Machine Learning Using Python Certification Program
in Certification ProgramAbout this course
Machine Learning Using Python Certification Program
Duration: 2 Months
Mode: Online
Level: Beginner to Intermediate
Prerequisites: Basic understanding of computers and the internet
Tools Required:
· Python (with Anaconda Distribution recommended)
· Jupyter Notebook
· Libraries: Numpy, Pandas, Matplotlib, Scikit-learn
· NOTE: We do help in installation and configuration
Program Overview
The Machine Learning Using Python Certification Program is a comprehensive 2-month online course designed to equip participants with practical knowledge and skills in machine learning. Whether you're a beginner or have some programming experience, this program offers a structured approach to mastering the essentials of machine learning using Python. Through interactive live sessions, hands-on projects, and one-on-one mentorship, participants will learn to harness Python's powerful libraries for data analysis, visualization, and predictive modeling. By the end of the program, you’ll have a completed machine learning project to showcase in your 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 project as part of your final project.
· Certificate of Completion: Receive a certificate that validates your skills and knowledge.
Learning Outcomes
· Have a strong understanding of machine learning concepts and techniques.
· Be able to preprocess and visualize data for machine learning tasks.
· Implement and evaluate various machine learning algorithms using Python.
· Apply machine learning to solve real-world problems.
· Showcase their work through a completed machine learning project.
Course Schedule and Details
We are excited to offer flexible timing options for our Machine Learning Using Python Certification Program to suit your convenience. Choose the batch that works best for you:
Batch Type | Timing |
Morning Batch (Weekdays) | Monday to Thursday, 7:30 AM - 8:30 AM |
Evening Batch (Weekdays) | Monday to Thursday, 7:30 PM - 8:30 PM |
Saturday Batch | 10:00 AM - 12:00 PM |
Sunday Batch | 10:00 AM - 12:00 PM |
Additional Benefits
· Access to recorded classes for review or if you miss a session.
· Comprehensive learning material and support.
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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.
