Just Enough Machine Learning

Ramp up yourself on Machine Learning in shortest possible time

Duration

3 Days

Level

Basic Level

Design and Tailor this course

As per your team needs

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This course has been designed and developed for providing a quick background in making data useful for Machine learning in a pragmatic way. It provides hands-on experience in Data Manipulation, Processing, Feature Engineering, Handling Missing Data, Outliers Detection, Data Encoding, Data Skew,  Analysis and Modeling using Python programming language. The course also focuses on Machine Learning Pipeline and on a few Machine Learning algorithms and their implementations.

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  • Course Introduction
  • Significance of Data
  • What is Machine Learning (ML)?
  • Practical Use cases
  • Concepts and Terms
  • Tools/Platforms for ML
  • Machine Learning End to End Pipeline
  • Who is Data Engineer and Data Scientist
  • What does a Data Scientist do?
  • Module Recap
  • Module Introduction
  • Introduction to Pandas
  • Data Manipulation using Pandas
  • Introduction to Numpy
  • Data Manipulation using Numpy
  • Module Recap
  • Module Introduction
  • What is Feature Engineering?
  • Exploring Data using DataFrames
  • Dealing with High Cardinality Data
  • Working with Feature Engineering
  • Filtering & Sorting Data
  • Pandas De-Duplication Dataframes
  • Module Recap
  • Module Introduction
  • What is Data Normalization?
  • Data Normalization Concepts
  • Key techniques of Data Normalization
  • Working with Data Normalization
  • Missing Data Handling
  • Working with Missing Data Handling
  • Module Recap
  • Introduction to Plotly Express
  • High-Level Features
  • Scatter, Line & Bar Charts
  • Layout, Styling & Useful Parameters
  • Module Exercises
  • Module Introduction
  • Introduction to Outliers
  • Possible Outlier Treatment
  • Way to detect Outliers
  • IQR Concepts
  • Outlier Detection using IQR Approach
  • Data Encoding Concepts
  • Working with Data Encoding
  • Introduction to Class Imbalance Problem
  • How to handle Class Imbalance
  • Module Recap
  • Module Introduction
  • Introduction to Data Skew
  • Working with Data Skew
  • What is Column Pruning?
  • Merging DataFrames
  • Slicing DataFrames
  • De-Duplicating DataFrames
  • Module Recap
  • Module Introduction
  • Exploring Data Pipeline
  • Working with Data Pipeline
  • Exploring GroupBy option
  • Working on Data Analysis
  • Module Recap
  • Module Introduction
  • Key Classification Algorithms
  • Conditional Probability 
  • Naïve Bayes Classifier
  • Regression and its key types
  • Linear, Logistic and Other Key types of Regressions
  • Pros and Cons of Linear Regression
  • Working with Linear Regression
  • Bias vs Variance Tradeoff
  • Key types of Unsupervised ML
  • Principal Component Analysis
  • Performing Clustering of data
  • Module Recap
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Attendees should preferably have basic knowledge of Unix commands and Basic Programming Knowledge of Python.

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