## Data Science Real World Projects in Python

### What you’ll learn

Hands on Real-World Projects on Various Domains of Data Science in Machine Learning, Natural Language Processing , Time Series Analysis

Develop Natural Language Processing Models for Customer Sentiments

Develop time series forecasting models to predict Prices of stocks

Learn how to map your Problem into Data Science problem

Learn best practices for real-world data sets.

Data Science Capstone Projects

### Requirements

Basic knowledge of programming is recommended. However, You can follow my Basics of Python Course which is free of cost therefore, the course has no prerequisites, and is open to anyone with basic programming knowledge. Students who enroll in this course will master data science and directly apply these skills to solve real world challenging business problems.

### Description

Check out what Other says :He is very aweome!, He explanied very well all over the concepts and nice teaching and execution, thankyou so much for this aweomse course. Thanks a lot! – Aravindan RPractical Utility of the learning experience has been well taken care of-which ensures the course to become an interesting one! – Sangita BhadraGreat Job….I Learnt a lot….I recommend this course for anyone reading this…..I will also urge those who have taking any form of introductory course in machine learning….do yourself a favour and get this course….extremely helpful – Adesan Orire NewmanAre you looking to land a top-paying job in Data Science?Or are you a seasoned AI practitioner who want to take your Data Science career to the next level?Or are you an aspiring data scientist who wants to get Hands-on Data Science and Artificial Intelligence?If the answer is yes to any of these questions, then this course is for you !Data Science is one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Data Science is widely adopted in many sectors nowadays such as banking, healthcare, Airlines, Logistic and technology.The purpose of this course is to provide you with knowledge of key aspects of data science applications in business in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets.1.Task #1 @Predict Price of Airlines Industry : Develop an Machine Learning model to predict Fare of Airlines at various Routes.2.Task #2 @Predict the strength of a Password: Predict the category of Password whether it is Strong, Good or Weak.3.Task #3 @Predict Prices of a Stock: Develop time series forecasting models to predict future Stock prices.In this course you will experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities – you name it!This course will give you a full overview of the Data Science journey , Once u complete this course you will know:How to collect , clean and prepare your data for analysisHow to perform basic & advance visualisation of your dataHow to perform Data ModellingHow to curve-fit your dataAnd finally, how to present your findings and wow the audienceWhy should you take this Course?It explains Projects on real Data and real-world Problems. No toy data! This is the simplest & best way to become a Data Scientist/Data Analyst/ ML EngineerIt shows and explains the full real-world Data. Starting with importing messy data , cleaning data, merging and concatenating data, grouping and aggregating data, Exploratory Data Analysis through to preparing and processing data for Statistics, Machine Learning , NLP & Time Series and Data Presentation.It gives you plenty of opportunities to practice and code on your own. Learning by doing.In real-world projects, coding and the business side of things are equally important. This is probably the only course that teaches both: in-depth Python Coding and Big-Picture Thinking like How you can come up with a conclusionGuaranteed Satisfaction: Otherwise, get your money back with 30-Days-Money-Back-Guarantee..

### Overview

Section 1: Introduction to this course

Lecture 1 Intro to this course

Lecture 2 Utilize QnA of the course ( Golden Oppurtunity ) !

Lecture 3 Installation of Anaconda Navigator

Lecture 4 Quick Summary of Jupyter Notebook

Section 2: Introduction to Data Science

Lecture 5 Data Science & its Applications

Lecture 6 Life-cycle of data science project in Real World

Section 3: Project 1–>> Predict Fare of Airlines Tickets using Machine Learning

Lecture 7 Introduction to Business Problem & Dataset

Lecture 8 Datasets & Resources

Lecture 9 Lets read our data !

Lecture 10 Perform data-preprocessing & extract derived Features .

Lecture 11 Perform data Cleaning & Featurization .

Lecture 12 Lets Perform Data Analysis

Lecture 13 Perform Data Pre-processing on Duration Feature.

Lecture 14 Analyse whether Duration impacts Price or not ?

Lecture 15 Lets Perform Bi-variate Analysis !

Lecture 16 Applying one-hot Encoding on data ( feature Encoding)

Lecture 17 Applying target guided encoding on data..

Lecture 18 How to handle Outliers in data.

Lecture 19 Select Best features using Feature Selection !

Lecture 20 Intuition Behind Random Forest Part-1

Lecture 21 Intuition Behind Random Forest Part-2

Lecture 22 Applying Machine Learning algorithm on data.

Lecture 23 Intuition Behind Decision Tree- Part 1

Lecture 24 Intuition Behind Decision Tree- Part 2

Lecture 25 Intuition Behind Decision Tree- Part 3

Lecture 26 Intuition Behind Decision Tree- Part 4

Lecture 27 Intuition Behind Decision Tree- Part 5

Lecture 28 Intuition Behind Decision Tree- Part 6

Lecture 29 Intuition Behind Linear Regression- Part 1

Lecture 30 Intuition Behind Linear Regression- Part 2

Lecture 31 Intuition Behind Linear Regression- Part 3

Lecture 32 Intuition Behind KNN- Part 1

Lecture 33 Intuition Behind KNN- Part 2

Lecture 34 Intuition Behind KNN- Part 3

Lecture 35 Intuition Behind KNN- Part 4

Lecture 36 How to automate Machine Learning pipeline

Lecture 37 Intuition Behind Cross Validation- Part 1

Lecture 38 Intuition Behind Cross Validation- Part 2

Lecture 39 How to hypertune Machine Learning model..

Section 4: Project 2–>> Predict Password Strength using Natural Language Processing

Lecture 40 Introduction to Business Problem & Dataset

Lecture 41 Datasets & Resources

Lecture 42 Exploring your data

Lecture 43 Intuition behind TF-IDF –part 1

Lecture 44 Intuition behind TF-IDF –part 2

Lecture 45 Apply TF-IDF on data

Lecture 46 Intuition behind Logistic Regression –part 1

Lecture 47 Intuition behind Logistic Regression –part 2

Lecture 48 Apply Machine Learning algorithm on Data

Lecture 49 Checking Accuracy of Model

Section 5: Project 3–>> Predict Stock Prices using Time Series Analysis

Lecture 50 Introduction to Business Problem & Dataset

Lecture 51 Datasets & Resources

Lecture 52 Analyzing Time Series data

Lecture 53 Data preparation for Time Series Forecasting

Lecture 54 Intuition behind ARIMA –part 1

Lecture 55 Intuition behind MA model –ARIMA part 2

Lecture 56 Intuition behind AR model — ARIMA part 3

Lecture 57 Intuition behind Integrating — ARIMA part 4

Lecture 58 Applying Auto-Arima(Time Series algorithm) on data

Lecture 59 Evaluating Time Series Model

Section 6: Bonus Section

Lecture 60 Bonus Session

One who is curious about Data Science, AI, Machine Learning, Natural Language Processing, Time Series Analysis..

#### Course Information:

Udemy | English | 9h 15m | 3.09 GB

Created by: Shan Singh

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