- Logistic Regression
- Artificial Neural Network
- Linear Regression
- Decision Trees
- Recommender Systems
Machine Learning
Completed by Animeshkumar Nayak
August 17, 2022
Approximately 2 months at 10 hours a week to completeAnimeshkumar Nayak's account is verified. Coursera certifies their successful completion of Stanford University & DeepLearning.AI Machine Learning Specialization.
Course Certificates Completed
Supervised Machine Learning: Regression and Classification
Advanced Learning Algorithms
Unsupervised Learning, Recommenders, Reinforcement Learning
What you will learn
Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)
Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods
Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection
Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model
Skills you will gain
Course Certificates
Earned after completing each course in the Specialization
Supervised Machine Learning: Regression and Classification
DeepLearning.AI & Stanford University
Taught by: Andrew Ng, Aarti Bagul & Geoff Ladwig
Completed by: Animeshkumar Nayak by July 22, 2022
At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.
Advanced Learning Algorithms
DeepLearning.AI & Stanford University
Taught by: Andrew Ng, Aarti Bagul & Geoff Ladwig
Completed by: Animeshkumar Nayak by August 15, 2022
At the rate of 5 hours a week, it typically takes 4 weeks to complete this course.
Unsupervised Learning, Recommenders, Reinforcement Learning
DeepLearning.AI & Stanford University
Taught by: Andrew Ng, Aarti Bagul & Geoff Ladwig
Completed by: Animeshkumar Nayak by August 17, 2022
At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.