AI & ML Using Python

Learning Data Analytics with Artificial Intelligence & Machine Learning

Leading Financial company

Today our client is one of the world’s largest banking and financial services organization serving more than 38 million customers worldwide. Headquartered in London, the bank operates through long-established businesses and an international network of around 3,800 offices in 66 countries and territories.

In the 21st century, bank renewed focus on its birthplace, growing its business in China both organically and through a series of strategic partnerships. Bank’s diversification and its core values of financial strength and stability have stood it in good stead in the recent global turbulence in economies and markets, and it remains well placed to deal with an uncertain world.

Our Trainers

Business Goals

  • A Python training workshop was needed for bank staff
  • Purpose was to introduce the Python Programming to the users
  • Include an introduction about Machine Learning
  • To understand the tools and techniques to use Python for Data Analytics

LSF's Solution

• Machine Learning Foundations:-

This section covered a wide variety of topics in machine learning and statistical modeling. The primary goal of this section was to help participants gain a deep understanding of the concepts, techniques and mathematical frameworks used by experts in machine learning.

• Python Programming Fundamentals:-

This section helped participants understand how to recognize and apply abstract patterns in programming through the extensive use of illustrative examples and practical exercises.

• Python for Data Science: Data Fetching and Visualisation:-

This section focused on using Python for Data Science. Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. It also included understanding the packages like NumPy and SciPy, Pandas Library, data importing and working with data frame objects.

• Deep-Dive into Scikit-Learn Package for Machine Learning:-

This section explained Data preprocessing, (Rescaling, Normalization, Standardization, Label-encoding, One-Hot encoding etc..) and Feature Section methods in sklearn package. Many Machine Learning Algorithms were also discussed with the participants to make them understand the practical usage.

• Regularization and Ensemble Methods in Machine Learning:-

Ensemble learning is a machine learning paradigm where multiple models are trained to solve the same problem and combined to get better results. This section helped participants understand all types of Regularization methods (Ridge, Lasso, ElasticNet etc..).

Statistics based on delegates feedback

82%
Impact
78%
The Session
70%
Work Application
80%
Your Facilitator
82%
Resources

Comments From Company Executives

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