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 beginners 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/Looping/Error 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
• Merging, 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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