This is the code repository for The Complete Machine Learning Course with Python [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you. You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more! Inside the course, you'll learn how to: • Set up a Python development environment correctly • Gain complete machine learning toolsets to tackle most real-world problems • Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them. • Combine multiple models with by bagging, boosting or stacking • Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data • Develop in Jupyter (IPython) notebook, Spyder and various IDE • Communicate visually and effectively with Matplotlib and Seaborn • Engineer new features to improve algorithm predictions • Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data • Use SVM for handwriting recognition, and classification problems in general • Use decision trees to predict staff attrition • Apply the association rule to retail shopping datasets • And much more! By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.
- Understand the concept of Block algorithms and how Dask leverages it to load large data.
- Implement various example using Dask Arrays, Bags, and Dask Data frames for efficient parallel computing
- Combine Dask with existing Python packages such as NumPy and Pandas
- See how Dask works under the hood and the various in-built algorithms it has to offer
- Leverage the power of Dask in a distributed setting and explore its various schedulers
- Implement an end-to-end Machine Learning pipeline in a distributed setting using Dask and scikit-learn
- Use Dask Arrays, Bags, and Dask Data frames for parallel and out-of-memory computations
To fully benefit from the coverage included in this course, you will need:
A newbie who wants to learn machine learning algorithm with Python. Anyone who has a deep interest in the practical application of machine learning to real world problems. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms. Any intermediate to advanced EXCEL users who is unable to work with large datasets. Anyone interested to present their findings in a professional and convincing manner. Anyone who wishes to start or transit into a career as a data scientist. Anyone who wants to apply machine learning to their domain.
This course has the following software requirements:
NA