AI & Machine Learning Training

Become a Certified AI & Machine Learning Professional with Sankalpa Skills and master Python, Machine Learning Algorithms, Deep Learning, NLP, Computer Vision, and Data Modeling. Gain hands-on experience with real-world projects and accelerate your career in the AI industry

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Explore Our Industry-Aligned Curriculum

Introduction to AI & Machine Learning

Introduction to Artificial Intelligence
Understand the fundamentals of Artificial Intelligence, its history, and real-world applications. Learn how AI is transforming industries through automation, decision-making, and intelligent systems that mimic human behavior and improve efficiency.
Machine Learning Overview
Explore the basics of machine learning, including supervised, unsupervised, and reinforcement learning. Understand how machines learn from data to make predictions and improve performance over time.
AI vs Machine Learning vs Deep Learning
Learn the differences and relationships between AI, machine learning, and deep learning. Understand how these technologies work together to solve complex problems.
Tools & Environment Setup
Set up tools like Python, Jupyter Notebook, and essential libraries. Prepare your development environment for building AI and machine learning models.

Python for AI/ML

Python Fundamentals
Learn Python basics including variables, data types, loops, and functions. Build a strong programming foundation for AI and machine learning development.
Data Structures in Python
Understand lists, tuples, dictionaries, and sets. Learn how to store and manipulate data efficiently for machine learning tasks.
Libraries for AI/ML
Explore essential libraries like NumPy, Pandas, and Matplotlib for data handling and visualization.
Working with Jupyter Notebook
Use Jupyter Notebook to write, execute, and document your code interactively for better productivity.

Data Analysis & Preprocessing

Data Collection & Loading
Learn how to collect and load data from different sources such as CSV files, APIs, and databases for analysis.
Data Cleaning Techniques
Handle missing values, duplicates, and inconsistent data to prepare clean datasets for modeling.
Exploratory Data Analysis (EDA)
Analyze datasets using statistical methods and visualizations to identify patterns and insights.
Feature Engineering
Create and select important features to improve model performance and accuracy

Statistics & Mathematics for ML

Descriptive Statistics
Learn key statistical measures like mean, median, variance, and standard deviation to understand data.
Probability Concepts
Understand probability theory and its importance in machine learning models.
Linear Algebra Basics
Learn vectors, matrices, and operations used in machine learning algorithms.
Calculus Fundamentals
Understand derivatives and gradients used in optimization algorithms

Machine Learning Algorithms

Supervised Learning
Learn regression and classification techniques to make predictions using labeled data.
Unsupervised Learning
Understand clustering and dimensionality reduction for analyzing unlabeled data.
Regression Algorithms
Implement linear and polynomial regression for predictive modeling.
Classification Algorithms
Use algorithms like decision trees, KNN, and Naive Bayes for classification tasks

Deep Learning

Introduction to Neural Networks
Understand the structure and working of artificial neural networks.
Artificial Neural Networks (ANN)
Build and train ANN models for prediction and classification tasks.
Convolutional Neural Networks (CNN)
Use CNN for image processing and computer vision applications.
Recurrent Neural Networks (RNN)
Work with sequential data using RNN for tasks like time-series forecasting

Natural Language Processing (NLP)

Introduction to NLP
Understand how machines process and analyze human language.
Text Preprocessing
Clean and prepare text data using tokenization, stemming, and lemmatization.
Sentiment Analysis
Analyze emotions and opinions in text data.
Text Classification
Build models to classify text into categories

Model Evaluation & Optimization

Model Evaluation Metrics
Evaluate models using accuracy, precision, recall, F1-score, and confusion matrix.
Overfitting & Underfitting
Understand model performance issues and techniques to improve generalization.
Cross-Validation
Use cross-validation methods to ensure model reliability and robustness.
Hyperparameter Tuning
Optimize model performance using Grid Search and Random Search techniques

AI Tools & Deployment

Model Deployment
Deploy machine learning models using Flask or cloud platforms.
API Integration
Integrate models into applications using APIs.
Cloud Platforms (AWS/GCP)
Use cloud services to deploy and scale AI models.
Automation in AI
Automate workflows and pipelines for AI applications

Projects & Career Preparation

End-to-End AI Project
Work on real-world AI projects covering data collection, modeling, and deployment.
Portfolio Building
Create a strong portfolio showcasing AI and ML projects.
Resume Preparation
Build an industry-ready resume highlighting your skills and experience.
Interview Preparation
Prepare for AI/ML interviews with practical questions and case studies

Get Industry-Ready with Comprehensive Career Support

How our program works

Enhance Your Skills to Transform Your Career Path

Gain official proof of your expertise from a trusted institution, increasing your value in the eyes of employers and clients.
Stand out in competitive job markets and unlock better job roles, promotions, or freelance opportunities.
Certifications ensure your skills are aligned with current industry standards, making you job-ready from day one.
Completing a certified course strengthens your belief in your capabilities and readiness to tackle real-world challenges.
Join a professional community, gain access to hiring managers, and receive support from placement partners and mentors.

Build a Job-worthy Project-Portfolio

Gain hands-on experience with cutting-edge cloud services that will equip you with the essential skills needed to thrive in a cloud computing career

Land Your Dream Job Our Alumni

Frequently Asked Questions (FAQ)

Who can join your courses?

Anyone from students to working professionals can join. Beginner courses require no prior experience.

Are your classes online or offline?

We offer both online live classes and offline classroom sessions.

Do you provide placement assistance?

Yes, our placement team assists with resume building, interviews, and connecting students to IT companies.

Are course materials provided?

Yes, students receive study materials, assignments, and project-based exercises

Do you provide soft skills or interview preparation?

Yes, we provide resume building, mock interviews, and soft skills training.

Are there weekend-only batches?

Yes, we offer weekend batches for students and professionals with tight schedules.

Are the classes live or recorded?

We offer a mix of live sessions and pre-recorded content for flexible learning.

What is Sankalpa Infotech?

Sankalpa is a leading software training institute that provides high-quality training in IT, development, cloud computing, cybersecurity, and more.

What are the class timings?

We provide flexible class timings including weekdays and weekends to suit working professionals and students.

Can I get a demo class before enrolling?

Yes, we offer free demo sessions so you can experience the quality of our training before enrolling.

What are the prerequisites to join?

Most beginner-level courses have no prerequisites. Advanced programs may require basic IT or coding knowledge

What makes Sankalpa different from others?

We focus on practical learning, expert trainers, flexible schedules, placement support, and industry-ready curriculum.

What Our Learners Have To Say

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Our programs combine real-world projects, expert mentorship, and globally recognized certifications. empowering you to launch and grow your career in the cloud and beyond