applied machine learning with python
Practical Machine Learning Techniques with Python
applied machine learning with python
Applied Machine Learning with Python refers to the practical implementation of machine learning techniques using the Python programming language. It involves leveraging Python's extensive libraries and frameworks, such asscikit-learn, TensorFlow, and PyTorch, to build, train, and evaluate predictive models on real-world datasets. This discipline focuses on applying algorithms for data preprocessing, feature selection, model training, and performance evaluation, while addressing challenges such as overfitting, model interpretability, and scalability. By integrating theoretical concepts with hands-on coding and experimentation, Applied Machine Learning with Python empowers practitioners to harness data-driven insights and create effective machine learning solutions across various domains, such as finance, healthcare, and marketing.
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1 - Introduction to Machine Learning: Understand the basic concepts and definitions of machine learning, including supervised and unsupervised learning.
2) Python for Data Science: Gain proficiency in Python, focusing on libraries like NumPy and Pandas which are essential for data manipulation and analysis.
3) Data Preprocessing: Learn techniques for cleaning and preprocessing data, including handling missing values, outliers, and categorical data encoding.
4) Exploratory Data Analysis (EDA): Develop skills to visualize and analyze data patterns using libraries like Matplotlib and Seaborn.
5) Machine Learning Algorithms Overview: Familiarize with key machine learning algorithms, including linear regression, decision trees, random forests, and support vector machines.
6) Model Evaluation Techniques: Understand metrics for evaluating model performance, such as accuracy, precision, recall, F1 score, and ROC AUC.
7) Train Test Split and Cross Validation: Learn the importance of splitting data into training and testing sets and using cross validation techniques to avoid overfitting.
8) Feature Engineering: Explore methods to select and create relevant features that can improve model performance.
9) Hyperparameter Tuning: Discover techniques for optimizing machine learning models through hyperparameter tuning, including grid search and randomized search.
10) Introduction to Neural Networks: Get acquainted with the basics of neural networks and deep learning architecture.
11) Using Scikit Learn: Master the Scikit learn library to implement machine learning models efficiently with built in functions.
12) Handling Imbalanced Datasets: Learn strategies for dealing with imbalanced data, including resampling techniques and using different evaluation metrics.
13) Deployment of Machine Learning Models: Understand how to deploy machine learning models into production environments using tools like Flask or FastAPI.
14) Introduction to Natural Language Processing (NLP): Explore fundamental NLP techniques and their applications, including sentiment analysis and text classification.
15) Real World Projects: Engage in hands on projects that apply machine learning techniques to solve real world problems, enhancing learning through practical experience.
16) Ethics in AI and Machine Learning: Discuss ethical considerations in machine learning, including bias, fairness, and the impact of AI on society.
17) Version Control with Git: Learn how to manage code and collaborate using version control systems, particularly Git and GitHub.
18) Capstone Project: Complete a capstone project that showcases your skills and knowledge, allowing you to apply what you’ve learned in a comprehensive manner.
This curriculum provides a thorough foundation in applied machine learning with Python, preparing students for practical applications and further study in the field.
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