The 2024 Advanced Data Science Bundle

Learning machine learning with R programming.

Deep Learning is designed to provide you with a comprehensive understanding of deep learning. Throughout this course, you will delve into various deep learning models, learn about neural network topologiesYou will gain hands-on experience with TensorFlow and its different data types, and learn how to use frameworks such as Keras for building and training models. The course covers essential deep learning concepts including convolutional and recurrent neural networks, autoencoders, and data augmentation techniques, culminating in a hands-on project to solidify your learning. This course covers crucial topics, including the principles and applications of deep learning, the structure and functioning of neural networks, and practical implementation using TensorFlow and Keras. You will also learn how to work with datasets such as MNIST, and apply advanced techniques like data augmentation to enhance model performance.

Deep Learning with TensorFlow is designed to equip you with the essential knowledge and skills to master deep learning techniques using TensorFlow. This comprehensive program covers the fundamentals of TensorFlow, from installation to hands-on projects, ensuring you gain practical experience in building and deploying deep learning models. You will learn the basics of TensorFlow, understand its installation process, and work on a mini project that consolidates your learning by applying TensorFlow to solve a real-world problem.

Welcome to "Advanced Machine Learning with Python," a comprehensive course designed to equip you with the advanced knowledge and skills needed to excel in machine learning. Throughout this program, you will explore various advanced machine learning concepts, algorithms, and practical applications. This course covers crucial topics, including feature selection, data preprocessing, bagging and boosting techniques, and model tuning with grid search. You will also delve into specific algorithms such as linear regression, random forest, and Naive Bayes theorem, gaining hands-on experience with Python to build and optimize machine learning models.

Welcome to "Keras for Deep Learning," a comprehensive course designed to provide you with the essential knowledge and skills needed to harness the power of Keras for deep learning. Throughout this program, you will explore a wide range of deep learning concepts, algorithms, and practical applications, focusing on building, training, and deploying neural networks using the Keras framework. This course covers crucial topics, including an introduction to Keras, testing different versions of Keras, and understanding the key differences between TensorFlow and Keras. You will also delve into various types of neural networks and their applications, gaining hands-on experience with Keras to build effective deep learning models.

"Deep Learning With Keras & TensorFlow," a comprehensive course designed to equip you with the essential knowledge and skills needed to master deep learning using the powerful Keras and TensorFlow frameworks. Throughout this program, you will explore many deep learning concepts, and algorithms, focusing on building, training, and deploying deep neural networks. This course covers crucial topics, including an introduction to TensorFlow, hands-on experience with TensorFlow 2.0, and a mini project explanation to solidify your understanding. You will also delve into Keras, learning about its installation, various network types, and how to leverage its simplicity for rapid prototyping and experimentation. By the end of this course, you will be proficient in building complex models to tackle real-world problems.

"PySpark for Data Scientists," a comprehensive course designed to provide you with the essential knowledge and skills needed to harness the power of PySpark for big data analytics. Throughout this program, you will explore a wide range of concepts, algorithms, and practical applications, focusing on the core principles of distributed data processing and large-scale data analysis. This course covers crucial topics, including the skills required for data science and understanding PySpark and its applications. You will delve into data manipulation techniques, gain hands-on experience with data handling and transformation, and implement various PySpark functionalities.

Power BI Advanced Course is designed to gain mastery over advanced Power BI features and techniques, including creating sophisticated visualizations such as key influencers, waterfall charts, and decomposition trees. Learn to implement both static and dynamic row-level security to control data access. The course will cover advanced DAX functions for complex calculations, as well as advanced Power Query techniques for data transformation. Additionally, explore integration of Power BI with other Microsoft services like MS Teams and PowerPoint, and learn to configure subscriptions and paginated reports. A comprehensive end-to-end project will consolidate your learning.This course will equip you with the skills and knowledge to utilize Power BI's advanced features effectively and integrate machine learning concepts into your data analysis workflows.