Overview
Introduction:
Python stands as a cornerstone language for data science and machine learning, offering versatile tools and libraries to analyze and model complex datasets. It covers the essential frameworks and techniques for data processing, visualization, and predictive modeling. 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:
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Utilize Python and its libraries to analyze, visualize, and manipulate data effectively.
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Prepare datasets for analysis by applying advanced preprocessing and exploration techniques.
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Develop supervised machine learning models and optimize their performance using Scikit-learn.
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Create and train deep learning models with TensorFlow for handling complex data types.
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Deploy machine learning models in production environments and ensure their scalability.
Target Audience:
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Data Scientists.
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Machine Learning Engineers.
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Python Developers.
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Data Analysts.
Program Outline:
Unit 1:
Introduction to Python for Data Science:
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Overview of Python in data science and machine learning.
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Setting up the Python environment for data science.
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Introduction to Jupyter notebooks and basic Python syntax.
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How to work with data using Pandas for data manipulation.
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Visualizing data using Matplotlib and Seaborn.
Unit 2:
Data Preprocessing and Exploration:
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The process of cleaning and preprocessing data for analysis.
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Handling missing values, outliers, and data normalization.
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How to analyze data using descriptive statistics.
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Feature selection and engineering for machine learning.
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Methods of splitting datasets into training and testing sets.
Unit 3:
Supervised Learning Models with Scikit-learn:
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The supervised learning algorithms: classification and regression.
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How to build machine learning models with Scikit-learn.
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Training models such as linear regression, decision trees, and SVMs.
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Evaluating model performance using accuracy, precision, and recall.
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Fine-tuning models with hyperparameter optimization.
Unit 4:
Introduction to Deep Learning with TensorFlow:
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Overview of deep learning and neural networks.
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Setting up TensorFlow and Keras for deep learning.
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How to build and train neural networks for image and text data.
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Backpropagation and gradient descent.
Unit 5:
Model Deployment:
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Introduction to model deployment in production environments.
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Steps for using Flask and Django to deploy Python-based models.
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How to integrate machine learning models with cloud platforms.
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The importance of continuous monitoring and updating of deployed models.