Python for Data Science and Machine Learning Workshop by Tonex
This intensive workshop by Tonex is designed to equip professionals with the essential Python skills required for Data Science and Machine Learning applications. Participants will gain hands-on experience and practical insights into leveraging Python’s powerful libraries for data analysis, visualization, and machine learning.
Tonex’s “Python for Data Science and Machine Learning Workshop” is a dynamic, hands-on training program catering to professionals seeking expertise in Python for data-driven decision-making. This intensive course covers fundamental Python skills, exploring key libraries like NumPy and Pandas for data manipulation, Matplotlib for visualization, and Scikit-Learn for machine learning applications.
Participants delve into deep learning with TensorFlow and Keras, honing model evaluation and optimization techniques. Geared towards data analysts, scientists, and developers, this workshop imparts practical insights, ensuring participants leave with a solid foundation to apply Python to real-world data science and machine learning challenges.
Learning Objectives:
- Master Python fundamentals for data manipulation and analysis.
- Explore popular Python libraries such as NumPy, Pandas, and Matplotlib.
- Build a solid foundation in data preprocessing and cleaning techniques.
- Understand the principles of machine learning and its applications.
- Implement supervised and unsupervised learning algorithms using Scikit-Learn.
- Dive into deep learning concepts and frameworks like TensorFlow and Keras.
- Develop proficiency in model evaluation and performance optimization.
- Apply learned skills to real-world data science and machine learning projects.
Audience: This workshop is ideal for data analysts, data scientists, software developers, and professionals seeking to enhance their Python skills for data-driven decision-making and machine learning applications. Basic Python knowledge is recommended.
Course Outline:
Introduction to Python for Data Science
- Overview of Python programming language
- Setting up Python environment
- Basic syntax and data types in Python
Data Manipulation with NumPy and Pandas
- Introduction to NumPy arrays
- Data manipulation with Pandas DataFrames
- Handling missing data and outliers
Data Visualization with Matplotlib
- Creating basic plots and charts
- Customizing plot appearance
- Visualizing data trends and patterns
Data Preprocessing and Cleaning
- Handling categorical data
- Scaling and normalizing features
- Dealing with outliers and anomalies
Introduction to Machine Learning
- Understanding the machine learning workflow
- Types of machine learning: supervised and unsupervised
- Choosing the right algorithm for a task
Supervised Learning with Scikit-Learn
- Overview of Scikit-Learn library
- Building and evaluating regression models
- Classifying data using classification algorithms
Deep Learning with TensorFlow and Keras
- Introduction to neural networks
- Building and training neural networks with TensorFlow
- Implementing deep learning models with Keras
Model Evaluation and Optimization
- Assessing model performance metrics
- Hyperparameter tuning for model optimization
- Strategies for avoiding overfitting
Participants will leave this workshop with a comprehensive understanding of Python for data science and machine learning, along with the practical skills to apply these concepts to real-world projects.