Machine Learning Using Python Internship Program
in InternshipAbout this course
Duration: 1 Month
Mode: Online
Level: Beginner to Intermediate
Prerequisites: Basic understanding of computers and the internet
Tools Required:
● Python(with Anaconda Distribution recommended)
● Jupiter Notebook
● Libraries: Numpy, Panda, Matplotlib, Scikit-learn
NOTE: we do help in installation and configuration
Program Overview
This internship program is designed to provide students with hands-on experience in web application development. Over the course of one month, participants will learn to create dynamic and responsive web applications using HTML, CSS, JavaScript, Bootstrap, PHP, and MySQL. By the end of the program, students will have developed a complete web application, ready to showcase in their portfolio.
Key Features
● Hands-on Experience: Work on real-world projects and gain practical experience in machine learning.
● Live Sessions: Interactive live sessions with industry experts to guide you through the course.
● Mentorship: One-on-one mentorship to help you overcome challenges and enhance your learning.
● Project-Based Learning: Develop a complete machine learning as part of your final project.
● Certificate of Completion: Receive a certificate that validates your skills and knowledge.
Learning Outcomes
By the end of this internship program, participants will:
● Have a strong understanding of machine learning concepts and techniques.
● Be able to preprocess and visualize data for machine learning tasks.
● Implementation and evaluate various machine learning algorithms usuing python.
● Apply machine learning to solve real-world problems.
● Showcase their work through a completed machine learning project.
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Gain an understanding of the fundamental concepts of machine learning, its applications across various industries, and an overview of different types of machine learning, including supervised, unsupervised, and reinforcement learning.
Refresh your knowledge of Python basics, including variables, data types, loops, and functions, and get introduced to essential data science libraries like NumPy and Pandas. You'll also learn to manipulate and analyze data using Pandas, setting a strong foundation for data-driven tasks
Understand the importance of data cleaning and preprocessing, including handling missing data, outliers, and categorical variables. Learn about feature scaling and normalization to prepare your data for effective model training.
Get introduced to data visualization techniques using Matplotlib and Seaborn. Learn to create various plots, histograms, scatter plots, and heatmaps to visualize the relationship between features and target variables in your dataset.
Explore core machine learning techniques, starting with linear regression, logistic regression, and extending to decision trees and random forests. Learn to evaluate model performance using key metrics like accuracy, precision, recall, and F1-score.
Delve into unsupervised learning techniques, including clustering methods like K-means and hierarchical clustering, and dimensionality reduction using PCA. Apply these methods to real-world datasets to uncover hidden patterns.
Discover advanced techniques such as ensemble methods (Bagging, Boosting), support vector machines (SVM), and neural networks. Learn about hyperparameter tuning and cross-validation to optimize your models
Apply your knowledge to develop a machine learning model for a real-world problem, culminating in a project presentation and peer review. This project will test your ability to build, deploy, and interpret a fully functional machine learning solution.
Learn how to deploy your machine learning model using Flask or Django, and gain insights into interpreting model results and understanding the limitations of your models.
