Applied Supervised & Unsupervised Learning with Python
PYTHON
Time:
5 Days
VILT
£270.00
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DOWNLOAD NOWWhat you’ll Learn
- Understand the concept of supervised learning and its applications
- Implement common supervised learning algorithms using machine learning Python libraries
- Validate models using the k-fold technique
- Build your models with decision trees to get results effortlessly
- Use ensemble modeling techniques to improve the performance of your model
- Apply a variety of metrics to compare machine learning models
- Understand the basics and importance of clustering
- Build k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packages
- Explore dimensionality reduction and its applications
- Use scikit-learn (sklearn) to implement and analyse principal component analysis (PCA)on the Iris dataset
- Employ Keras to build autoencoder models for the CIFAR-10 dataset
- Apply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction data
Description
Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques to your data science projects using Python. You’ll explore Jupyter notebooks, a technology widely used in academic and commercial circles with support for running inline code. With the help of fun examples, you’ll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you’ll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modelling, validation, and error metrics. You’ll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. Applied Unsupervised Learning with Python guides you on the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The course begins by explaining how basic clustering works to find similar data points in a set. Once you are well versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it.
Requirements
Some familiarity with Python programming along with basic knowledge of mathematical concepts including exponents, square roots, means, and medians will be beneficial.
Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.
Modules in this Course
- Supervised Machine Learning
- Jupyter Notebooks
- Pandas
- Data Quality Considerations
- Summary Statistics and Central Values
- Missing Values
- Distribution of Values
- Relationships within the Data
- Regression and Classification Problems
- Linear Regression
- Multiple Linear Regression
- Autoregression Models
- Linear Regression as a Classifier
- Logistic Regression
- Classification Using K-Nearest Neighbors
- Classification Using Decision Trees
- Overfitting and Underfitting
- Bagging
- Boosting
- Evaluation Metrics
- Splitting the Dataset
- Performance Improvement Tactics
- Introduction
- Unsupervised Learning versus Supervised Learning
- Clustering
- Introduction to k-means Clustering
- Clustering Refresher
- The Organization of Hierarchy
- Introduction to Hierarchical Clustering
- Linkage
- Agglomerative versus Divisive Clustering
- k-means versus Hierarchical Clustering
- Introduction to DBSCAN
- DBSCAN Versus k-means and Hierarchical Clustering
- Overview of Dimensionality Reduction Techniques PCA
- Fundamentals of Artificial Neural Networks
- Autoencoders
- Stochastic Neighbour Embedding (SNE)
- Interpreting t-SNE Plots
- Cleaning Text Data
- Latent Dirichlet Allocation
- Non-Negative Matrix Factorization
- Market Basket Analysis
- Characteristics of Transaction Data
- Kernel Density Estimation
- Hotspot Analysis
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