Discover fundamental deep learning concepts, master coding models using NumPy and Keras, and test your knowledge with quizzes and coding challenges. Gain insights into effective deep learning techniques.

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This beginner level and highly comprehensive course is intended for learners who are familiar with Python programming. You will become familiar with the fundamental concepts and terminologies used in deep learning. In addition, this course will help you understand the importance of deep learning techniques. You will examine simple models like perceptron before learning more complex yet powerful deep learning models. The course will provide hands-on practical knowledge of how to code simple and complex deep learning models in NumPy, a powerful Python library and Keras, a cutting-edge library for deep learning in Python. You can test your knowledge with the quizzes that are provided at the end of every lesson and coding challenges that will help you gain a higher understanding. By the end of the course, you should have a general understanding of the basics in deep learning and you will be equipped with the right tools to learn more advanced concepts.

A Beginner's Guide to Deep Learning

# Problem statement

The IRIS data set is taken from the [UCI](https://archive.ics.uci.edu/ml/datasets/iris) repository of datasets. It has four features and a label that classifies the flower.

|sepal_length|	sepal_width	|petal_length	|petal_width|	species|
|-|-|-|-|-|
|6.7|	3.3	|5.7	|2.1|	Iris-setosa|
|6.4	|3.1	|5.5	|1.8	|Iris-setosa|
|4.9	|3.1	|1.5	|0.1	|Iris-virginca|
|5.5|	3.5	|1.3|	0.2|	Iris-virginca|


The flowers can belong to either of the following classes:

Find the optimal weights that can classify each flower: Iris-setosa and non-iris setosa.

The data is in `iris.csv` file. Read the data from the `csv` file and store the features and the labels as a NumPy array prior to applying the perceptron algorithm.

For preprocessing the dataset, Python's library `pandas` is the best choice.

>  📝 Note: We will not cover every detail of the `pandas` package but we will cover the basic preprocessing techniques.






Classify flowers using the perceptron algorithm.

✨Before We Begin

✨Introduction to Deep Learning

✨Simple Perceptron Models in NumPY

✨Towards Deep Neural Networks in NumPY

🖥️ Project: Build a Letter Classification Model

Deep Learning in NumPy Exam

✨Building Deep Learning Models with Keras

✨Fine-tune Keras Model

🖥️ Project: Build a Digit Recognition Model

✨Conclusion

Deep Learning in Keras Exam

🍀 Challenge: Classification Using IRIS DataSet

Problem statement

sepal_length	sepal_width	petal_length	petal_width	species
6.7	3.3	5.7	2.1	Iris-setosa
6.4	3.1	5.5	1.8	Iris-setosa
4.9	3.1	1.5	0.1	Iris-virginca
5.5	3.5	1.3	0.2	Iris-virginca