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The Complete Data Science Bundle (Bundle)

Give your career a new dimension with complete Python course. Create your own Machine Learning application. This is a detailed Hands-on Data Science course with Python with Capstone Projects from various domains. This course is suitable for all levels of learners from beginners to advanced students.

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Designed by the OK Google Developer, this course will help you master the most popular and highly-demanded programming language Python along with Data Structures and Machine Learning. Create your own Machine Learning applications by the end of this course. 

This course includes important Data Science topics such as  Data Collection, Data Visualization, Data Preprocessing, Machine Learning.

In this course you will learn the basics of Python Programming required for Data Science and Important Python Libraries required for Data Science.


After completing these modules you will learn how to use your knowledge of Data Science in Projects. This course includes Capstone Projects including Building a Movie Recommendation System, Cancer Prediction, etc.

WHAT YOU’LL LEARN

Importance of Python

Describe the basics of the Python programming language

Install Python and write your first program

Use variables and constants to store, retrieve and calculate information

Utilize core programming tools such as functions and loops

Accomplish multi-step tasks like sorting or looping using tuples

Create programs that are able to read and write data from files

Store data as key/value pairs using Python dictionaries

Start with basics of Object Oriented Programming

Understanding class and object

Encapsulation, abstraction, inheritance and polymorphism

Using Scikit Learn in Machine Learning Techniques like Regression, Classification, Clustering, Recommender Systems

Doing basic project in Python

Select and process the data

Process data using Graphs Library in Python

Creating capstone project on Machine Learning using TensorFlow in Python

Usage of Python Libraries

Live Classes

Machine Learning

Python Certification

  • Introduction to the course
  • Need of Programming: What it takes to become a top notch programmer
  • Introduction to Python: Why it is the most demanded language and how tech giants are using it
  • Introduction to Python: Transition from Python 2 to Python 3

  • Instructions: Installation of Python on Windows and Mac
  • Demo: Step-by-step guide to installing Python on your PC
  • Python Usage: Introduction to practical use case of Python
  • Python Online: How to use your Machine to code online

  • Build your 1st Python code: Hello world assignment

  • Constants in Python: Introduction, application and use cases
  • Variable Reserved Words in Python: Introduction, application and use cases
  • Data Types in Python
  • Conversions in Python: Type and string conversions
  • Taking user inputs in Python: Introduction and application
  • Module Quiz: Constant Resever Words Variables
  • Module Assignment : Expressions and User Input

  • Using Comparison Operator in Python
  • Using IF ELIF Conditions in Python
  • Demo: Using conditions in Python
  • Using EXCEPT in Python
  • Demo: Using EXCEPT in Python
  • Module Quiz: Conditions and Comparisons
  • Module Assignment: TRY EXCEPT Assignment
  • Module Assignment Solution:TRY EXCEPT in Python

  • Understanding Functions in Python
  • Understanding Functions with Arguments in Python
  • Return Functions: Introduction and use cases
  • Multiple Arguments in Python: Introduction and use cases
  • Module Quiz: Functions in Python
  • Module Assignment: Functions in Python
  • Module Assignment Solution: Functions in Python

  • Loops in Python: Introduction
  • Break Continue: Understanding and Use Cases
  • FOR Loop: Understanding and Use Cases
  • Demo: Loops Code Implmentation
  • Loops and interation
  • Demo: Mastering Loops with more Use Cases
  • Module Quiz: Loops in Python
  • Module Assignment: Loops in Python
  • Module Assignment Solution: Loops in Python

  • Data Structures in Python: Course Introduction
  • Machine Learning in Python: Course Introduction

  • Python Interview Questions: Top 15 Python Programming Questions
  • Social Media Links for references
Data Structures in Python

  • Introduction Video: Data Structures Course
  • Introduction to Strings and LEN function
  • String Looping: Introduction and Use Cases
  • String Slicing: Introduction and Use Cases
  • String Library Function: Introduction and Use Cases
  • Stripping Whitespace: Introduction and Use Cases
  • PARSING EXTRACTING
  • Module Quiz: Strings in Python
  • Module Assignment: Strings in Python
  • Module Assignment Solution: Strings in Python

  • Introduction to Files in Python
  • Counting Lines in a File in Python: Introduction and use cases
  • Reading files in Python: Introduction and use cases
  • Searching Lines in a File in Python: Introduction and use cases
  • Naming Files with User Data: Introduction and use cases
  • Module Quiz: Files in Python
  • Module Assignment: Handling files in Python
  • Module Assignment Solution: Handling files in Python

  • Introduction to Lists in Python
  • Finding Length of Range Objects in Python: len()
  • Continuation of lists and their use cases
  • Calculating Sum and Average of Lists in Python
  • Spliting Strings in to Lists in Python: split()
  • Moduel Quiz: Lists
  • Module Assignment: Lists in Python
  • Module Assignment Solution: Lists in Python

  • Understanding Dictionaries in Python
  • Distinguishing Lists and Dictionaries in Python: Lists Vs. Dictionaries
  • Demo: Understanding Get Function in Python
  • Iterating over dictionaries with loops
  • Module Quiz: Dictionaries in Python
  • Module Assignment: Dictionaries in Python
  • Module Assignment Solution: Dictionaries in Python

  • Understanding Tuples in Python
  • Distinguishing Tuples and Dictionaries in Python: Tuples Vs. Dictionaries
  • Sorting a Tuple in Python
  • Module Quiz: Tuples
  • Module Assignment: Tuples in Python
  • Module Assignment Solution: Tuples in Python

  • Uderstanding Sets in Python
  • Creating Sets in Python
  • Creating Empty Dictionaries in Python: Empty Set
  • Understanding Add and Update in Sets
  • Deleting Elements of Sets in Python
  • Distinguishing Sets, Lists and Dictionaries in Python: Sets Vs. Lists Vs. Dictionaries
  • Python Set Operations (Union, Intersection, Difference and Symmetric Difference)
  • Understanding Python Set Methods
  • Module Quiz: Sets in Python
  • Module Assignment: Python Sets

  • Machine Learning: Introduction
  • Social Media Links for references

  • Data Strcuture Interview Questions: Top Data Structure Programming Questions
  • Implementing Stacks and Queues
  • Searching, Sorting and Complexity Analysis in Data Structures
Object Oriented Programming

  • Understanding programming concepts in relation to OOPs
  • Distinguishing Procedural and OOPs: Procedural Programming Vs. Object Oriented Programming
  • Understanding Objects in OOPs
  • Understanding Classes in OOPs
  • Demo: Objects and Classes in OOPs

  • Understanding Constructors
  • Understanding Types of Constructors

  • Introduction to Encapsulation and Abstraction in Python
  • Use Cases of Encapsulation and Abstraction in Python

  • Understanding Inheritance in Python
  • Demo: Use Cases of Inheritance
  • Understanding Polymorphism in Python
  • Demo: Use Cases of Polymorphism
  • Various Functions in Inheritance and Polymorphism

  • Module Quiz: OOPs
  • Module Assignment : OOPs

  • Project: Red Wine - Predicting Qualtiy of Wines
  • Dealing with Missing Values in Python
  • Understanding Machine Learning: Use Case Scenarios
  • Project: Medical Cost Prediction
  • Python Libraries Guides
  • Matplotlib: Creating Static, Animated, and Interactive visualizations in Python
  • Pandas: Analysing Data in Python
  • *Args And **Kwargs: Passing a variable number of arguments to a Python function
  • NumPy: Working with Arrays in Python
  • Sets in Python
  • Python OOPs Concepts
  • Exploratory Data Analysis aka EDA on Red Wine Data - Part 2
  • Exploratory Data Analysis aka EDA on Red Wine Data - Part 1
  • Python Test Quiz
  • Tutorial - Seaboar
  • Python Projects - Stop Watch
  • H2O Tutorial - Machine Learning Framework
  • Various Types of Data Distribution
  • Various Types of Errors in Machine Learning Models
Machine Learning in Python

  • Course Walkthrough
  • Overview to Machine Learning
  • Artificial Intelligence Vs Machine Learning Vs Deep Learning
  • Machine Learning: Python Libraries
  • Supervised Vs. Unsupervised Learning
  • Module Quiz: Machine Learning Overview

  • Introduction to Regression
  • Various Regression Techniques: Linear, Polynomial, Stepwise, Ridge, Lasso and ElasticNet
  • Linear Regression using Python
  • Linear Regression Techniques: Slopes And Intercept
  • Linear Regression using SKLEARN Coding
  • Demo Machine Code: Linear Regression using SKLEARN Coding
  • Demo Code Explaination: Linear Regression using SKLEARN Coding
  • Errors in Linear Techniques
  • R-Squared or Coefficient of Determination
  • Multiple Linear Regression (MLR)
  • Polynomial Vs. Non Linear Regression
  • Module Quiz: Regression in Python

  • Introduction to Classification
  • K-Nearest Neighbor(KNN) Algorithm for Machine Learning Introduction
  • Working with K-Nearest Neighbor(KNN) Algorithm for Machine Learning
  • Evaluation Metrics for Classification
  • Machine Learning Decision Tree Classification Algorithm
  • Support Vector Machine (SVM) Algorithm in Machine Learning
  • Machine Learning Random Forest Algorithm
  • Module Quiz: Classification

  • Clustering Algorithms in Machine Learning
  • Clustering Vs Classifications: Machine Learning
  • K means Clustering - Introduction
  • Module Quiz: Clusterin in Machine Learning

  • Understanding Recommender Systems in Machine Learning
  • Content-based Filtering: Recommendation Systems
  • Collaborative Filtering: Recommendation Systems
  • Module Quiz: Recommender Systems in Machine Learning

  • Introduction to the Project: Guess Game
  • Project Implementation: Undestanding the Concepts to Build Guess Game
  • Proejct Code: Guess Game Using Python

  • Understanding Python Libraries
  • Three-dimensional Plotting in Python using Matplotlib
  • Tri-Surface Plot in Python using Matplotli
  • Professional 3D Plotting in Matplotlib - 3D Plot Game

  • Understanding Machine Learning Concepts
  • Flowcharts - Problem Solving with Python
  • Deep Learning: Training and Testing the Network with Tensorflow and Keras
  • Tensorflow and Keras For Neural Networks and Deep Learning
  • Final Certification Quiz; Complete Course
  • Machine Learning Final Assignment

  • Machine Learning Live Class with Google Expert

  • Logistic Regression for Malignancy Prediction in Cancer (Breast Cancer)
  • Spam Mail Prediction using Machine Learning with Python
  • Personal Assistant
  • Weather recommendation system

  • Understanding Machine Learning Workflow
  • Decoding Model Selection for Machine Learning
  • Understanding Overfitting in Machine Learning
  • Understanding Underfitting in Machine Learning
  • Understanding the Bias-Variance Tradeoff
  • Introduction to Dimensionality Reduction for Machine Learning
  • Gradient Descent algorithm and its Variants

  • Hands-On-Guide To Machine Learning Model Deployment Using Flask

  • GitHub Tutorial and Profile Buidling
Complete Data Science Course with Python

  • Course Introduction
  • Course Curriculum

  • Overview of the steps involved
  • Understanding the problem statement
  • Data Collection
  • Data Visualization and Analysis
  • Data Preprocessing - Part 1
  • Data Preprocessing - Part 2
  • Model Selection
  • Evaluation
  • Presenting our Insights
  • Summary
  • Module 2 : Steps involved in a Data Science Project

  • Overview
  • Google Colaboratory - Getting Started
  • Google Colab - Basics

  • Python Basics - Synopsis
  • Python Basics
  • Basic Python Data Types
  • Basic Operators in Python
  • Special Data Types - list
  • Tuple, Set and Dictionary
  • If Statements
  • Loops
  • Module 4 : Python Basics
  • Assignment 1 - Python basics

  • Overview
  • Numpy Basics Part - 1
  • Numpy Basics Part - 2
  • Numpy Basics - Part 3
  • Pandas Basics - Part 1
  • Pandas Basics - Part 2
  • Other important Python Libraries
  • Module 5 : Numpy and Pandas Tutotial
  • Assignment 2 - Pandas and Numpy Tutorial

  • Data Collection - Overview
  • Getting the dataset - part 1
  • Getting the dataset - part 2
  • Importing Kaggle Dataset through API
  • Web Scrapping - Wikipedia Table
  • Summary
  • Assignment 3 - Data Collection

  • Data Visualization - Overview
  • Data Visualization - Example
  • Basic Plots - Matplotlib
  • Iris Dataset - Visualization
  • Titanic Dataset - Visualization
  • Module 7 : Data Visualization and Analysis
  • Assignment 4 - Data Visualization and Analysis

  • Data Preprocessing - Overview
  • Dataset Manipulation - Basics
  • Handling missing values - by Dropping
  • Handling missing values - by Imputation
  • Dataset Standardization
  • Label Encoding
  • Module 8: Data Pre-processing
  • Assignment 5 - Data pre-processing

  • Overview
  • AI vs ML vs DL
  • Machine Learning
  • Types of Machine Learning
  • Model Selection
  • 9.6. Logistic Regression model
  • Support Vector Machine model
  • Module 9 : Machine Learning - Basics

  • Data Science Projects - Overview
  • Breast Cancer Prediction - Overview
  • Breast Cancer Prediction - Logistic Regression - Part 1
  • Breast Cancer Prediction - Part 2
  • Iris Clustering
  • Spam mail prediction
  • Movie Recommendation System - Overview
  • Movie Recommendation System - Part 1
  • Movie Recommendation System - Part 2
  • Assignment 6 - Data Science projects
Resume Building

  • Introduction - Overleaf, Online LaTeX Editor
  • Building Professional/Educational Resume using LaTeX

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We at Perfect Plan B are working towards eradicating the financial hurdles from education with our Learn Now, Pay Later initiative. This course will not only help you in getting industry specific knowledge but will also help you achieve your career goals with right training and mentoring from P2B team.

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The Complete Data Science Bundle

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  • 272 Lessons
  • 27:34:05 Hours On demand videos
  • Lifetime Video Access
  • Certificate of Completion
  • Approx. 3 Months to Complete
  • Live Classes
  • Quizzes and Assignments
  • No Prior Experience Required
  • Top 50 Interview Question
  • Resume Building
  • Internships and Placement