## Master the Essential Python Skills for Machine Learning
Start your journey to becoming a machine learning scientist with this comprehensive Python Track. Gain hands-on experience with supervised, unsupervised, and deep learning techniques as you work with real-world datasets. By the end of this Track, you'll have the confidence and skills to tackle complex machine learning problems and build powerful predictive models.
## From Python Basics to Advanced Machine Learning
Whether you're new to Python or an experienced programmer, this Track has you covered. You'll start by learning the fundamentals of Python programming and quickly progress to advanced machine learning concepts. The carefully curated curriculum includes:
* Supervised learning with scikit-learn
* Unsupervised learning techniques like clustering and dimensionality reduction
* Linear classifiers and tree-based models
* Gradient boosting with XGBoost
* Feature engineering and preprocessing for machine learning
* Time series analysis and forecasting
* Natural language processing with spaCy
* Deep learning with PyTorch
* Distributed machine learning with PySpark
## Hands-on Learning with Real-World Projects
Apply your skills to practical projects that mirror the challenges faced by machine learning scientists in industry. You'll work with diverse datasets, ranging from customer behavior to image and text data, to solve real-world problems. Through predictive modeling for agriculture, clustering Antarctic penguin species, and forecasting movie rental durations, you'll gain hands-on experience tackling complex machine learning tasks. Additionally, you'll explore strategies for excelling in Kaggle competitions, refining your ability to develop high-performing models. These projects will help you build a compelling portfolio to showcase your machine learning expertise to potential employers.
## Become Job-Ready with In-Demand Skills
Machine learning is one of the most sought-after skills in today's job market. By completing this Track, you'll be well-prepared to:
* Apply for machine learning scientist positions across industries
* Collaborate with data science teams to solve complex problems
* Participate in Kaggle competitions and hackathons
* Pursue further specialization in areas like NLP, computer vision, or big data
## Why Python for Machine Learning?
Python has become the language of choice for machine learning due to its simplicity, versatility, and extensive ecosystem of powerful libraries. With tools like scikit-learn, PyTorch, and PySpark, Python enables you to implement machine learning algorithms efficiently and scale them to handle large datasets. Mastering Python for machine learning will open up a world of opportunities in this rapidly growing field.
## Unlock Your Potential as a Machine Learning Scientist
Ready to take your first step towards a rewarding career in machine learning? Enroll in the Machine Learning Scientist in Python Track today and gain the skills and confidence to tackle real-world machine learning challenges. With expert instruction, hands-on projects, and a supportive learning community, you'll be well on your way to becoming a machine learning scientist.
Overview
Syllabus
- Supervised Learning with scikit-learn
- Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!
- Predictive Modeling for Agriculture
- Unsupervised Learning in Python
- Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
- Clustering Antarctic Penguin Species
- Linear Classifiers in Python
- In this course you will learn the details of linear classifiers like logistic regression and SVM.
- Machine Learning with Tree-Based Models in Python
- In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
- Predicting Movie Rental Durations
- Extreme Gradient Boosting with XGBoost
- Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
- Cluster Analysis in Python
- In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
- Dimensionality Reduction in Python
- Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
- Preprocessing for Machine Learning in Python
- Learn how to clean and prepare your data for machine learning!
- Machine Learning for Time Series Data in Python
- This course focuses on feature engineering and machine learning for time series data.
- Feature Engineering for Machine Learning in Python
- Create new features to improve the performance of your Machine Learning models.
- Model Validation in Python
- Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
- Hyperparameter Tuning in Python
- Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
- Introduction to Natural Language Processing in Python
- Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
- Natural Language Processing with spaCy
- Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
- Feature Engineering for NLP in Python
- Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
- Introduction to Deep Learning with PyTorch
- Learn how to build your first neural network, adjust hyperparameters, and tackle classification and regression problems in PyTorch.
- Intermediate Deep Learning with PyTorch
- Learn about fundamental deep learning architectures such as CNNs, RNNs, LSTMs, and GRUs for modeling image and sequential data.
- Image Processing in Python
- Learn to process, transform, and manipulate images at your will.
- Introduction to PySpark
- Master PySpark to handle big data with ease—learn to process, query, and optimize massive datasets for powerful analytics!
- Machine Learning with PySpark
- Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
- Winning a Kaggle Competition in Python
- Learn how to approach and win competitions on Kaggle.
Taught by
Benjamin Wilson, Katharine Jarmul, Sergey Fogelson, Nick Solomon, Lore Dirick, Chris Holdgraf, Mike Gelbart, Elie Kawerk, James Chapman, Jeroen Boeye, Robert O'Callaghan, Shaumik Daityari, Andrew Collier, Kasey Jones, Alex Scriven, Rounak Banik, Yauhen Babakhin, Rebeca Gonzalez, George Boorman, Maham Khan, Thomas Hossler, Azadeh Mobasher, and Michał Oleszak
