Introduction to Machine Learning Bootcamp
7 Courses | 18h 6m 26s
This course is designed to introduce you to the machine learning basics using python and help you build industry level use case for financial banks - predicting fraud in credit card transaction. This course will help you learn most popular python libraries like scikit learn, pandas, numpy and many more along with theoretical concepts like supervised learning, model building and optimisation, feature engineering and pre-processing. The course will also cover basic machine learning algorithms like Linear Regression, Logistic Regression, Decision Trees and Random Forest. This course will benefit you if you are new to machine learning and python, or if you are coming from any IT vertical or other cross functional areas like marketing, finance, human resource, sales etc and looking forward to enhance your skills (make sure to take pre-requisite courses before attending; Machine Learning Introduction, Data Science Overview, and Python - Introduction to Pandas and DataFrames). The course will be 4 hours per day for 4 days and will include combination of theory lectures, virtual games, digital whiteboard, live quizzes, industry information, python hand-on demos, assignments, future reference and notes. The course includes building live end to end machine learning use case from financial domain where you will experience everything from data processing, feature engineering, model building, model optimisation and deployment using various python libraries and core machine learning concepts to help banks predict transactional fraud model that can save them million of dollars.
Introduction to Machine Learning Bootcamp: Session 1 Replay
4 videos | 3h 40m
This is a recorded Replay of the Introduction to Machine Learning Live session that ran on February 1st at 11:00 AM EST. In this session Lavi Nigam discusses Machine Learning, its various types, and Python.
Introduction to Machine Learning Bootcamp: Session 2 Replay
3 videos | 3h 56m
This is a recorded Replay of the Introduction to Machine Learning Live session that ran on February 2nd at 11:00 AM EST. In this session Lavi Nigam introduces Linear & Logistic Regression, and Feature Engineering.
Introduction to Machine Learning Bootcamp: Session 3 Replay
3 videos | 3h 52m
This is a recorded Replay of the Introduction to Machine Learning Live session that ran on February 3rd at 11:00 AM EST. In this session Lavi Nigam introduces Decision Trees, Random Forests, Model Evaluation & Optimization.
Introduction to Machine Learning Bootcamp: Session 4 Replay
3 videos | 3h 59m
This is a recorded Replay of the Introduction to Machine Learning Live session that ran on February 4th at 11:00 AM EST. In this session Lavi Nigam discusses Building a Fraud System.
Machine Learning Introduction
8 videos | 43m
Machine learning is a particular area of data science that uses techniques to create models from data without being explicitly programmed. Explore the conceptual elements of various machine learning techniques.
Data Science Overview
9 videos | 43m
Data science differentiates itself from statistics and application programming by using what it needs from a variety of disciplines. Explore what it means to be a data scientist and what sets data science apart from other disciplines.
Python - Introduction to Pandas and DataFrames
14 videos | 1h 10m
Simplify data analysis with Pandas DataFrames. Pandas is a Python library that enables you to work with series and tabular data, including initialization, and population. For this course, learners do not need prior experience working with Pandas, but should be familiar with Python3, and Jupyter Notebooks. Topics include the following: Define your own index for a Pandas series object; load data from a CSV (comma separated values) file, to create a Pandas DataFrame; Add and remove data from your Pandas DataFrame; Analyze a portion of your DataFrame; Examine how to reshape or reorient data, and to create a pivot table. Finally, represent multidimensional data in two-dimensional DataFrames, with multi or hierarchical indexes.
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