Mastering Python for Data Science and Machine Learning

Overview:

Introduction:

This training program equips participants with the skills to apply Python in data science and machine learning. It empowers them to build predictive models, analyze complex datasets, and apply machine learning techniques using Python's powerful libraries and frameworks.

Program Objectives:

By the end of this program, participants will be able to:

• Understand Python’s role in data science and machine learning.

• Use Python libraries like Pandas, NumPy, and Matplotlib for data analysis.

• Build machine learning models using Scikit-learn and TensorFlow.

• Apply data preprocessing techniques for machine learning.

• Develop and evaluate predictive models.

Target Audience:

• Aspiring Data Scientists.

• Machine Learning Engineers.

• Python Developers.

• Data Analysts.

Program Outline:

Unit 1:

Introduction to Python for Data Science:

• Overview of Python in data science and machine learning.

• Setting up the Python environment for data science.

• Introduction to Jupyter notebooks and basic Python syntax.

• Working with data using Pandas for data manipulation.

• Visualizing data using Matplotlib and Seaborn.

Unit 2:

 Data Preprocessing and Exploration:

• Cleaning and preprocessing data for analysis.

• Handling missing values, outliers, and data normalization.

• Exploratory data analysis using descriptive statistics.

• Feature selection and engineering for machine learning.

• Splitting datasets into training and testing sets.

Unit 3:

Supervised Learning Models with Scikit-learn:

• Understanding supervised learning algorithms (classification and regression).

• Building machine learning models with Scikit-learn.

• Training models such as linear regression, decision trees, and SVMs.

• Evaluating model performance using accuracy, precision, and recall.

• Fine-tuning models with hyperparameter optimization.

Unit 4:

Introduction to Deep Learning with TensorFlow:

• Overview of deep learning and neural networks.

• Setting up TensorFlow and Keras for deep learning.

• How to build and train neural networks for image and text data.

• Understanding backpropagation and gradient descent.

Unit 5:

Model Deployment and Real-World Applications:

• Introduction to model deployment in production environments.

• Using Flask and Django to deploy Python-based models.

• Integrating machine learning models with cloud platforms.

• Best practices for scaling machine learning applications.

• Continuous monitoring and updating of deployed models.