Track
Machine Learning Scientist in Python
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Track Description
Machine Learning Scientist in Python
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.Prerequisites
There are no prerequisites for this trackCourse
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!
Project
Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.
Course
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Project
Arctic Penguin Exploration: Unraveling Clusters in the Icy Domain with K-means Clustering
Course
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Course
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Project
Build a regression model for a DVD rental firm to predict rental duration. Evaluate models to recommend the best one.
Course
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Course
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Course
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Course
Learn how to clean and prepare your data for machine learning!
Course
This course focuses on feature engineering and machine learning for time series data.
Course
Create new features to improve the performance of your Machine Learning models.
Course
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Course
Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
Skill Assessment
Course
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
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Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
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Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
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Learn how to build your first neural network, adjust hyperparameters, and tackle classification and regression problems in PyTorch.
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Learn about fundamental deep learning architectures such as CNNs, RNNs, LSTMs, and GRUs for modeling image and sequential data.
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Learn to process, transform, and manipulate images at your will.
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Master PySpark to handle big data with ease—learn to process, query, and optimize massive datasets for powerful analytics!
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Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Course
Learn how to approach and win competitions on Kaggle.
Complete
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