998 Data Science courses

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In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.

In this course, you will be learning one of the widely used deep learning frameworks, that is, PyTorch, and learn the basics of convolutional neural networks in PyTorch. We will also cover the basics of Python and understand how to implement different Python libraries.

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

Kickstart your career with our Complete Python from Scratch: Start your career in Python 3+ course. Python is an all-purpose language with one of the biggest and abundant library features. It is used for a wide range of purposes such as web development, scripting, testing, app development, and data science. So it's one of the most sought after skills by employers. The Complete Python from Scratch: Start your career in Python 3+ course is designed to give you a complete understanding of the programming language right from setup to advanced level applications.The experience will provide you with the chance to work in a variety of sectors including web development, machine learning, data security, analytics and so much more. It will prepare you with sound theoretical and practical knowledge of Python programming that will prepare you to work with evidence-based strategies. If you are keen to equip yourself with knowledge of programming with Python and make a strategic career intervention, then choose our Complete Python from Scratch: Start your career in Python 3+ course. Upon completion of this CPD accredited course, you will be awarded a certificate of completion, as proof of your expertise in this field, and you can show off your certificate in your LinkedIn profile and in your resume to impress employers and boost your career. Our Complete Python from Scratch: Start your career in Python 3+ course is packed with 14 modules, with a total of 18 hours of learning materials. You will be able to study this course at your own pace, from anywhere and at any time. Enrol today and upgrade your knowledge on Python programming to lead a more prosperous life.

An intermediate-level course that will help you improve your Power BI skills and become an expert data analyst or data scientist. The course is carefully structured to provide an in-depth understanding of Microsoft Power BI and its features, along with some important tips and tricks.

This course empowers you to create interactive web applications using Shiny for Python. From fundamental concepts to advanced techniques, you will master web development with Python as your toolkit. Develop dynamic projects, learn diverse deployment methods, and embark on a journey to become a skilled Python web developer.

In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

If you are someone with a background in Python programming and is interested in presenting your analysis in interactive web-based dashboards, then you are in the right place. This course primarily focuses on Dash, along with other key data science libraries, including Pandas and Plotly. Learn to use Dash and Plotly in Python which can help you to visualize your critical insights and KPIs in web apps that are easily sharable.