Track Description

Machine Learning for Everyone

Learn machine learning even if you have no coding experience with this collection of tutorials covering supervised learning, unsupervised learning, and deep learning.

Prerequisites

There are no prerequisites for this track

Course
1
Understanding Machine Learning
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An introduction to machine learning with no coding involved.
An introduction to machine learning with no coding involved.
Course
2
Introduction to Python
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Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
Course
3
Intermediate Python
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Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Course
4
Introduction to Functions in Python
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Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
Course
5
Python Toolbox
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Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Course
6
Statistical Thinking in Python (Part 1)
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Build the foundation you need to think statistically and to speak the language of your data.
Build the foundation you need to think statistically and to speak the language of your data.
Course
8
Unsupervised Learning in Python
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Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Course
9
Linear Classifiers in Python
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In this course you will learn the details of linear classifiers like logistic regression and SVM.
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Course
10
Machine Learning with Tree-Based Models in Python
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In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Course
11
Extreme Gradient Boosting with XGBoost
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Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Course
12
Cluster Analysis in Python
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In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Course
13
Dimensionality Reduction in Python
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Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Course
14
Preprocessing for Machine Learning in Python
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Learn how to clean and prepare your data for machine learning!
Learn how to clean and prepare your data for machine learning!
Course
15
Machine Learning for Time Series Data in Python
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This course focuses on feature engineering and machine learning for time series data.
This course focuses on feature engineering and machine learning for time series data.
Course
16
Feature Engineering for Machine Learning in Python
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Create new features to improve the performance of your Machine Learning models.
Create new features to improve the performance of your Machine Learning models.
Course
17
Model Validation in Python
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Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Course
19
Feature Engineering for NLP in Python
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Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Course
20
Introduction to TensorFlow in Python
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Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Course
21
Introduction to Deep Learning in Python
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Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 in Python.
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 in Python.
Course
22
Introduction to Deep Learning with Keras
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Learn to start developing deep learning models with Keras.
Learn to start developing deep learning models with Keras.
Course
23
Advanced Deep Learning with Keras
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Learn how to develop deep learning models with Keras.
Learn how to develop deep learning models with Keras.
Course
24
Image Processing in Python
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Learn to process, transform, and manipulate images at your will.
Learn to process, transform, and manipulate images at your will.
Course
25
Image Modeling with Keras
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Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.
Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.
Course
26
Hyperparameter Tuning in Python
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Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
Course
27
Foundations of PySpark
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Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Course
28
Machine Learning with PySpark
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Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Course
29
Winning a Kaggle Competition in Python
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Learn how to approach and win competitions on Kaggle.
Learn how to approach and win competitions on Kaggle.