This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as group by, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

- Describe common Python functionality and features used for data science
- Query DataFrame structures for cleaning and processing
- Explain distributions, sampling, and t-tests

**Week-1**

- 1. What is deep learning
- 2. Before we begin: the mathematical building blocks of neural networks (tensor, vector, matrix)

**Week-2**

- 3. Getting started with neural networks (getting familiar all parts of a model)
- 4. Fundamentals of machine learning with convolution neural network (pictures).

**Week-3**

- 5. Image classification
- 6. Image segmentation

**Week-4-5**

- 7. Make predictions
- 8. Evaluate the model with score ()
- 9. Project/s build and train a model with google Collaboratory

**Week-6-7 (using text to build a model and make predictions)**

- 1. What is deep learning
- 2. Before we begin: the mathematical building blocks of neural networks (tensor, vector, matrix)

**Week-8**

- 3. Getting started with neural networks (getting familiar all parts of a model)
- 4. Fundamentals of machine learning with convolution neural network (pictures).

**Week-9**

- 5. Image classification
- 6. Image segmentation

**Week-10**

- 7. Make predictions
- 8. Evaluate the model with score ()
- 9. Project/s build and train a model with google Collaboratory