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Data Science and Machine Learning using Python - A Bootcamp

Data Science and Machine Learning using Python – A Bootcamp

4.8

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Course Highlights

Understand the exciting field of data science and machine learning with our all-inclusive bootcamp, “Data Science and Machine Learning using Python.” This comprehensive course is designed for experts and aspiring data enthusiasts who want to use Python for machine learning, data analysis, and visualisation. Starting with a strong foundation in Python fundamentals, the journey guarantees that you have the coding skills needed for the fascinating modules that come next.

The course unfolds with an exploration of Python libraries such as NumPy and Pandas, enabling you to perform robust data analysis. You’ll learn to extract valuable insights from datasets, manipulate information efficiently, and handle complex data structures. You will become proficient with Matplotlib, Seaborn, and Plotly as you move smoothly into the field of data visualisation and produce eye-catching visual representations of data.

As you progress, your concentration shifts to understanding machine learning and the intricate algorithms that support intelligent systems. Models like as PCA, K Means Clustering, Decision Trees, Random Forests, SVMs, Linear Regression, and Logistic Regression may all be implemented with Scikit-Learn as a toolkit. Beyond the fundamentals, the course explores further subjects such as Recommender Systems and Natural Language Processing with NLTK. Upon completion, you will possess a diverse skill set that encompasses data manipulation and predictive model creation, equipping you for positions like data scientist, machine learning engineer, or data analyst in the rapidly changing data field.

This program is more than just education; it’s about changing how you see data. Whether you want to progress in your career or start a new one, this training offers opportunities in data-driven decision-making. Come join us and explore the thrilling connections between Data Science and Machine Learning.

Learning outcome

Master Python programming for data analysis and visualization.
Understand essential machine learning algorithms and their applications.
Develop skills in implementing machine learning models using Scikit-Learn.
Apply Python libraries for data manipulation, exploration, and visualization.
Gain skills through a Capstone Project and additional topics like NLP and Recommender Systems.

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Why should I take this course?

Acquire essential skills in Data Science and Machine Learning.
Python programming and its libraries with projects.
Enhance your career prospects in data-driven industries.
Build a strong foundation in machine learning algorithms.
Develop proficiency in data analysis, visualization, and model implementation.

Career Path

Requirements

Curriculum

Module 01: Welcome, Course Introduction & Overview, And Environment Set-Up

Welcome & Course Overview

00:07:00
Set-up the Environment for the Course (lecture 1)

00:09:00
Set-up the Environment for the Course (lecture 2)

00:25:00
Two other options to setup environment

00:04:00

Module 02: Python Essentials

Python data types Part 1

00:21:00
Python Data Types Part 2

00:15:00
Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1)

00:16:00
Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2)

00:20:00
Python Essentials Exercises Overview

00:02:00
Python Essentials Exercises Solutions

00:22:00

Module 03: Python For Data Analysis Using NumPy

What is Numpy? A brief introduction and installation instructions.

00:03:00
NumPy Essentials – NumPy arrays, built-in methods, array methods and attributes.

00:28:00
NumPy Essentials – Indexing, slicing, broadcasting & boolean masking

00:26:00
NumPy Essentials – Arithmetic Operations & Universal Functions

00:07:00
NumPy Essentials Exercises Overview

00:02:00
NumPy Essentials Exercises Solutions

00:25:00

Module 04: Python For Data Analysis Using Pandas

What is pandas? A brief introduction and installation instructions.

00:02:00
Pandas Introduction

00:02:00
Pandas Essentials – Pandas Data Structures – Series

00:20:00
Pandas Essentials – Pandas Data Structures – DataFrame

00:30:00
Pandas Essentials – Handling Missing Data

00:12:00
Pandas Essentials – Data Wrangling – Combining, merging, joining

00:20:00
Pandas Essentials – Groupby

00:10:00
Pandas Essentials – Useful Methods and Operations

00:26:00
Pandas Essentials – Project 1 (Overview) Customer Purchases Data

00:08:00
Pandas Essentials – Project 1 (Solutions) Customer Purchases Data

00:31:00
Pandas Essentials – Project 2 (Overview) Chicago Payroll Data

00:04:00
Pandas Essentials – Project 2 (Solutions Part 1) Chicago Payroll Data

00:18:00

Module 05: Python For Data Visualization Using Matplotlib

Matplotlib Essentials (Part 1) – Basic Plotting & Object Oriented Approach

00:13:00
Matplotlib Essentials (Part 2) – Basic Plotting & Object Oriented Approach

00:22:00
Matplotlib Essentials (Part 3) – Basic Plotting & Object Oriented Approach

00:22:00
Matplotlib Essentials – Exercises Overview

00:06:00
Matplotlib Essentials – Exercises Solutions

00:21:00

Module 06: Python For Data Visualization Using Seaborn

Seaborn – Introduction & Installation

00:04:00
Seaborn – Distribution Plots

00:25:00
Seaborn – Categorical Plots (Part 1)

00:21:00
Seaborn – Categorical Plots (Part 2)

00:16:00
Seborn-Axis Grids

00:25:00
Seaborn – Matrix Plots

00:13:00
Seaborn – Regression Plots

00:11:00
Seaborn – Controlling Figure Aesthetics

00:10:00
Seaborn – Exercises Overview

00:04:00
Seaborn – Exercise Solutions

00:19:00

Module 07: Python For Data Visualization Using Pandas

Pandas Built-in Data Visualization

00:34:00
Pandas Data Visualization Exercises Overview

00:03:00
Panda Data Visualization Exercises Solutions

00:13:00

Module 08: Python For Interactive & Geographical Plotting Using Plotly And Cufflinks

Plotly & Cufflinks – Interactive & Geographical Plotting (Part 1)

00:19:00
Plotly & Cufflinks – Interactive & Geographical Plotting (Part 2)

00:14:00
Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Overview)

00:11:00
Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Solutions)

00:37:00

Module 09: Capstone Project - Python For Data Analysis & Visualization

Project 1 – Oil vs Banks Stock Price during recession (Overview)

00:15:00
Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 1)

00:18:00
Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 2)

00:18:00
Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 3)

00:17:00
Project 2 (Optional) – Emergency Calls from Montgomery County, PA (Overview)

00:03:00

Module 10: Python For Machine Learning (ML) - Scikit-Learn - Linear Regression Model

Introduction to ML – What, Why and Types…..

00:15:00
Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff

00:15:00
scikit-learn – Linear Regression Model – Hands-on (Part 1)

00:17:00
scikit-learn – Linear Regression Model Hands-on (Part 2)

00:19:00
Good to know! How to save and load your trained Machine Learning Model!

00:01:00
scikit-learn – Linear Regression Model (Insurance Data Project Overview)

00:08:00
scikit-learn – Linear Regression Model (Insurance Data Project Solutions)

00:30:00

Module 11: Python For Machine Learning - Scikit-Learn - Logistic Regression Model

Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc.

00:10:00
scikit-learn – Logistic Regression Model – Hands-on (Part 1)

00:17:00
scikit-learn – Logistic Regression Model – Hands-on (Part 2)

00:20:00
scikit-learn – Logistic Regression Model – Hands-on (Part 3)

00:11:00
scikit-learn – Logistic Regression Model – Hands-on (Project Overview)

00:05:00
scikit-learn – Logistic Regression Model – Hands-on (Project Solutions)

00:15:00

Module 12: Python For Machine Learning - Scikit-Learn - K Nearest Neighbors

Theory: K Nearest Neighbors, Curse of dimensionality ….

00:08:00
scikit-learn – K Nearest Neighbors – Hands-on

00:25:00
scikt-learn – K Nearest Neighbors (Project Overview)

00:04:00
scikit-learn – K Nearest Neighbors (Project Solutions)

00:14:00

Module 13: Python For Machine Learning - Scikit-Learn - Decision Tree And Random Forests

Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging….

00:18:00
scikit-learn – Decision Tree and Random Forests – Hands-on (Part 1)

00:19:00
scikit-learn – Decision Tree and Random Forests (Project Overview)

00:05:00
scikit-learn – Decision Tree and Random Forests (Project Solutions)

00:15:00

Module 14: Python For Machine Learning - Scikit-Learn -Support Vector Machines (SVMs)

Support Vector Machines (SVMs) – (Theory Lecture)

00:07:00
scikit-learn – Support Vector Machines – Hands-on (SVMs)

00:30:00
scikit-learn – Support Vector Machines (Project 1 Overview)

00:07:00
scikit-learn – Support Vector Machines (Project 1 Solutions)

00:20:00
scikit-learn – Support Vector Machines (Optional Project 2 – Overview)

00:02:00

Module 15: Python For Machine Learning - Scikit-Learn - K Means Clustering

Theory: K Means Clustering, Elbow method …..

00:11:00
scikit-learn – K Means Clustering – Hands-on

00:23:00
scikit-learn – K Means Clustering (Project Overview)

00:07:00
scikit-learn – K Means Clustering (Project Solutions)

00:22:00

Module 16: Python For Machine Learning - Scikit-Learn - Principal Component Analysis (PCA)

Theory: Principal Component Analysis (PCA)

00:09:00
scikit-learn – Principal Component Analysis (PCA) – Hands-on

00:22:00
scikit-learn – Principal Component Analysis (PCA) – (Project Overview)

00:02:00
scikit-learn – Principal Component Analysis (PCA) – (Project Solutions)

00:17:00

Module 17: Recommender Systems With Python - (Additional Topic)

Theory: Recommender Systems their Types and Importance

00:06:00
Python for Recommender Systems – Hands-on (Part 1)

00:18:00
Python for Recommender Systems – – Hands-on (Part 2)

00:19:00

Module 18: Python For Natural Language Processing (NLP) - NLTK - (Additional Topic)

Natural Language Processing (NLP) – (Theory Lecture)

00:13:00
NLTK – NLP-Challenges, Data Sources, Data Processing …..

00:13:00
NLTK – Feature Engineering and Text Preprocessing in Natural Language Processing

00:19:00
NLTK – NLP – Tokenization, Text Normalization, Vectorization, BoW….

00:19:00
NLTK – BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes …

00:13:00
NLTK – NLP – Pipeline feature to assemble several steps for cross-validation…

00:09:00