Python Crash Course for Data Science and AI - Free Course
This Python Crash Course is designed to foundations in order to write simple programs in Python . No previous knowledge to programming is needed. After Finishing this Course, you'll understand the benefits of programming in IT roles; be able to write simple programs using Python.
What you will learn
Everything you need know about Python
Python - a tool, not a reptile
There is more than one Python
Let's start our Python adventure
Your first program
Operators - data manipulation tools
Variables - data-shaped boxes
How to talk to computer?
Making decisions in Python
Logic and bit operations in Python
Lists - collections of data
Sorting simple lists - the bubble sort algorithm
Lists - some more details
Lists in advanced applications
Writing functions in Python
How functions communicate with their environment?
Returning a result from a function
Scopes in Python
Let's make some fun... sorry, functions
Tuples and dictionaries
Some useful modules
What is package?
Errors - the programmer's daily bread
The anatomy of exception
Some of the most useful exceptions
Characters and strings vs. computers
Python's nature of strings
Strings in action
and many more things
This Course Cover Topics such as Python Basic Concepts, Python Advance Concepts,
This is best course for any one who wants to start career in data science.
The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered.
Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.
Data Visualization and Analysis with ML using Python, Numpy Pandas, Matplotlib, Seaborn, Plotly & Scikit Learn library
This Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies
Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.
Who this course is for: Beginner Python Developer