Pragmatic Machine Learning

Course for Software Engineers

Duration

3 Days

Level

Intermediate Level

Design and Tailor this course

As per your team needs

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The course is a stepping stone to initiate your journey to become a Data Scientist/Machine Learning Expert. The course starts with a quick introduction to Data Science, ML, AI and Big Data. Initially it provides a high level overview about the Data Science Lifecycle then it starts to delve deeper into hands-on aspects of the DataScience Process. The course provides a pragmatic introduction to various steps involved in DataScience Process including Data ingestion, munging, exploratory data analysis, modeling, optimization etc. The intention is to enable participants how to solve problems, define strategy and uncover hidden needs.

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This program is designed for those who aspire for Data/ML/AI roles:

Software Engineers
Data Scientists
Machine Learning Engineers
Data Integration Engineers
Data Architects

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Understanding the Big Picture

Significance of Data
What is Machine Learning (ML)?
Practical Use cases
Concepts and Terms
Tools/Platforms for ML
Machine Learning End to End Pipeline
Data Science Process
Roles and Responsibilities of Data Engineer and Data Scientist

Environment for Experiments

Installing Anaconda
Setting up Jupyter Notebook
Experiencing Notebooks
Introduction to Google Colab
Hands-on Exercise(s)

Laying the Foundation: Understand Basic Statistics

Working with NumPy Array
NumPy Arrays Compared to Python Lists
Manipulating Arrays
Hands-on Exercise(s)

Lego of Machine Learning: NumPy

Data Acquisition

Acquisition Approaches, Pros & Cons
Working with Beautiful Soup
Acquiring data using Twitter Streaming APIs
Connecting to External data sources
Hands-on Exercise(s)

Storytelling with Data

Why is Storytelling important?
How to share stories?
Key types of plots
Demo: Exploratory Analysis using MatPlot Lob
Hands-on Exercises
Introduction to Seaborn
Demo: Working with Seaborn
Hands-on Exercise(s)

Working with Pandas

Key Tools for Data Manipulation
Basic types – Series and DataFrames
Working with a Series
Dataframe Operations
Creating a DataFrame from various sources
Data Manipulation using Pandas
Joining datasets
Hands-on Exercise(s)

Data Preparation

How to make data useful for Machine Learning?
Exploratory Data Analysis
Data Cleaning techniques
- Add default values
- Remove incomplete rows
- Deal with error-prone columns
- Dealing with missing data
Data Preparation for ML
- Normalize data types
- Feature Scaling
- Feature Standardization
- Label Encoding
- One-Hot Encoding
Hands-on Exercise(s)

Feature Engineering

What is Feature Engineering?
Why Feature Engineering?
How to apply Feature Engineering?
Discussions on various scenarios
Hands-on Exercise(s)

Machine Learning Concepts

Types of Machine Learning
Key Algorithms in Machine Learning
Concepts and Terms
Why Scikit Learn?
Gradient Descent
Loss function
Bias vs Variance Tradeoff
Model Interpretability
Hands-on Exercise(s)

Model Evaluation Metric

Accuracy
Evaluation Metric for imbalanced datasets
- Precision
- Recall
Confusion Matrix
Regression Metrics

Supervised Machine Learning

Strategies for Splitting Data
Key Classification Algorithms
Conditional Probability
Proof of Bayes Theorem
Naïve Bayes Classifier
Linear and Logistic Regressions
Decision Trees
Hyper Parameter Tuning
Hands-on Exercise(s)

Un-Supervised Machine Learning

Key types of Unsupervised ML
Performing Clustering of data
Principal Component Analysis
Hands-on Exercise(s)

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Participants should preferably have some hands-on experience in programming language. Knowledge of Python would be a plus.

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Pragmatic Machine Learning

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