Machine Learning Using Python.pptx Machine Learning Using PythonMachine Learning Using Python

Machine Learning Using Python

1. M achine L earning U sing P ython

2. Concept in Brief

3. Machine learning using Python involves using various libraries and tools to build, train, and deploy machine learning models. Here’s a brief overview of the key steps and tools commonly used: Key Steps in Machine Learning Data Collection and Preparation : Collect data from various sources. Clean and preprocess the data (handling missing values, encoding categorical variables, etc.). Split the data into training and testing sets. Exploratory Data Analysis (EDA) : Use statistical methods and visualization tools to understand the data. Identify patterns, correlations, and insights.

4. 3.Model Selection : Choose appropriate machine learning algorithms based on the problem (classification, regression, clustering, etc.). Popular algorithms include Linear Regression, Decision Trees, Random Forests, Support Vector Machines, K-Means Clustering, etc. 4. Model Training : Train the model using the training data. Adjust hyperparameters to optimize performance. 5. Model Evaluation : Evaluate the model using the testing set. Use metrics like accuracy, precision, recall, F1-score, ROC-AUC for classification tasks, and RMSE, MAE for regression tasks. 6. Model Deployment : Deploy the trained model to a production environment. Monitor the model’s performance over time and retrain if necessary.

5. Key Python Libraries NumPy : Fundamental package for scientific computing with Python. Provides support for arrays, matrices, and many mathematical functions. Pandas : Data manipulation and analysis library. Provides data structures like DataFrame for handling tabular data. Matplotlib and Seaborn : Libraries for data visualization. Matplotlib is the core plotting library, while Seaborn is built on top of it and provides a high-level interface for drawing attractive statistical graphics.

6. 4. Scikit-learn : A comprehensive library for machine learning. Provides simple and efficient tools for data mining and data analysis. 5. TensorFlow and Keras : Libraries for deep learning. TensorFlow is an open-source platform for machine learning, and Keras is an API designed for human beings, not machines, which makes it easy to build and train neural networks. 6. PyTorch : An open-source machine learning library based on the Torch library. Used for applications such as computer vision and natural language processing.