https://github.com/adicherlavenkatasai/coursera
Coursera Speccialization Courses
https://github.com/adicherlavenkatasai/coursera
deep-learning deep-neural-networks hyperparameter-optimization machine-learning machine-learning-algorithms neural-networks python sframe-dataframe train-test-split turicreate
Last synced: 17 days ago
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Coursera Speccialization Courses
- Host: GitHub
- URL: https://github.com/adicherlavenkatasai/coursera
- Owner: AdicherlaVenkataSai
- Created: 2020-06-11T18:25:28.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-08-28T11:39:09.000Z (almost 6 years ago)
- Last Synced: 2025-10-07T05:45:08.955Z (8 months ago)
- Topics: deep-learning, deep-neural-networks, hyperparameter-optimization, machine-learning, machine-learning-algorithms, neural-networks, python, sframe-dataframe, train-test-split, turicreate
- Language: Jupyter Notebook
- Homepage:
- Size: 40.7 MB
- Stars: 1
- Watchers: 0
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Machine Learning Specialization
## 1. Machine Learning with Python(audit) [Resources](https://drive.google.com/drive/folders/1VKe2otKaAREkaCvmVG8FZm4oK0JRr4CZ?usp=sharing)
What all i learnt?
- In this audit course, i have implemented the supervised and unsupervised learning algorithms
- Tuning the hyper parameters
## 2. Machine Learning Foundation
### WEEK 1 | 20 July [Resources](https://drive.google.com/drive/folders/196p39Nz6ECY0MesNwV8_3WMaWq33tYEd?usp=sharing)
- Week 1 offers the basic intoduction about Machine learning, how it evolved
- Introduction to turicreate, SFrame and its basic implementation
- Solved quiz questions
Note: Check out the Resources to access .ipynb, data files and other materials.
### WEEK 2 | 21 July | Use Case 1 [Resources](https://drive.google.com/drive/folders/1Okl0w3M7IFnBX7RA4W5fa38YdPATshcz?usp=sharing)
What all i learnt?
- Linear Regression use case approach and its other applications
- How to load .sframe data file
- Data exploration using turicreate.SFrame
- Train test split of SFrame data file
- Creating simple regression model using one/set of independent varibales
- Training the model, and evaluating it on test_data
- solved quiz questions
Note: Check out the Resources to access .ipynb, data files and other materials.
### WEEK 3 | 26 July | Use Case 2 [Resources](https://drive.google.com/drive/folders/1FSwDbLdF_ReJD26UojnRKquRrTnM3oc3?usp=sharing)
What all i learnt?
- linear Classifier (binary classificatio)
# Deep Learning Specialization
## 1. Neural Networks and Deep learning
### WEEK 1 | 27 July [Resources](https://drive.google.com/drive/folders/1xAjxhIZRBQCWW6jQMhnpR6wfP6qVCy_k?usp=sharing)
What all i learnt?
- In this week we have introduction to neural networks and its examples
- Check the hand written notes for more information
### WEEK 2 | 27 July [Resources](https://drive.google.com/drive/folders/1xAjxhIZRBQCWW6jQMhnpR6wfP6qVCy_k?usp=sharing)
What all i learnt?
- Logistic regression (binary classification)
- Gradient Descent in Logistic Regression, Cost Funtion
- Vectorization
### WEEK 3 | 1 August [Resources](https://drive.google.com/drive/folders/1xAjxhIZRBQCWW6jQMhnpR6wfP6qVCy_k?usp=sharing)
What all i learnt?
- Forward Propagation
- Backward Propagation
- Gardients and updating the weights and bias
- single hidden layer neural network
### WEEK 4 | 5 August [Resources](https://drive.google.com/drive/folders/1xAjxhIZRBQCWW6jQMhnpR6wfP6qVCy_k?usp=sharing)
What all i learnt?
- L layered Neural Network
- Forward and Back Propagations
- Gardients and updating the weights and bias
- Implementing L layer neural network for a Simple Classification Problem (Cat vs no-Cat)
## 2. Improving Deep Neural Networks (Hyperparameter tuning, Regularization and Optimization)
### WEEK 1 | 10 August [Resources](https://drive.google.com/drive/folders/1A6ywFEvLgzjdp0XCVBmHSUogFiZMP_B-?usp=sharing)
What all i learnt?