Webskitters Academy aligns its Machine Learning training standards with globally recognized institutions and platforms.
Webskitters Academy creates an opportunity to learn within a real-world IT ecosystem. We are the learning and development division of Webskitters, successfully bridging the gap between academic learning and industry requirements. Every student enrolled in our ML DL course in Kolkata experiences instructor-led training through guided projects, hands-on labs, and a final capstone.
Build job-ready practical skills across Machine Learning, Deep Learning, and Applied AI while developing a GitHub project portfolio.
Whether it’s the beginning of your AI journey, your shift from analytics towards Machine Learning, or developing a practical foundation in ML/DL, our artificial intelligence and machine learning course is designed to provide structured learning through hands-on projects and practice, ensuring you become capable of achieving all your goals.
Recognized as one of the best Machine Learning course providers in Kolkata, Webskitters Academy combines hands-on learning, industry-standard tools, and business case studies to help you become job-ready in Machine Learning. We offer a structured learning journey that progresses from data foundations and Python to ML/DL.
Our machine learning course in Kolkata offers more than just understanding algorithms. It helps you explore and learn practical ML/DL concepts and industry-relevant tools, so you are well-prepared to support business decisions.
Learn Python refresher, Pandas, arrays, NumPy, data manipulation, DataFrames, functions, visualization, and Jupyter workflows to develop and strengthen a Python foundation for Machine Learning and data handling.
Leverage the opportunity to discover and learn about missing values, duplicates, outliers, data cleaning, scaling, categorical encoding, visualization, data preparation, and EDA (Exploratory Data Analysis).
Explore and learn linear multiple regression, polynomial concepts, linear regression, regularization, and evaluation using MSE, RMSE, MAE, and R².
Learn K-Means, DBSCAN overview, PCA, clustering evaluation, hierarchical clustering concepts, and practical use cases.
From median, mean, variance, and probability to vectors, matrices, distributions, and gradient intuition, learn all the mathematics and statistics required for Machine Learning.
Learn AI vs. ML vs. DL, ML types, train-validation-test workflows, features and labels, underfitting, and overfitting
Work with KNN, decision trees, logistic regression, SVM, random forest, and Naive Bayes, followed by learning recall, F1-score, precision, ROC-AUC, and confusion matrices.
From feature selection, class imbalance, transformation, and cross-validation to GridSearch/ RandomizedSearch, hyperparameter tuning, and model comparison, this module covers everything for you to learn about the ins and outs of feature engineering and model optimization.
Learn Python refresher, Pandas, arrays, NumPy, data manipulation, DataFrames, functions, visualization, and Jupyter workflows to develop and strengthen a Python foundation for Machine Learning and data handling.
From median, mean, variance, and probability to vectors, matrices, distributions, and gradient intuition, learn all the mathematics and statistics required for Machine Learning.
Leverage the opportunity to discover and learn about missing values, duplicates, outliers, data cleaning, scaling, categorical encoding, visualization, data preparation, and EDA (Exploratory Data Analysis).
Learn AI vs. ML vs. DL, ML types, train-validation-test workflows, features and labels, underfitting, and overfitting
Explore and learn linear multiple regression, polynomial concepts, linear regression, regularization, and evaluation using MSE, RMSE, MAE, and R².
Work with KNN, decision trees, logistic regression, SVM, random forest, and Naive Bayes, followed by learning recall, F1-score, precision, ROC-AUC, and confusion matrices.
Learn K-Means, DBSCAN overview, PCA, clustering evaluation, hierarchical clustering concepts, and practical use cases.
From feature selection, class imbalance, transformation, and cross-validation to GridSearch/ RandomizedSearch, hyperparameter tuning, and model comparison, this module covers everything for you to learn about the ins and outs of feature engineering and model optimization.
Salaries may vary depending on experience, technical skills, industry, and location.
₹6L - ₹10L / Year
$51.5K - $78.5K / Year
£33K – £39K / Year
$59K – C$96K / Year
A$95K – A$120K
Gain practical expertise in Python, Machine Learning algorithms, Neural Networks, Computer Vision, NLP, and AI model deployment through hands-on projects and expert-led training.
Read what our students say about their experience with us. We are really proud of creating a learning environment that helps people. Our goal is to support individuals in achieving their career goals through education and practical help.
Over the years, we have established a fruitful network with 300+ IT companies and MNCs, and they have numerous positive reviews to contribute.
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