Self-Paced Machine Learning October 2024 Batch
A Machine Learning course teaches students to create algorithms that enable systems to learn from data and improve over time. It covers supervised, unsupervised, and reinforcement learning, along with tools like Python, scikit-learn, and TensorFlow. Students gain practical experience in building predictive models, preparing for roles in AI, data science, and analytics.
FREE
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Course Overview
Perfect for those who prefer flexibility, our self-paced courses offer pre-recorded classes that you can access anytime, anywhere. Learn at your own pace and revisit lessons as needed, giving you complete control over your schedule. Ideal for busy professionals or independent learners who want to gain knowledge at their convenience.
Schedule of Classes
Start Date & End Date
Oct 01 2024 - May 31 2025
Course Curriculum
1 Subject
Self-Paced Machine Learning October 2024 Batch
34 Learning Materials
Induction Session and Class Agenda
Induction Session
Introduction to Python
Class 1
Class 2
Class 3
Class 4
Class 5
Class 6
Class 7
Class 8
Class 9
Class 10
Class 11
Class 12
Pre-Recorded Sesssions
Class 1 - Introduction to AI and Machine Learning Setting up your development environment
Class 2 - supervised learning project using scikit-learn during the live session.
Class 3 - linear regression project using scikit-learn during the live session.
Class 4 - Logistic regression, K-Nearest Neighbors, & model evaluation.
Class 5 - classification project using scikit-learn during the live session
Class 6 - decision tree project using scikit-learn during the live session.
Class 7 - Random Forest: Introduction to random forests, bagging, & boosting.
Class 8 - random forest project using scikit-learn during the live session.
Class 9 - unsupervised learning project using scikit-learn during the live session.
Class 10 - Introduction to principal com ponent analysis (PCA) (t-SNE)
Class 11 - dimensionality reduction project using scikit-learn during the live session
Class 12 - SVM project using scikit-learn during the live session.
Class 13 -Introduction to neural networks, backpropagation algorithm, & activation functions.
Class 14 -neural network project using TensorFlow during the live session
Class 15 - deep learning project using TensorFlow during the live session.
Class 16 -Introduction to NLP, text preprocessing, and bag-of-words model.
Class 17 - NLP project using scikit-learn during the live session.
Class 18 - time series analysis project using statsmodels during the live session.
Class 19 - Ensemble Learning: Introduction to ensemble learning, bagging, boosting, and stacking.
Notes
Task 1
main_nlp_file
Course Instructor
Teachnook Team
567 Courses • 41569 Students
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