Unlocking the Potential of Machine Learning: An Exploration of Complex Problem-Solving with AI
- Machine learning is a subset of AI that can be used to solve complex problems
- Machine Learning uses algorithms to find and learn patterns in input data
- Libraries such as Numpy and Pandas are popular for ML projects
- Data needs to be cleaned before being fed into the model
- Once trained, the model can make predictions with a certain level of accuracy
- Evaluation of predictions is necessary to optimize accuracy.
Unlocking Data Science With Anaconda and Jupyter
- Anaconda is used to install Jupyter, an environment for writing code
- Matplotlib is a two-dimensional plotting library
- Scikit-learn is a popular machine learning library that provides common algorithms like decision trees and neural networks
- Visualizing data in a terminal window is difficult so Jupyter makes it easy to inspect data
- Installing Anaconda will install Jupyter as well as other popular data science libraries like numpy, pandas, etc.
- Microsoft VSCode can also be installed with Anaconda by default
- To create a jupyter notebook type ‘jupyter notebook’ in the terminal window.
Unlocking the Benefits of Jupyter Notebook for Data Analysis
- Jupyter notebook is an interactive tool for coding
- It provides an easy way to visualize data
- Df.describe returns basic information about each column of a dataset such as the count, mean, standard deviation and min value
- Df.values returns a two-dimensional array
- Keyboard shortcuts in Jupyter such as ‘a’ or ‘b’ can be used to insert empty cells above or below the active cell respectively
- The command mode and edit mode can be used to execute code or write code respectively.
Exploring the Capabilities of Machine Learning Models
- Importing and preparing data
- Selecting a machine learning algorithm to build a model
- Training the model and asking it to make predictions
- Evaluating the accuracy of the algorithm.
Assessing the Accuracy of a Decision Tree Model With Data Sets
- In this lecture, we split a data set into two sets (input and output)
- We build a decision tree model and train it with the input set
- Then we use the model to make predictions using new input sets
- To measure the accuracy of the model, we split our data into training and testing sets and compare our prediction results against actual values in the test set.
Discovering the Benefits of Sklearn Model Selection and Model Persistence for Accurate Predictions
- Train Test Split is a function in the sklearn model selection module which allows us to easily divide our dataset into two sets
- One for training and one for testing.
- We can also specify the size of our test set using the keyword argument ‘test_size’.
- The accuracy score is calculated by using the function ‘accuracy_score’ from sklearn.metrics, which takes two arguments ‘y_test’ (expected values) and ‘predictions’ (actual values).
- Model Persistence stores our trained model in a file using joblib.dump, so that we don’t have to retrain it every time we want to make predictions.
A Crash Course on Machine Learning: Understanding Decision Trees
- This lecture demonstrates how to persist and load a model in order to make predictions
- Exporting the model in a visual format is also demonstrated
- The example uses decision trees as an example, which is the easiest machine learning algorithm to understand
- Parameters for displaying the decision tree are set, such as class names, feature names, rounded corners and filling with colors.
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