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What you'll learn
Learn the theory+ practical application of machine learning with 4 Real Projects
Harness The Power Of Anaconda/iPython For Practical Data Science
Carry Out Basic Data Pre-processing & Wrangling In Python
Learn to use NumPy for Numerical Data
All About Pandas
Data Visualization -How Data is Beautiful
Machine Learning - Regression -Understanding the concept of Machine Learning
END TO END ML Model with Deployment
Requirements
No Programming experience . You will learn everything you need to know
Be Able To Operate & Install Software On A Computer
Description
Are you ready to start your path to becoming a Data Scientist!
This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! With LIVE 4 STEP by STEP PROJECTS
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!
This course is designed for both bners with some programming experience or experienced developers looking to make the jump to Data Science!
This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! this is one of the most comprehensive course for data science and machine learning on Udemy!
We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning
***Topics***
Introduction with AI & Machine Learning
Use of Data in the world of AI
Why Python
Installing & Setting up Python on System
Simple Python Program
Python Revisit: Keywords, Data Types, Operators
Conditional/Loopirror Handling in Python
Comprehensions
Python User Defined Functions
Python Generators
Lambda Expressions
Python Modules: Usage and Installation
Understanding the OOP of Python
Python Numpy Arrays
Creating, Accessing, Manipulating Numpy Array
Numpy Data Types
Array Attributes
Data Operations
Arithmetic and Statistical Methods
Sort, Search, Count
File Handling with Numpy
The Series and DataFrame
Creating, Accessing, Manipulating Pandas Data
Series and DataFrame Attributes & Basic Functions
Iteration on Data
Statistical Functions; String Functions
Logical Indexing; Sorting & Reindexing
Meg, Joining & Concatenation of Data
Grouping Data
How Data is Beautiful
Visualization Libraries in Python
MATPLOTLIB PYPLOT: line, scatter, pie, box, area, etc
Decorating the plots using Matplotlib (labels, colors, markers,legend, grids, figure sizes, etc)
The Subplots and axes in matplotlib
Showing Images
Understanding the concept of Machine Learning
The Flow of Machine Learning
The Mathematics Required for ML
Types of Learning and their sub-categories
The Scikit-learn Library
REGRESSION: Linear Regression
The Line Equation; Fitting Data in Model
Trade-off between biases-variances
Performance Evaluation of Model
Multiple Linear Regression: Case-study
Logistic Regression: Concept
Kernel Nearest Neighbors (KNN)
Decision Trees Classifier
Random Forest Classifier Introduction to Streamlit
*Projects*
Project 1: "House Price Prediction Model"
Project 2: "Heart Disease Detection Model"
Project 3: "Bank Churn Prediction Model"
Project 4: "Automated Loan Prediction System"
Who this course is for
Developer Interested In Getting Started With Data Science Applications In The Python Environment
Developer Wishing To Learn The Implementation Of Unsupervised Learning On Real Data Using Python




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