data science & machine learning with python

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•  Python Part 1
•  Python Part 2
•  Python Projects Assignments
•  Projects +assignments
•  Python Libraries
•  Mathematics 
• Assignments 

•  Data pre-Processing Part 1
•  Data pre-Processing Part 2
•  Projects 
• Assignments  
• Data pre-Processing Part 3
• Assignment
•  Projects
•  Quiz 

•  ML Algorithms Part 1
•  Project 1-3
•  Project 3-6
•  ML Algorithms Part 2
•  Project 6-9
•  ML Algorithms Part 3
•  Assignment
•  Quiz 

•  Internship Project 1 - 2 
• Internship Project 3 - 4
•  Report Writing 
• GitHub
•  Technical Interview Questions
•  Quiz 2
•  Assignment
•  Quiz 3 

•  Resume Building
•  Technical Mock Interview
•  LinkedIn
•  HR Mock Interview
•  Task Submission 

Machine Learning can be referred to as a subset of Artificial Intelligence. Machine Learning refers to predicting outcomes on the basis of automatically learnt past data without programming explicitly. 

Python is a very flexible and easily understandable code which is very helpful for Machine Learning. It is concise and easily readable even to new developers and thus can help do a set of complex Machine Learning tasks and enables building prototypes for product testing in the Machine Learning process. 

Pandas refers to a Python package that provides high performance and easy to use data for the Python programming language. It is a perfect tool for wrangling and munging.

SciPy library contains modules for efficient mathematical routines. It’s main function is to build upon NumPy and its arrays. It has modules to perform common scientific programming Add an answer to this item.

Statistics and Machine Learning are two very closely related fields . Statistical methods like mean, median , etc can be used to prepare data ready for modeling . Probability acts as a medium in solving problems. It is an important factor in predictive analysis.

Data Pre processing refers to conversion of raw data into an understandable format. It also helps in analyzing data before Machine Learning.

Data Visualization helps in better qualitative understanding  through bar, Pie ,Data mapping ,etc.