February 12, 2022

Machine Learning Course with Python

The Complete Machine Learning Course in Python With brand new sections as well as updated and improved content, you get everything you need to master Machine Learning in one course! The machine learning field is constantly evolving, and we want to make sure students have the most up-to-date information and practices available to them:

Brand new sections include:

  • Foundations of Deep Learning covering topics such as the difference between classical programming and machine learning, differentiate between machine and deep learning, the building blocks of neural networks, descriptions of tensor and tensor operations, categories of machine learning and advanced concepts such as over- and underfitting, regularization, dropout, validation and testing and much more.
  • Computer Vision in the form of Convolutional Neural Networks covering building the layers, understanding filters / kernels, to advanced topics such as transfer learning, and feature extractions.

And the following sections have all been improved and added to:

  • All the codes have been updated to work with Python 3.6 and 3.7
  • The codes have been refactored to work with Google Colab
  • Deep Learning and NLP
  • Binary and multi-class classifications with deep learning

Get the most up to date machine learning information possible, and get it in a single course!

What Will I Learn?

  • Machine Learning Engineers earn on average $166,000 - become an ideal candidate with this course!
  • Solve any problem in your business, job or personal life with powerful Machine Learning models
  • Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
  • Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning etc

Course Content

Introduction to Data Science

Data Extraction, Wrangling, & Visualization

Introduction to Machine Learning with Python

Supervised Learning – I

Dimensionality Reduction

Supervised Learning – II

Unsupervised Learning

Association Rules Mining and Recommendation Systems

Reinforcement Learning

Time Series Analysis

Model Selection and Boosting

In-Class Project