मशीन लर्निंग वैज्ञानिक में Python

Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.

scikit-learn और scipy का उपयोग करके बिना लेबल वाले डेटासेट को क्लस्टर, ट्रांसफ़ॉर्म, विज़ुअलाइज़ और उनसे इनसाइट्स निकालना सीखें।

Arctic Penguin Exploration: Unraveling Clusters in the Icy Domain with K-means Clustering

In this course you will learn the details of linear classifiers like logistic regression and SVM.

इस पाठ्यक्रम में, आप scikit-learn का उपयोग करके regression और classification के लिए tree-based models और ensembles का उपयोग करना सीखेंगे।

Build a regression model for a DVD rental firm to predict rental duration. Evaluate models to recommend the best one.

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

मशीन लर्निंग के लिए अपने डेटा को साफ़ और तैयार करना सीखें!

This course focuses on feature engineering and machine learning for time series data.

Create new features to improve the performance of your Machine Learning models.

Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.

Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

Master text analysis with essential NLP techniques from preprocessing to advanced transformer models.

Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

PyTorch में अपना पहला न्यूरल नेटवर्क बनाना, हाइपरपैरामीटर समायोजित करना, और वर्गीकरण व प्रतिगमन समस्याएँ हल करना सीखें।

CNN, RNN, LSTM और GRU जैसी मूलभूत डीप लर्निंग आर्किटेक्चर सीखें, ताकि इमेज और क्रमिक डेटा का मॉडल बना सकें।

Learn to process, transform, and manipulate images at your will.

PySpark में महारत हासिल करें ताकि बड़े डेटा को आसानी से संभाल सकें—विशाल डेटासेट को प्रोसेस, क्वेरी और ऑप्टिमाइज़ करना सीखें, शक्तिशाली एनालिटिक्स के लिए!

Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

Learn how to approach and win competitions on Kaggle.