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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Introduction to Machine Learning Realm

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

Data Manipulation

Module Introduction
Introduction to Pandas
Data Manipulation using Pandas
Introduction to Numpy
Data Manipulation using Numpy
Module Recap

Feature Engineering and Filtering & Sorting

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

Data Normalization & Handling Missing Data

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

Exploratory Data Analysis using Plotly Express

Introduction to Plotly Express
High-Level Features
Scatter, Line & Bar Charts
Layout, Styling & Useful Parameters
Module Exercises

Outlier Detection, Data Encoding, and Class Imbalance problem

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

Data Skew, Column Pruning, and Altering DataFrames

Module Introduction
Introduction to Data Skew
Working with Data Skew
What is Column Pruning?
Merging DataFrames
Slicing DataFrames
De-Duplicating DataFrames
Module Recap

Data Pipeline and Analysis

Module Introduction
Exploring Data Pipeline
Working with Data Pipeline
Exploring GroupBy option
Working on Data Analysis
Module Recap

Supervised and Unsupervised Machine Learning

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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