Python with Machine Learning Workshop – 24 Hours

Python with Machine Learning Workshop

Duration: 24 Hours
The Python with Machine Learning Workshop is designed to help students build practical programming, data analysis, and machine learning skills. Participants will learn Python from the basics, work with datasets, create charts, train machine learning models, and complete a real-world mini project.
This workshop is suitable for degree students, graduates, and beginners interested in Artificial Intelligence, Data Science, and Machine Learning.

Module 1: Introduction to Python, AI and Machine Learning

Duration: 2 Hours
Learn the basics of Python programming and understand the relationship between Artificial Intelligence, Machine Learning, Data Science, and Deep Learning.
Practical Activity:
Install Python and Jupyter Notebook or use Google Colab. Write basic Python programs.

Module 2: Python Programming Fundamentals

Duration: 4 Hours
Learn Python syntax, variables, data types, operators, input and output statements, conditional statements, loops, functions, strings, lists, tuples, sets, and dictionaries.
Practical Activity:
Create Python programs for student marks, attendance, calculators, and simple menu-driven applications.

Module 3: Python for Data Analysis

Duration: 4 Hours
Learn how to work with data using NumPy and Pandas. Topics include importing datasets, viewing data, data cleaning, handling missing values, sorting, filtering, grouping, and basic statistical analysis.
Practical Activity:
Import a CSV dataset and perform data cleaning and analysis using Pandas.

Module 4: Data Visualization

Duration: 3 Hours
Learn to represent data visually using Matplotlib and Seaborn.
Practical Activity:
Create bar charts, line charts, pie charts, histograms, box plots, and scatter plots using real datasets.

Module 5: Introduction to Machine Learning

Duration: 3 Hours
Understand the machine learning workflow, including supervised learning, unsupervised learning, features, labels, datasets, training data, testing data, model prediction, and evaluation.
Practical Activity:
Prepare a dataset and perform train-test split using Scikit-learn.

Module 6: Machine Learning Algorithms

Duration: 5 Hours
Learn and implement basic machine learning algorithms such as Linear Regression, Logistic Regression, Decision Tree, K-Nearest Neighbors, Naive Bayes, and K-Means Clustering.
Practical Activity:
Train, test, and evaluate machine learning models using Scikit-learn.

Module 7: Mini Project

Duration: 2 Hours
Participants will build a basic machine learning project using Python.
Project Options:

• Student Performance Prediction
• House Price Prediction
• Sales Prediction
• Customer Purchase Prediction
• Iris Flower Classification
• Loan Eligibility Prediction

Module 8: Presentation and Assessment

Duration: 1 Hour
Participants will demonstrate their project and explain the dataset, Python code, machine learning model, predictions, and results.
Assessment Includes:

• Project demonstration
• Viva
• Feedback session

Pricing Structure

Workshop Duration: 24 Hours
Inhouse Workshop: ?2,500 per candidate
Onsite Workshop: ?1,500 per candidate
For a batch of 40 students:

• 40 × ?1,500 = ?60,000 for an onsite workshop
• 40 × ?2,500 = ?1,00,000 for an inhouse workshop

Batch Size Criteria

The batch size should be a minimum of 40 participants and a maximum of 60 participants.
For batches with fewer than 40 participants, workshop charges will be calculated on an hourly basis.