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https://github.com/sondosaabed/intro-to-machine-learning-with-tensorflow-nanodegree

Acquired a full scholarship from Google Palestine Launchpad. It is about the mastery of machine learning techniques such as data transformation and algorithms that can find patterns in data and apply machine learning algorithms to tasks of their own design.
https://github.com/sondosaabed/intro-to-machine-learning-with-tensorflow-nanodegree

machine-learning palestine python tensorflow

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Acquired a full scholarship from Google Palestine Launchpad. It is about the mastery of machine learning techniques such as data transformation and algorithms that can find patterns in data and apply machine learning algorithms to tasks of their own design.

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# Intro-to-Machine-Learning-with-TensorFlow-Nanodegree

Acquired a full scholarship from Google Palestine Launchpad. It is about the mastery of machine learning techniques such as data transformation and algorithms that can find patterns in data and apply machine learning algorithms to tasks of their own design.



## Verified Certificate Of Nanodegree Program Completion



![image](https://github.com/user-attachments/assets/1907b1db-0c9a-4044-ab65-688b01034786)



## Courses



### 1. Supervised Machine Learning

#### Lesson 1: Regression

• Learn the difference between regression and classification.

• Train a linear regression model to predict values.

• Learn to predict states using logistic regression.



#### Lesson 2: Perceptron Algorithms

• Learn the definition of a perceptron as a building block for neural networks, and the perceptron algorithm for classification.



#### Lesson 3: Decision Trees

• Train decision trees to predict states.

• Use Entropy to build decision trees recursively.



#### Lesson 4: Naive Bayes

• Learn Bayes’ rule and apply it to predict cases of spam messages using the



#### Naive Bayes algorithm.

• Train models using Bayesian learning.

• Complete an exercise that uses Bayesian learning for natural language processing.



#### Lesson 5: upport Vector Machines

• Learn to train a support vector machine to separate data linearly.

• Use Kernel Methods in order to train SVMs on data that is not linearly separable.



#### Lesson 6: Ensemble of Learners

• Enhance traditional algorithms via boosting.

• Learn and apply Random Forest algorithms.

• Use AdaBoost and evaluate the performance of boosted models.



#### Lesson 7; Evaluation Metrics

• Learn about different metrics to measure model success.

• Calculate accuracy, precision, and recall to measure the performance of your models.



#### Lesson 8 Training & Tuning Models

• Train and test models with Scikit-learn.

• Choose the best model using evaluation techniques like cross validation and grid search.



### 2. Introduction to neural networks

#### Introduction to Neural Networks

In this lesson, Luis will give you solid foundations on deep learning and neural networks. You'll also implement gradient descent and backpropagation in Python right here in the classroom.



#### Implementing Gradient Descent

Mat will introduce you to a different error function and guide you through implementing gradient descent using NumPy matrix multiplication.



#### Training Neural Networks

By this lesson, you will know what neural networks are, be familiar with much of the underlying mathematics, and be able to construct basic networks in code. But your models won't always work well—so in this lesson, you will learn several techniques to optimize the training process.



#### Deep Learning with TensorFlow

In this lesson, we'll get a lot of hands-on practice and learn how to use TensorFlow to train a deep learning model that can classify images.



### 3. Unsupervised Learning

#### I. Clustering

Clustering is one of the most popular unsupervised approaches. In a first look at clustering, you will gain an understanding of what clustering your data means. Then, you will see how the k-means algorithm works. You will put your skills to work to find groupings of similar movies!



#### II. Hierarchical and Density Based Clustering

Another set of clustering algorithms takes an approach of using density based 'closeness' measures. At the end of the lesson, you will see how this can be used in traffic classification, as well as in anomaly detection (finding points that aren't like others in your dataset).



#### III. Gaussian Mixture Models and Cluster Validation

To extend the density based approaches, you will get some practice with gaussian mixture models. This technique is not far from what you learned in the previous lesson, and it is the last of the clustering algorithms you will learn before moving to matrix decomposition methods.



#### IV. Principal Component Analysis

Principal component analysis is one of the most popular decomposition methods available today. In this lesson, you will learn how matrix decomposition methods work conceptually. Then you will apply principal component analysis to images of handwritten digits to reduce the dimensionality of these images.



#### V. Random Projection and Independent Component Analysis

Another way to decompose data is through independent component analysis. In this lesson, you will see how this method can pull apart audio related to a piano, cello, and television that has been overlaid in the same file.



## Projects



### Course one project: Supervised Machine Learning

https://github.com/sondosaabed/Finding-Charity-Donors 



### Course two project: Introduction to neural networks with Tensorflow

https://github.com/sondosaabed/Image-Classifier-with-Deep-Learning-TF



### Course three project: Unsupervised Learning

https://github.com/sondosaabed/Identify-Customer-Segments