Python for Machine Learning

Master the skills to use machine learning in your day-to-day work with this Python course. Create algorithms to predict classes, continuous values, and more.

Course length

150 Lectures

18 Hours

Student rating

5.00 Out Of 44 Students

How to Become a Data Scientist

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written by Pierian Training founder Jose Portilla!

What You’ll Learn

  • You will learn how to use data science and machine learning with Python.
  • Understand Machine Learning from top to bottom.
  • Learn NumPy for numerical processing with Python.
  • Conduct feature engineering on real world case studies.
  • Learn Pandas for data manipulation with Python.
  • Create supervised machine learning algorithms to predict classes.
  • Create regression machine learning algorithms for predicting continuous values.
  • Construct a modern portfolio of machine learning resume projects.
  • Learn how to use Scikit-learn to apply powerful machine learning algorithms.
  • Get set-up quickly with the Anaconda data science stack environment.
  • Understand the full product workflow for the machine learning lifecycle.
  • Explore how to deploy your machine learning models as interactive APIs.

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Course Content


This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Machine Learning. With a focus on SciKit Learn, you’ll learn all aspects of Machine Learning ranging from a variety of regression types (Linear / Lasso /Ridge), Elastic Net, K Nearest Neighbors and Means Clustering, Hierarchal Clustering, DBSCAN, PCA, and Model Deployment.

Machine Learning concepts

  • Supervised vs Unsupervised Learning
    • Supervised learning: This type of machine learning uses labeled data to train a model. The model learns to predict the output for a given input.
    • Unsupervised learning: This type of machine learning uses unlabeled data to train a model. The model learns to find patterns in the data.
  • Types of Machine Learning – Classification vs Regression
    • Classification: This type of machine learning predicts a categorical output. For example, a classification model could predict whether a customer will churn or not.
    • Regression: This type of machine learning predicts a continuous output. For example, a regression model could predict the price of a house.
  • Evaluation
    • There are many different metrics for evaluating machine learning models. Some common metrics include accuracy, precision, recall, and F1 score.

Machine Learning Methods – All in Theory and Practice

  • Linear Regression
    • Linear regression is a simple but powerful machine learning method. It can be used to predict a continuous output.
    • Libraries: NumPy, SciPy, Scikit-learn
    • Use cases: Predicting house prices, predicting customer churn, predicting sales
  • Logistic Regression
    • Logistic regression is a type of classification machine learning method. It can be used to predict a binary output.
    • Libraries: NumPy, SciPy, Scikit-learn
    • Use cases: Predicting whether a customer will churn, predicting whether a patient has cancer
  • K Nearest Neighbors
    • K nearest neighbors is a simple but effective machine learning method. It predicts the output for a given input by finding the k most similar inputs and averaging their outputs.
    • Libraries: Scikit-learn
    • Use cases: Predicting customer behavior, recommending products, classifying images
  • Support Vector Machine
    • Support vector machine is a powerful machine learning method. It can be used for both classification and regression tasks.
    • Libraries: Scikit-learn
    • Use cases: Classifying images, predicting customer churn, predicting sales
  • Decision Trees
    • Decision trees are a simple but effective machine learning method. They predict the output for a given input by following a series of if-then-else rules.
    • Libraries: Scikit-learn
    • Use cases: Predicting customer behavior, recommending products, classifying images

Unsupervised Learning Methods

  • Clustering
    • Clustering is a type of unsupervised machine learning method. It groups similar data points together.
    • Libraries: Scikit-learn
    • Use cases: Customer segmentation, market basket analysis, image clustering
  • Anomaly Detection
    • Anomaly detection is a type of unsupervised machine learning method. It identifies data points that are significantly different from the rest of the data.
    • Libraries: Scikit-learn
    • Use cases: Fraud detection, intrusion detection, quality control

Feature Engineering and Data Preparation

  • Feature engineering is the process of transforming raw data into features that are useful for machine learning models.
  • Data preparation is the process of cleaning and formatting data so that it can be used by machine learning models.
  • Libraries: NumPy, SciPy, Pandas

Experienced Python developers looking to understand a wide variety of machine learning algorithms, including supervised and unsupervised learning algorithms.


Course Description

This machine learning course is designed for experienced python developers who want to learn the theory and application of a large variety of machine learning methods. Starting from simple linear regression, this training takes students through a tour of the most popular machine learning models used in practice.  The course focuses on teaching students how to unlock the power of the Scikit-Learn Python library. Students will learn how to choose a model, train the model on data, and evaluate and tune the model for deployment.

What Students Are Saying

This has been the course that has laid the foundation in my career as a data scientist.

Linda Mukami

Best instructor very knowledgeable and teaching style is very impressive. This is my third course with him and every course is great very helpful. Thanks

Ahsan Parvez

Provides knowledge and mathematical background on the main ML models. You'll learn very helpful coding tips as well as good practice for each model so definitely an invaluable course

Fraggle Baggle

The course is all inclusive. Almost everything you need to become data scientist at that cheap price.

Fatai Jimoh

The course is very easy to follow and very well paced.

Augmented Startups

A good introduction to the fundamentals of machine learning and its application.The data analytics and visualization tools are proving to be very useful for my class projects.

Shine Bedi

Taking it slow and steady so far, explaining all the tiny bits of Jupyter Notebook nicely

Rachit Toshniwal

Yes, definitely an excellent course. Keep up the good work!

Linmu Liu

Exceptional Course. Cannot recommend this course enough. Just the exercises and walkthroughs alone are enough to enroll. One of my favorite data science courses I've ever enrolled in. Jose is an exceptional teacher. Would recommend to anyone seriously interested in learning about Data Science from the ground up in Python

Alexander Mason

A very useful course for those who know the theory of statistics and want to learn how to apply it in Python. All knowledge is given by examples. Very interesting and useful practical exercises.

Kate Chernyavskaya
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