This Machine Learning with Python course dives into the basics of machine learning using Python, an approachable and well-known programming language. You’ll learn about supervised vs. unsupervised learning, look into how statistical modelling relates to machine learning, and do a comparison of each.
Machine Learning with Python
Machine learning with Python refers to the practice of using the Python programming language to implement, develop, and apply machine learning algorithms and models. Machine learning is a subfield of artificial intelligence that focuses on creating systems that can learn from data and make predictions or decisions without being explicitly programmed. Python is a popular choice for machine learning because it offers a wide range of libraries and tools that simplify the implementation of machine learning algorithms.
Here is a step-by-step explanation of how machine learning is typically done with Python:
“Data Collection”: The first step in any machine learning project is collecting and preparing the data. This data can be in the form of structured data (e.g., data in tables) or unstructured data (e.g., text or images). Python provides libraries and tools to import, clean, and pre-process data, making it ready for analysis.
“Data Pre-processing”: Data often requires cleaning and pre-processing to handle missing values, outliers, and to standardize or normalize the data. Python libraries such as NumPy, Pandas, and scikit-learn are commonly used for these tasks.
“Selecting a Machine Learning Algorithm”: Depending on the nature of the problem, you’ll choose an appropriate machine learning algorithm. Python offers a variety of libraries with pre-implemented algorithms, including scikit-learn, TensorFlow, and PyTorch. These libraries cover a wide range of machine learning techniques, from traditional statistical methods to deep learning.
“Training the Model”: To train a machine learning model, you use a portion of your data (the training data) to teach the model patterns and relationships within the data. The Python code will involve importing the chosen machine learning library, specifying the model, and fitting it to the training data.
“Evaluation and Validation”: After training, you need to assess how well your model performs. Common metrics for evaluation include accuracy, precision, recall, F1 score, and others depending on the problem (classification, regression, etc.). Python libraries provide functions to calculate these metrics and tools for visualization.
“Hyperparameter Tuning”: Many machine learning models have hyper parameters that need to be optimized for best performance. You might use techniques like grid search or random search to find the best hyper parameters. Libraries like scikit-learn have built-in functions for hyper parameter tuning.
“Testing and Deployment”: Once you are satisfied with your model’s performance, you can use it to make predictions on new, unseen data. Python libraries allow you to deploy your model in various ways, such as integrating it into a web application or running it as a service.
“Monitoring and Maintenance”: After deployment, it’s important to monitor your model’s performance over time and retrain it as new data becomes available. Python can help automate this process.
“Iterative Improvement”: Machine learning is often an iterative process. You might need to go back to earlier steps to refine the data, choose a different algorithm, or adjust hyper parameters to improve your model’s performance.
“Documentation and Reporting”: Proper documentation of your work is essential. Jupyter Notebooks are commonly used in the Python ecosystem for creating interactive reports and documentation of machine learning projects.
Python is well-suited for machine learning because of its readability, extensive libraries, and a large and active community of developers. Common Python libraries used in machine learning projects include scikit-learn, TensorFlow, Keras, PyTorch, Pandas, NumPy, and Matplotlib. These libraries provide tools for data manipulation, model development, visualization, and more, making Python a versatile and powerful language for machine learning applications. History
