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Kickstart your learning of Python with this beginner-friendly self-paced course taught by an expert. Python is one of the most popular languages in the programming and data science world and demand for individuals who have the ability to apply Python has never been higher. This introduction to Python course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn about Python basics and the different data types. You will familiarize yourself with Python Data structures like List and Tuples, as well as logic concepts like conditions and branching. You will use Python libraries such as Pandas, Numpy & Beautiful Soup. You’ll also use Python to perform tasks such as data collection and web scraping with APIs. You will practice and apply what you learn through hands-on labs using Jupyter Notebooks. By the end of this course, you’ll feel comfortable creating basic programs, working with data, and automating real-world tasks using Python. This course is suitable for anyone who wants to learn Data Science, Data Analytics, Software Development, Data Engineering, AI, and DevOps as well as a number of other job roles.

This specialization will provide learners with the fundamentals of using Big Data, Artificial Intelligence, and Machine Learning and the various areas in which you can deploy them to support your business. You'll cover ethics and risks of AI, designing governance frameworks to fairly apply AI, and also cover people management in the fair design of HR functions within Machine Learning. You'll also learn effective marketing strategies using data analytics, and how personalization can enhance and prolong the customer journey and lifecycle. Finally, you will hear from industry leaders who will provide you with insights into how AI and Big Data are revolutionizing the way we do business. By the end of this specialization, you will be able to implement ethical AI strategies for people management and have a better understanding of the relationship between data analytics, artificial intelligence, and machine learning. You will leave this specialization with insight into how these tools can shape and influence how you manage your business. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource,". You can find Professor Hosanagar's book on his personal website or atPenguin Randomhouse.

What you'll learn ✓ Identify problems healthcare providers face that machine learning can solve ✓ Analyze how AI affects patient care safety, quality, and research ✓ Relate AI to the science, practice, and business of medicine ✓ Apply the building blocks of AI to help you innovate and understand emerging technologies

What you'll learn ✓ Describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction ✓ Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn ✓ Deploy machine learning algorithms and pipelines on Apache Spark ✓ Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow

What you'll learn ✓ Apply your skills to identify real-world generative AI uses and popular generative AI models and tools for text, code, image, audio, and video ✓ Apply best practices for creating prompts and explore examples of impactful prompts ✓ Employ generative AI tools and techniques for product management tasks, such as creating product concepts, roadmaps, and marketing collaterals ✓ Demonstrate the skills, tools and techniques required to boost your product manager career using generative AI

What you'll learn ✓ Apply your skills to recognize real-world generative AI uses and identify popular generative AI models and tools. ✓ Gain knowledge of generative AI prompt engineering concepts, examples, common tools and techniques needed to create effective, impactful prompts. ✓ Identify appropriate generative AI tools for data analytics. ✓ Examine real-world applications where generative AI can enhance data analytics workflows.

CuMind's 6-Pillar AI Learning Path curated by AI industrial experts is a video series that breaks down AI into six easy-to-understand pillars, with real-life examples to illustrate each concept. The six pillars covered in the series are: • AI Introduction • Machine Learning • NLP (Natural Language Processing) • Generative AI • AI in Action • AI Advanced Whether you're a complete beginner or you have some existing knowledge of AI, this series is a great way to learn more about this rapidly developing field.

What you'll learn ✓ Apply your skills to recognize real-world generative AI uses and identify generative AI models and tools for text, code, image, audio, and video. ✓ Explain generative AI prompt engineering concepts, examples, and common tools and learn techniques needed to create effective, impactful prompts. ✓ Implement data engineering processes such as data warehouse schema design, data generation, augmentation and anonymization using generative AI tools ✓ Evaluate real-world case studies showcasing the successful application of generative AI for ETL and data repositories

What you'll learn Identify real-world generative AI uses and describe popular generative AI models and tools for text, code, image, audio, and video Explain generative AI prompt engineering concepts, examples, and common tools and learn techniques needed to create effective, impactful prompts Learn about tools and techniques to generate code snippets, scripts, test cases, and applications using generative AI models Develop innovative software engineering solutions using AI-powered tools and LLMs

What you'll learn ✓ Apply skills to recognize real-world generative AI uses and identify popular generative AI models and tools for text, code, image, audio, and video ✓ Understand generative AI prompt engineering concepts, examples, and common tools and learn techniques to create effective, impactful prompts ✓ Learn how to identify appropriate generative AI tools for data science use cases ✓ Apply generative AI prompt techniques to generate and augment datasets and to develop and refine machine learning models

What you'll learn ✓ Explain the fundamental concepts, capabilities, models, tools, applications, and platforms of generative AI foundation models. ✓ Apply powerful prompt engineering techniques to write effective prompts and generate desired outcomes from AI models. ✓ Discuss the limitations of generative AI and explain the ethical concerns and considerations for the responsible use of generative AI. ✓ Recognize the ability of generative AI to enhance your career and help implement improvements at your workplace.

What you'll learn ✓ Gain foundational knowledge, practical skills, and a functional understanding of how generative AI works ✓ Dive into the latest research on Gen AI to understand how companies are creating value with cutting-edge technology ✓ Instruction from expert AWS AI practitioners who actively build and deploy AI in business use-cases today