Data Science and AI/ML (Python) training course

Python Core Concepts and Best Practices

• Introduction to Python basic concepts, data structures and control flow structures. Overview of how Python is used for Data Science and Data Analytics projects.
• Environment set-up using Anaconda, a free and enterprise-ready distribution of Python. We'll discuss how to set up virtual environments and install Python packages. We'll also set up Jupyter, a web-based interactive environment where users can organise, write and run their Python code in notebooks.
• Notions of Object-Oriented Programming and Functional Programming, applied to the design of Python applications and analysis pipelines using best practices.
• The Anaconda distribution as Python Data Science platform
• Overview on Python virtual environment set-up
• Running code in Jupyter notebook
• Core data types in Python
• Control flow statements
• Defining and using custom functions
• The Python standard library

Working with data:

• Iteration and list comprehensions
• Accessing raw data on file (CSV, JSON, ...)
• Working with dates and times
• Basics of Object-Oriented Programming in Python

Python Data Science Tools

• We'll explore the most important Python tools for Data Science.
• NumPy, short for Numerical Python, is one of the main building blocks for scientific computing in Python. It provides high speed manipulation of multi-dimensional arrays and it's used by higher level libraries (like pandas) to support sophisticated analytics with high speed computation.
• Pandas is a highly performant library for data manipulation and data analysis in Python. It's built on top of NumPy and optimised for performance, while offering a high-level interface. We'll discuss how to create and manipulate Series and DataFrame objects in pandas, accessing data from multiple sources, cleaning and transforming data sets to get them in the right shape for advanced analysis.

Python Data Science Tools  (Cont..)

Numpy:

• Working with NumPy arrays
• Essential operations with NumPy arrays
• Stats and linear algebra with NumPy

Pandas:

• Working with table-like data in pandas
• Essential operations with Series and DataFrame object
• Loading data from file into DataFrame objects
• Summary statistics over DataFrame objects
• Data aggregation queries (groupby() method)
• Exploratory analysis of new datasets
• Data visualisation over DataFrames
• Join/merge operations with DataFrames
• Working with text data in DataFrames

Databases:

• Working with relational databases in Python
• Overview on SQLAlchemy for database interaction
• Integration of pandas and SQL

Accessing & Preparing Data

• Data can come in multiple formats and from multiple sources. We'll examine how to read and write data from local files in different formats, and how to access data from remote source.
• Data cleaning and data preparation are the first steps in a data analysis project, so we'll discuss how to perform data transformation to get ready for further analysis.

Data Analysis

• With our data in the right shape, we're ready to analyse them in order to extract useful insights.
• We'll perform the computation of summary information and basic statistics from data sets. We'll approach split-apply-combine operations with Data Frames, in order to perform advanced transformations and reshaping our data with pandas.
• We'll query our Data Frames using the powerful group-by method.

 Data Visualisation

• Data analysis benefits from the visualisation of data. If a picture if worth a thousand words, complex data structures can be easier to understand and analyse using effective visualisation techniques. Communicating the results with non-technical users is also a challenge that visualisation techniques help to overcome. We'll showcase how to easily produce beautiful visualisations with matplotlib.

Miscellanea

• Python packaging: using and creating custom libraries
• Unit testing: tools to perform unit testing in Python
• Interaction with web services

Overview

• What is Artificial Intelligence? What's up with the hype?
• Data Science vs. Data Mining vs. Machine Learning
• Machine Learning Problems and Applications
• Python Environment Set-up with Anaconda Python
◦ Jupyter Notebooks
◦ Python Ecosystem for Data Science and Machine Learning

Machine Learning Overview

• Learning and Prediction
• Feature Engineering
• Training data and Test data
• Cross-validation
• Underfitting and Overfitting

Supervised Learning Problems

• Classification: predicting a label
• Algorithms for classification: k-Nearest Neighbours, Support Vector Machine and Naive Bayes
• Regression: predicting a quantity
• Algorithms for regression: Linear Regression and Polynomial Regression

Unsupervised Learning Problems

• Clustering: grouping similar items
• Algorithms for clustering: k-Means, Hierarchical Clustering and DBSCAN
• Dimensionality Reduction
• Algorithms for dimensionality reduction: Principal Component Analysis

Evaluation of Machine Learning algorithms

• Evaluation metrics for machine learning
• Planning an evaluation campaign on your data

Deep Learning & Neural Network Overview

• Intro to Artificial Neural Networks
• Neural Network concepts
◦ Neural Network Types
◦ Gradient Descend
◦ Back-propagation
◦ Activation Functions
◦ Loss Functions
◦ Hyper-parameters
• Neural Networks in the Wild: examples of successful applications
• Deep Network Architectures
• Deep Learning Libraries