https://github.com/edaaydinea/machinelearning

https://github.com/edaaydinea/machinelearning

Last synced: 6 months ago
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• Host: GitHub
• URL: https://github.com/edaaydinea/machinelearning
• Owner: edaaydinea
• License: apache-2.0
• Created: 2024-07-30T16:13:07.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-09-17T19:07:48.000Z (about 1 year ago)
• Last Synced: 2024-09-18T04:32:12.412Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 1.71 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Machine Learning Specialization

* **Where:** Coursera

* **University/Institute:** Deeplearning.AI

* **Status:** Completed

---

## Courses in this Specialization

Course 1: Supervised Machine Learning: Regression and Classification

* **Status:** Completed

* **Link:** \<>

Course 2: Advanced Learning Algorithms

* **Status:** Complete

* **Link:** \<>

Course 3: Unsupervised Learning, Recommenders, Reinforcement Learning

* **Status:** Completed

* **Link:** \<>

---

## Courses

---

### **Course 1: Supervised Machine Learning: Regression and Classification**

**Week1:**

* [**Lecture Note**](Course1/Week1/lecture_note.md)

**Week2:**

* [**Lecture Note**](Course1/Week2/lecture_note.md)

* **Programming Assignments:**

  * [Optional Lab: python, NumPy and Vectorization](Course1/Week2/C1_W2_Lab01_Python_Numpy_Vectorization_Soln.ipynb)

  * [Optional Lab: Multiple Variable Linear Regression](Course1/Week2/C1_W2_Lab02_Multiple_Variable_Soln.ipynb)

  * [Optional Lab: Feature scaling and Learning Rate (Multi-variable)](Course1/Week2/C1_W2_Lab03_Feature_Scaling_and_Learning_Rate_Soln.ipynb)

  * [Optional Lab: Feature Engineering and Polynomial Regression](Course1/Week2/C1_W2_Lab04_FeatEng_PolyReg_Soln.ipynb)

  * [Optional Lab: Linear Regression using Scikit-Learn](Course1/Week2/C1_W2_Lab05_Sklearn_GD_Soln.ipynb)

  * [Practice Lab: Linear Regression](Course1/Week2/C1_W2_Linear_Regression.ipynb)

**Week3:**

* [**Lecture Note**](Course1/Week3/lecture_note.md)

* **Programming Assignments**

  * [Optional Lab: Classification](Course1/Week3/C1_W3_Lab01_Classification_Soln.ipynb)

  * [Optional Lab: Logistic Regression](Course1/Week3/C1_W3_Lab02_Sigmoid_function_Soln.ipynb)

  * [Optional Lab: Logistic Regression, Decision Boundary](Course1/Week3/C1_W3_Lab03_Decision_Boundary_Soln.ipynb)

  * [Optional Lab: Logistic Regression, Logistic Loss](Course1/Week3/C1_W3_Lab04_LogisticLoss_Soln.ipynb)

  * [Optional Lab: Cost Function for Logistic Regression](Course1/Week3/C1_W3_Lab05_Cost_Function_Soln.ipynb)

  * [Optional Lab: Gradient Descent for Logistic Regression](Course1/Week3/C1_W3_Lab06_Gradient_Descent_Soln.ipynb)

  * [Ungraded Lab: Logistic Regression using Scikit-Learn](Course1/Week3//C1_W3_Lab07_Scikit_Learn_Soln.ipynb)

  * [Ungraded Lab: Overfitting](Course1/Week3/C1_W3_Lab08_Overfitting_Soln.ipynb)

  * [Optional Lab - Regularized Cost and Gradient](Course1/Week3/C1_W3_Lab09_Regularization_Soln.ipynb)

  * [Practice Lab: Logistic Regression](Course1/Week3/C1_W3_Logistic_Regression.ipynb)

---

### **Course 2: Advanced Learning Algorithms**

**Week 1:**

* [**Lecture Note**](Course2/Week1/lecture_note.ipynb)

* **Programming Assignments**

  * [Optional Lab - Neurons and Layers](Course2/Week1/C2_W1_Lab01_Neurons_and_Layers.ipynb)

  * [Optional Lab - Simple Neural Network](Course2/Week1/C2_W1_Lab02_CoffeeRoasting_TF.ipynb)

  * [Optional Lab - Simple Neural Network](Course2/Week1/C2_W1_Lab03_CoffeeRoasting_Numpy.ipynb)

  * [C2\_W1\_Assignment](Course2/Week1/C2_W1_Assignment.ipynb)

**Week 2:**

* [**Lecture Note**](Course2/Week2/lecture_note.ipynb)

* **Programming Assignments**

  * [Optional Lab - ReLU activation](Course2/Week2/C2_W2_Relu.ipynb)

  * [Optional Lab - Softmax activation](Course2/Week2/C2_W2_SoftMax.ipynb)

  * [Optional Lab - Multi-class Classification](/Course2/Week2/C2_W2_Multiclass_TF.ipynb)

  * [Optional Lab - Derivatives](/Course2/Week2/C2_W2_Derivatives.ipynb)

  * [Optional Lab: Back propagation using a computation graph](/Course2/Week2/C2_W2_Backprop.ipynb)

  * [C2\_W2\_Assignment](/Course2/Week2/C2_W2_Assignment.ipynb)

**Week 3:**

* [**Lecture Note**](Course2/Week3/lecture_note.ipynb)

* **Programming Assignments**

  * [Optional Lab - Model Evaluation and Selection](/Course2/Week3/C2W3_Lab_01_Model_Evaluation_and_Selection.ipynb)

  * [Optional Lab - Diagnosing Bias and Variance](/Course2/Week3/C2W3_Lab_02_Diagnosing_Bias_and_Variance.ipynb)

  * [C2\_W3\_Assignment](/Course2/Week3/C2_W3_Assignment.ipynb)

**Week 4:**

* [**Lecture Note**](Course2/Week4/lecture_note.ipynb)

* **Programming Assignments**

  * [Ungraded Lab - Decision Trees](/Course2/Week4/C2_W4_Lab_01_Decision_Trees.ipynb)

  * [Ungraded Lab - Trees Ensemble](/Course2/Week4/C2_W4_Lab_02_Tree_Ensemble.ipynb)

  * [C2\_W4\_Assignment](/Course2/Week4/C2_W4_Decision_Tree_with_Markdown.ipynb)

---

### **Course 3: Unsupervised Learning, Recommenders, Reinforcement Learning**

**Week 1:**

* [**Lecture Note**](/Course3/Week1/lecture_note.ipynb)

* **Programming Assignments**

  * [K-Means Clustering](/Course3/Week1/C3_W1_KMeans_Assignment.ipynb)

  * [Anomaly Detection](/Course3/Week1/C3_W1_Anomaly_Detection.ipynb)

**Week 2:**

* [**Lecture Note**](/Course3/Week2/lecture_note.ipynb)

* **Programming Assignments**

  * [Practice lab: Collaborative Filtering Recommender Systems](/Course3/Week2/C3_W2_Collaborative_RecSys_Assignment.ipynb)

  * [Practice lab: Deep Learning for Content-Based Filtering](/Course3/Week2/C3_W2_RecSysNN_Assignment.ipynb)

  * [PCA and Data Visualization](/Course3/Week2/C3_W2_Lab01_PCA_Visualization_Examples.ipynb)

**Week 3:**

* [**Lecture Note**](/Course3/Week3/lecture_note.ipynb)

* **Programming Assignments**

  * [State Action Value Function Example](/Course3/Week3/State-action%20value%20function%20example.ipynb)

  * [Deep Q-Learning - Lunar Lander](/Course3/Week3/C3_W3_A1_Assignment.ipynb)

---

## Certificates

* [**Course 1: Supervised Machine Learning: Regression and Classification**](https://coursera.org/share/ea0f566cd6d30c9494c8bd423d380b01)

* [**Course 2: Advanced Learning Algorithms**](https://coursera.org/share/38bfdec1a987d7c665ae10b2ede6c9a3)

* **Course 3: Unsupervised Learning, Recommenders, Reinforcement Learning**