Machine Learning, Data Science and Generative AI with Python

Packt via Coursera Specialization

Go to class Write review

Overview

Coursera Flash Sale

40% Off Coursera Plus for 3 Months!

Grab it

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this comprehensive course, learners will explore machine learning, data science, and generative AI using Python. You will gain a solid understanding of machine learning principles, including supervised and unsupervised learning techniques, and apply algorithms such as linear regression, decision trees, and support vector machines. The course delves into advanced AI concepts like GANs, Variational Autoencoders, and Transformer architecture, which powers models like GPT. You’ll develop, train, and fine-tune models to solve real-world problems such as recommendation systems and sentiment analysis. By the end of the course, you’ll have hands-on experience with tools like TensorFlow, Keras, and Apache Spark to handle large-scale data challenges. You’ll also learn to deploy models in real-time systems while considering AI ethics. This course is ideal for those eager to explore data science, machine learning, and AI. Whether you're a data scientist, developer, or enthusiast, you'll gain the practical skills needed to thrive in the rapidly evolving AI field.

Syllabus

Course 1: Foundations of Data Science and Machine Learning with Python
Course 2: Advanced Machine Learning, Big Data, and Deep Learning
Course 3: Generative AI, LLMs, and Advanced Applications with Python

Courses

0 reviews
9 hours 21 minutes
View details

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Delve into the world of generative AI and large language models (LLMs) with hands-on applications using Python. You'll explore the power of Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs) to create synthetic data, including images and music. Alongside, you'll get to grips with Transformers and self-attention mechanisms, which are foundational to models like GPT and ChatGPT, unlocking advanced AI applications. Learn the intricacies of GPT architecture, including tokenization and fine-tuning, and apply these concepts using tools like Hugging Face and Google Colab. The course also covers cutting-edge topics such as Retrieval Augmented Generation (RAG) and advanced LLM agents. Through interactive activities, you’ll create powerful AI applications like chatbots and personalized systems. This course is designed for learners aiming to advance their knowledge of AI, machine learning, and Python, with a focus on generative models and LLMs. If you want to build your own AI-driven applications and deepen your understanding of state-of-the-art AI technologies, this course is for you.
0 reviews
10 hours 45 minutes
View details

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Dive deep into advanced machine learning techniques, including data mining, dimensionality reduction, reinforcement learning, and deep learning. You'll gain hands-on experience with tools like K-Nearest Neighbors, Principal Component Analysis, and Apache Spark while working with real-world datasets. The course emphasizes key machine learning concepts such as model evaluation, cross-validation, and handling unbalanced data. As you progress, you'll explore advanced neural networks like Convolutional and Recurrent Neural Networks, with practical applications such as sentiment analysis and handwriting recognition. Learn how to deploy models, use transfer learning, and understand the ethics behind machine learning and deep learning. This course is ideal for anyone with a basic understanding of machine learning who wants to advance their skills with real-world applications and big data tools. Gain the expertise needed to work with cutting-edge technologies in machine learning and deep learning. Ideal for data scientists, machine learning engineers, and anyone with a keen interest in AI and its real-world applications.
0 reviews
8 hours 52 minutes
View details

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Embark on a hands-on learning journey through data science and machine learning with Python. In this course, you will gain a deep understanding of core data science concepts and machine learning techniques, while mastering essential Python libraries. You will build the skills necessary to analyze datasets, visualize results, and apply machine learning models to real-world data. The course begins with an introduction to data handling, including installing necessary tools like Anaconda, followed by a Python crash course. You will then explore foundational statistical concepts and their application using Python. Next, we delve into building predictive models, from linear regression to polynomial and multiple regression, and understanding their real-world applications. As you progress, you'll dive into machine learning techniques, such as supervised and unsupervised learning, including decision trees, support vector machines, and ensemble learning methods like XGBoost. Finally, you’ll learn how to build recommender systems, helping you understand the intricacies of collaborative filtering and how to improve your model’s predictions. This course is ideal for individuals eager to break into the world of data science and machine learning, as well as those wishing to enhance their Python skills for professional growth. The course assumes basic familiarity with programming concepts, making it perfect for beginners in the field.