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In Python Programming for Artificial Intelligence Learner will get an Introduction to Python Programming.
Also, will understand different data types in Python, Comparison operator and Conditional Statements, Looping statement, Functions and methods, Data Analysis - Numpy, Data analysis - Pandas, Introduction to Artificial Intelligence, Statistics, Data Visualization - Matplotlib, Data Visualization - Seaborn, Data Visualization - Pandas, Data Visualization - Plotly and cufflinks, Data Visualization - geographical plotting, Data pre-processing.
Python provides the benefit of a reasonable code. AI and ML require solving complex algorithms. However, the simplicity of Python will ensure that developers can easily write the codes. Whenever opting for Python development, businesses should consider understanding the code. One of the main reasons most individuals opt for Python development is that it is easy to learn. Developers can easily understand Python codes, making it easier for everyone to understand the basics of machine learning. Many developers also believe that Python is a better language than others. Other languages do not provide the benefit of simplifying the concepts, and however, Python brings in the benefit of a collaborative environment. Python is a basic general-purpose language that can easily conduct a wide range of complex tasks. When companies hire python development professionals, they often test their knowledge.
Who should take this course?
For a student or anyone seeking employment, this is a great opportunity to build a rewarding, future-proof, and meaningful career.
If you are already employed but are trying to rekindle your career in the exciting world of cybersecurity, this program is perfect for you.
Curriculum
In this course, Learner will be able to understand the use cases of Python programming, Learner will understand the difference between C++ and Python programming language
Learner will get Introduction to Google colab
Learner will be able to understand different data types in python
Learner will be able the different comparison operators in Python programming language
Learner will be able to understand the conditional statements
A better technical understanding of conditional statements
Better understanding of looping statements and its usage
Understand list comprehension topic
Understand the usage of enumerate function
Learner understands the usage of looping statements
Understanding the usage of Lambda expressions
Understanding the usage of Map and filter in Python
Learner will understand the basics of NumPy array
Understanding the concept of indexing in NumPy array
Understanding the different operations in NumPy array
Basic understanding of Pandas
Important - How data frame looks like & importance of data frame in data analysis
Understand the different ways to handle missing values in the data
How to sort data and dig deeper into analysis by using group by in pandas
Basic operations of Merging
Joining and concatenating of data
Hands on of Data analysis using Panda’s library
Understand the basics of AI, ML and DL
Understanding the basic understanding of Data science
Understanding the NLP concept
Understand why matplotlib is used
Understand and learner should be aware of the different types of plots in matplotlib, hands on experience of basic matplotlib plotting
Understanding the use cases of seaborn
Understand and learner should be aware of the different types of plots in seaborn, hands-on experience of basic seaborn plotting
Understanding the built-in data visualizations in pandas
Practical coding of visualization in pandas, basics of plotly and cufflinks
Practical coding of visualization in plotly
Understanding the importance of geographical plotting
Practical coding of visualization using choropleth
Understanding the basics of data pre-processing which is helpful for other AI courses
Understanding the methods of data cleaning, data transformation and data splitting
Tools you will learn in the course
Learn the essential foundations of AI: the programming tools (Python, NumPy, PyTorch), the math (calculus and linear algebra), and the key techniques of neural networks (gradient descent and backpropagation).