Boost your Data Science and Machine Learning career with a certificate from IIT Roorkee. Master in-demand technologies like Machine Learning, Artificial Intelligence, OpenAI’s ChatGPT, Stable Diffusion, Deep Learning, Python, and more with hands-on experience in our cloud labs.
This course in Machine Learning, Generative AI, and Agentic AI is designed for those who want to go beyond using tools and truly understand how intelligent systems are built. You will develop strong foundations in algorithms, models, and system design, learning to think from first principles rather than relying only on libraries and APIs.
By the end of the program, you’ll understand how machine learning models are developed, how large language models are trained and fine-tuned, and how intelligent systems are structured to reason, plan, and act. From core ML algorithms to building your own ChatGPT-style system and agent-based …
Learn Python, NumPy, Pandas, Scikit-learn, HDFS, ZooKeeper, Hive, HBase, NoSQL, Oozie, Flume, Sqoop, Spark, Spark RDD, Spark Streaming, Kafka, SparkR, SparkSQL, MLlib, Regression, Clustering, Classification, SVM, Random Forests, Decision Trees, Dimensionality Reduction, TensorFlow 2, Keras, Convolutional & Recurrent Neural Networks, Autoencoders, and Reinforcement Learning
Learn Python, NumPy, Pandas, scikit-learn, Regression, Clustering, Classification, SVM, Random Forests, Decision Trees, Dimensionality Reduction, TensorFlow 2, Keras, Convolutional & Recurrent Neural Networks, Autoencoders, and Reinforcement Learning
Learn Python, NumPy, Scipy, Pandas, Jupyter, Scikit-learn, Regression, Clustering, Classification, Support Vector Machines, Random Forests, Decision Trees and Dimensionality Reduction
Learn Python, Jupyter, Linux, NumPy, SciPy, Scikit-learn, Pandas, Linear algebra, From Industry Experts. A foundation course for Machine Learning & Data Science
Learn Python, NumPy, Pandas, scikit-learn, Regression, Clustering, Classification, SVM, Random Forests, Decision Trees, Dimensionality Reduction, TensorFlow 2, Keras, Convolutional & Recurrent Neural Networks, Autoencoders, and Reinforcement Learning, Git, Docker, Kubernetes, Ansible, Terraform, Jenkins, and Graffana.
This chapter covers different NumPy constructs and functions along with Overview of Pandas, Matplotlib and Linear Algebra which is normally used in Machine Learning projects
A gentle introduction to the world of Machine Learning. Know about the various types of Machine Learning, and their various applications.
Welcome to this project on the Forecasting Bike Rentals with DecisionTreeRegressor, LinearRegression, RandomForestRegressor using scikit-learn. In this project, you will use Python and scikit-learn to build models using the above-mentioned algorithms, and apply them to forecast the bike rentals.
Forecasting is a regression problem, which is a highly demanded skill in the real world. This exercise enables you to understand the basic workflow to solve a regression problem, which includes data preprocessing and data modeling steps. You will understand how Pandas and scikit-learn, in association with Python, could be used to solve a machine learning problem end-to-end project. In addition …
This chapter covers different Pandas constructs and functions which are normally used in Machine Learning projects
