Introduction to Machine Learning Basics
- Created By raju2006
- Last Updated December 20th, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
Embark on your journey into the world of machine learning basics with our "Introduction to Machine Learning" course. Explore how machine learning algorithms autonomously extract information from data, free from predetermined equations. Gain insight into real-world machine learning patterns and use cases, enhancing your data-mindedness through statistics and data analytics. This course demystifies the data science process and equips you with essential Python libraries like Numpy, Pandas, Matplotlib, and Scikit-learn. Set up Anaconda and dive into hands-on labs using Jupyter Notebooks, igniting your passion for machine learning fundamentals. Join us and lay the foundation for your machine learning expertise.
Course Code/Duration:
BDT3 / 1 Day
Learning Objectives:
After this course, you will be able to:
- Describe Supervised and Unsupervised learning techniques and usages
- Understand techniques like Classification, Clustering and Regression
- Discuss how to identify which kinds of technique to be applied for specific use case
- Understand the popular Machine offerings like Amazon Machine Learning, TensorFlow, Azure Machine Learning, Spark mlib, Python and R etc.
- Install and Setup Anaconda.
- Perform hands-on activity using Jupyter Notebooks.
Pre-requisite:
- 1) Basic Programming knowledge preferred
- 2) Please take a few moments to complete this pre-training questionnaire to help us understand your training needs https://forms.gle/yfGSRPisTsyJeAnQ8
- 3) We would be using Jupyter Notebooks (Python) through Anaconda Navigator. Please install Anaconda (Python version 3.x) using below link https://www.anaconda.com/distribution/
- 4) Refresh your Python basic skills watching the video below (Python in 43 minutes). The Prerequisite for this class is Basic Python Programming training or equivalent experience.That said, in the past batches people without much coding background have been able to get value out of this course and are very satisfied.
- Developers, Business Analysts who want to start a career in or want to learn about the exciting domain of Data Science and Machine Learning, Non-technical professionals who want to start a career in Machine Learning.
- Developers, Business Analysts who want to start a career in or want to learn about the exciting domain of Data Science and Machine Learning, Non-technical professionals who want to start a career in Machine Learning.
Course Outline:
- Course Introduction
- Machine Learning patterns
- Classification
- Clustering
- Regression
- Gartner Hype Cycle for Emerging Technologies
- Machine Learningofferings in Industry
- Hands-on exercise 1 – Install and Setup Anaconda.
- Python Libraries
- NumPy
- Pandas
- Scikit Learn
- Hands-on exercise 2 – Data Analysis using Pandas
- Algorithms
- Linear Regression
- Decision Tree
- Hands-on exercise 3 – Perform Linear regression using Scikit-learn
- References and Next steps
Structured Activity/Exercises/Case Studies:
- Exercise 1 – Install and Setup Anaconda
- Exercise 2 – Data Analysis using Pandas
- Exercise 3 – Perform Linear regression using Scikit-learn
- Exercise 4 – Perform Decision tree on Titanic Data set using Scikit-learn
Training material provided:
Yes (Digital format)
