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Python Machine Learning
4 students

Python Machine Learning

Learn to create Machine Learning Algorithms in Python
Created byDATAhill Solutions Srinivas Reddy
Last updated 1/2020
English

What you'll learn

  • Master Machine Learning on Python
  • Make accurate predictions
  • Make robust Machine Learning models
  • Use Machine Learning for personal purpose
  • Handle advanced techniques like Dimensionality Reduction
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem
  • Have a great intuition of many Machine Learning models
  • Make powerful analysis
  • Create strong added value to your business
  • Know which Machine Learning model to choose for each type of problem
  • Great Learning Skills

Course content

1 section • 12 lectures • 5h 42m total length

  • Introduction1:04

    Introduce mission learning, its relation to data science, and the types of mission learning, plus how to load data, train, evaluate, and improve the model performance for real-time problems.

  • 1. Machine Learning Introduction25:06
  • 2. Data Science Introduction30:42
  • 3. Machine Learning, Types of ML with Algorithms1:04:53
  • 4. How Machine Solve Real Time Problems43:33
  • 5. SciKit Learn & Machine Learning Map40:27
  • 6. Collecting & Preparing the Data15:09
  • 7. Train the Machine to Develop a Model33:28
  • 8. Using SciKitLearn Algorithms27:29
  • 9. Evaluate the Performance of the Model38:34

    Evaluate model performance in Python machine learning by applying cross-validation, accuracy metrics, and confusion matrices across classifiers such as logistic regression, linear discriminant analysis, k-nearest neighbors, and support vector machines.

  • 10. Improve the Performance of the Model19:56

    Explore classification performance using confusion matrices, evaluating true/false positives and negatives, and metrics like precision, recall, F1, and accuracy across algorithms on cancer datasets.

  • Summary1:50

    Learn the end-to-end machine learning workflow from data collection and processing to evaluating model performance and iterating improvements, with examples touching weather and global warming.

Requirements

  • Just some high school mathematics level.
  • No Programming skills needed

Description

Are you interested in the field of Machine Learning? Then this course is perfect for you!

Designed by two professional Data Scientists, this course aims to demystify complex theory, algorithms, and coding libraries, presenting them in a simple and understandable way. Our goal is to share our knowledge and help you grasp the essentials of Machine Learning, making it accessible and engaging.

We will guide you step-by-step into the fascinating World of Machine Learning. Each tutorial is crafted to help you develop new skills and deepen your understanding of this challenging yet highly rewarding sub-field of Data Science. As you progress, you'll gain confidence and expertise, preparing you for real-world applications.

This course is not only educational but also fun and exciting. We dive deep into Machine Learning while ensuring the content is approachable and enjoyable. The course is structured in a logical sequence to facilitate effective learning:

  1. Machine Learning Introduction: Get an overview of Machine Learning, its significance, and its applications.

  2. Data Science Introduction: Understand the broader field of Data Science and how Machine Learning fits into it.

  3. Machine Learning Types and Algorithms: Explore various types of Machine Learning and their respective algorithms.

  4. Solving Real-Time Problems with Machine Learning: Learn how to apply Machine Learning to solve real-world problems.

  5. SciKit Learn & Machine Learning Map: Familiarize yourself with the SciKit Learn library and the Machine Learning workflow.

  6. Collecting & Preparing the Data: Discover the methods for gathering and preparing data for analysis.

  7. Training the Machine to Develop a Model: Understand the process of training Machine Learning models.

  8. Using SciKitLearn Algorithms: Learn to implement Machine Learning algorithms using SciKit Learn.

  9. Evaluating the Performance of the Model: Gain skills in assessing the performance of your models.

  10. Improving the Performance of the Model: Learn techniques to enhance your model’s performance.

Join us in this comprehensive course to embark on your Machine Learning journey. Gain the knowledge and skills needed to excel in this cutting-edge field, and unlock new opportunities in the world of Data Science.

Who this course is for:

  • Beginner Python developers curious about Data Science
  • Beginner to advanced level

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