity Identify and resolve correlated features that can distort model results.
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Real-World Case Studies Learn from real business problems solved using data and AI.
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Working with Kaggle Datasets Practice on the same datasets used in global data science competitions.
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End-to-End ML Pipeline Build a complete project you can showcase directly in interviews.
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Model Deployment Basics Understand how trained models are deployed into real-world applications.
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Building a Data Science Portfolio Create job-ready projects that demonstrate your practical skills to recruiters.

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Machine Learning & AI Certification

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Generative AI

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Frequently Asked Questions

What is the duration of the Machine learning course ?

The course is available for the duration of both 45 days and 3 months including 1.5 hours of classroom training and 1.5 hours of practical assignments.

For this course, there are no particular requirements. However, having a basic understanding of computers and the internet is helpful.

We provide classes both in-person and online. The format that best meets your needs can be selected.

Data Science is the broad field of extracting insights from data. Machine Learning is a subset focused on building algorithms that learn from that data to make predictions.

Absolutely. Our syllabus starts with “Introduction to Python” specifically for beginners. We cover the basics of programming before moving to complex AI topics.

Do you cover Generative AI and ChatGPT?

Yes, our advanced modules include an introduction to Generative AI, ChatGPT and Large Language Models (LLMs), and how to use APIs like OpenAI for building modern applications.

Yes, you will receive a certification from our institute after completing the course successfully.

You will gain practical expertise with a range of website strategies and tools through the completion of case studies, real-world projects, and assignments.

Who are the instructors for the course?

Our professors are professionals in the field with years of experience. They bring practical knowledge and real-world insights to the classroom.

You will be able to communicate with teachers and other students in one-on-one mentorship sessions.

Yes, we have a dedicated placement cell. We help you build a resume. optimize your LinkedIn profile for AI roles, and conduct mock technical interviews to prepare you for companies in Chandigarh and Mohali.

How do I enroll in the ML training course?

By going to our website and completing the online registration form, you can register. As an alternative, you can come to our institute to finish the registration procedure.

Completing an application, attending a brief interview, and paying the course price are all part of the admissions process.

Yes, we occasionally give out a variety of discounts. To learn more, please get in touch with our admissions office.

What kind of job roles can I expect after completing the Machine learning and AI course?

Completing the Machine Learning and Artificial Intelligence Course in Chandigarh at Netmax opens up various career opportunities:

1. AI Engineer: Work on creating and deploying AI models.
2. Machine Learning Engineer: Build systems that can learn from data and improve over time.
3. Data Scientist: Apply AI and ML techniques to extract insights from data.

Yes, we offer job placement assistance through our network of industry partners.

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