Machine Learning with Scikit-learn, PyTorch & Hugging Face

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Overview

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Build and deploy real-world machine learning solutions with confidence. The Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate is a hands-on program that teaches through practical, project-based learning using industry tools like Python, scikit-learn, PyTorch, Hugging Face, and LangChain. Progress through the full machine learning lifecycle—learning to build, evaluate, and deploy models across diverse applications. Develop models using supervised and unsupervised learning, enhance performance with feature engineering and ensemble methods, and explore reinforcement learning. Work with time series forecasting, modern NLP techniques, tokenization, and embeddings, advancing into deep learning with neural networks and PyTorch. Explore generative AI, covering large language models (LLMs), transformer architectures, and diffusion models. Apply skills in hands-on labs, guided projects, and a final capstone to solve a real-world machine learning problem, build and deploy a solution, and implement model monitoring. Upon completion, have a portfolio of applied projects that demonstrate your ability to build and deploy machine learning solutions using industry tools. Be equipped to solve real-world data challenges, applying techniques from classical machine learning to deep learning and generative AI. With hands-on experience across the full ML lifecycle, be prepared to contribute to data-driven innovation across a wide range of industries.

Syllabus

Course 1: Foundations of Machine Learning
Course 2: Advanced Machine Learning Techniques
Course 3: Deep Learning with PyTorch
Course 4: Generative AI and Large Language Models
Course 5: Building a Machine Learning Solution

Courses

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1 day 9 hours 42 minutes
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Welcome to Advanced Machine Learning Techniques, where you'll dive deep into sophisticated approaches that power modern AI applications. We'll explore five key areas of advanced ML: ensemble methods for combining models, dimensionality reduction techniques for handling complex data, natural language processing for text analysis, reinforcement learning for decision-making systems, and automated machine learning for optimization. You'll work hands-on with industry-standard tools including Scikit-learn, XGBoost, NLTK, PyTorch, and MLflow, learning how to implement and optimize advanced algorithms in real-world scenarios. By the end of this course, you'll be able to: -Implement ensemble methods including bagging, boosting, and stacking to enhance model performance -Apply dimensionality reduction techniques like PCA, t-SNE, and UMAP for data visualization and feature extraction -Process and analyze text data using modern NLP techniques and transformer models -Design and train reinforcement learning agents for autonomous decision-making -Optimize machine learning workflows using AutoML tools and experiment tracking Through practical exercises and a comprehensive capstone project, you'll develop the advanced skills needed to tackle complex machine learning challenges in your professional work.
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20 hours 13 minutes
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Welcome to Building a Machine Learning Solution, where you'll journey through the complete lifecycle of a machine learning project. This capstone course covers critical steps from problem definition to deployment and maintenance. You'll learn to define clear problem statements, collect and preprocess data, perform exploratory data analysis (EDA), and engineer features to enhance model performance. The course guides you in selecting and implementing appropriate models, comparing classical machine learning, deep learning, and generative AI approaches. Emphasizing real-world considerations, you'll address scalability, interpretability, and ethical implications. You'll gain hands-on experience with tools like scikit-learn, TensorFlow, PyTorch, and more, ensuring you can deploy and monitor models effectively. By the end of this course, you'll be equipped to build end-to-end ML solutions that transform data into actionable insights, making informed decisions at each stage of development.
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1 day 2 hours 18 minutes
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This course offers a comprehensive and practical introduction to deep learning using PyTorch, a leading open-source framework. Learners will develop a solid understanding of foundational concepts such as neural networks, activation functions, forward and backward propagation, and optimization algorithms. Through a structured progression, the course covers essential architectures including perceptrons, multi-layer networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) models, and Transformers. Learners will apply these models to real-world tasks in computer vision and natural language processing, gaining experience in training, evaluating, and optimizing deep learning systems. Advanced topics such as transfer learning, regularization, batch normalization, mixed precision training, attention mechanisms, and model pruning are also explored to help learners build models that are both accurate and efficient. By the end of the course, participants will be equipped with the skills and tools necessary to design and implement deep learning solutions in PyTorch for a wide range of practical applications.
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1 day 6 hours 19 minutes
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Welcome to the Foundations of Machine Learning, your practical guide to fundamental techniques powering data-driven solutions. Master key ML domains—supervised learning (prediction), unsupervised learning (pattern discovery), data preprocessing & feature engineering, and time series forecasting—using Pandas, Scikit-learn, Statsmodels, and Prophet to tackle real-world challenges. By the end of this course, you'll be able to: - Implement and evaluate key supervised models (e.g., regression, classification, Tree-based models & SVMs) for prediction. - Apply unsupervised methods (e.g., K-Means, Isolation Forest) for segmentation and anomaly detection. - Perform robust data preprocessing: handle missing data, encode categoricals, scale features, and apply dimensionality reduction (PCA). - Build and analyze time series forecasts with ARIMA, Exponential Smoothing, Holt-Winters and Prophet. Through hands-on exercises and a capstone customer purchase prediction project, you'll develop versatile skills to confidently address common machine learning challenges.
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1 day 3 hours 16 minutes
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Welcome to the world of Generative AI and Large Language Models (LLMs)—where technology mirrors human creativity and intelligence. This course is designed to provide you with a comprehensive understanding of generative models, including their evolution, applications, and the underlying architectures that make them possible. Throughout the modules, you'll explore various generative techniques such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), diffusion models, and multimodal AI. You'll also gain hands-on experience with tools like OpenAI's GPT, Hugging Face, Streamlit, and MLflow, ensuring you can deploy and fine-tune models for real-world applications.