Full Stack Data Science - Python, ML and MLOps

About Course

Course Description:

Welcome to the world of data-driven decision-making with Python and Data Science. This comprehensive course is designed to equip you with the knowledge and skills to harness the power of Python for data analysis, machine learning, and more. Whether you’re an aspiring data scientist, analyst, or a professional looking to dive into data, this course offers a hands-on journey into the data-driven future.

Course Highlights:

Python Fundamentals: Master the fundamentals of Python programming, including data types, loops, and functions.
Data Analysis: Learn how to clean, transform, and analyze data using libraries like Pandas and NumPy.
Data Visualization: Create informative and captivating data visualizations using Matplotlib and Seaborn.
Machine Learning Basics: Explore the principles of machine learning and build predictive models.
Real-World Projects: Apply your skills to real data science projects and scenarios.

Course Content

1. Introduction to Python Programming

Course Introduction

00:00
Why Python for Data Science?

00:00
Python Data Types (Strings, Integers, Lists, Tuples, Dictionaries)

00:00
Control Structures: If-else, Loops (for, while)

00:00
Functions: Basics, Arguments, Return values

00:00

2. Data Analysis with Pandas and NumPy

3. Data Visualization with Matplotlib and Seaborn

Hackathon on Data Analysis and Visualization

4. Supervised Learning – Part 1

5. Data Science Process Walkthrough (Part 1)

6. Data Science Process Walkthrough (Part 2)

7. Supervised Learning – Part 2

Hackathon on Supervised Models
Linear Regression, Logistic Regression, Decision Tree

8. Advanced Supervised Learning – Part 1

9. Advanced Supervised Learning – Part 2

Hackathon on Ensemble Modelling

10. Unsupervised Learning

Hackathon on Customer Segmentation

11. Natural Language Processing (NLP)

12. Neural Networks

13. Machine Learning Operations (MLOps)

14. Model Deployment – Part 1

15. Capstone Project – End to End with Deployment

16. Final Conclusion

17. Bonus

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About Course

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Course Content

1. Introduction to Python Programming

Course Introduction

Why Python for Data Science?

Python Data Types (Strings, Integers, Lists, Tuples, Dictionaries)

Control Structures: If-else, Loops (for, while)

Functions: Basics, Arguments, Return values

2. Data Analysis with Pandas and NumPy

Introduction to Pandas and NumPy

Series and Data Frames

Data Importing and Exporting

Basic Data Cleaning, Filtering, and Transformations

Descriptive Statistics using NumPy

Data retrieval and insertion in Python from multiple databases

3. Data Visualization with Matplotlib and Seaborn

Basics of Data Visualization

Introduction to Matplotlib

Common Plots: Line, Bar, Scatter using Matplotlib

Introduction to Seaborn

Advanced Plots: Box, Violin, Pair plot with Seaborn

Hackathon on Data Analysis and Visualization

4. Supervised Learning – Part 1

Introduction to Supervised Learning

Deep Dive: Linear Regression with Practical Example

Deep Dive: Logistic Regression with Practical Example

5. Data Science Process Walkthrough (Part 1)

Overview of the Data Science Process

Data Gathering Techniques

Data Cleaning: Handling Missing Values, Duplicates etc.

Data Preprocessing: Scaling, Encoding

6. Data Science Process Walkthrough (Part 2)

Outlier Detection and Treatment

Feature Engineering and Selection

Train-Test Splitting

Introduction to Model Selection

Hyperparameter Tuning

Model Evaluation

7. Supervised Learning – Part 2

Deep Dive: Decision Trees

Hands-on Exercises and Evaluation

Hackathon on Supervised Models Linear Regression, Logistic Regression, Decision Tree

8. Advanced Supervised Learning – Part 1

Introduction to Ensemble Techniques

Deep Dive: Bagging and Boosting

Deep Dive: K-Nearest Neighbors (KNN)

9. Advanced Supervised Learning – Part 2

Support Vector Machines (SVM)

XGBOOST

CATBOOST

Light GBM (LGBM)

Hackathon on Ensemble Modelling

10. Unsupervised Learning

Introduction to Unsupervised Learning

Deep Dive: K-means Clustering

Deep Dive: Principal Component Analysis (PCA)

Hackathon on Customer Segmentation

11. Natural Language Processing (NLP)

NLP Intro – Preprocessing

Algorithmic Text Classification

NLP Deep Learning

12. Neural Networks

Overview of ANNs

Core components

Feedforward, Backpropagation

Hands-on Workshop

13. Machine Learning Operations (MLOps)

Overview of MLOps

Introduction to Kubeflow, MLflow

Basics of TFX (TensorFlow Extended)

14. Model Deployment – Part 1

Importance of Model Deployment

Model Deployment in AWS

Model Deployment in Google Cloud Platform

Model Deployment in Microsoft Azure

15. Capstone Project – End to End with Deployment

Introduction to the Capstone Project

Data Exploration to Deployment: A Comprehensive Exercise

Group Work and Reviews

Hackathon on Supervised Models
Linear Regression, Logistic Regression, Decision Tree