Python

Python is a versatile programming language known for its simplicity and readability. It is widely used in various domains, including web development, data analysis, artificial intelligence, and scientific computing. Python's extensive libraries and frameworks make it a popular choice for developers and data scientists alike. With its strong community support and active development, Python continues to evolve, making it a powerful tool for both beginners and experienced programmers. Whether you're building web applications, automating tasks, or analyzing data, Python provides the tools and flexibility needed to accomplish your goals efficiently.

Curriculum

Installation of Anaconda Prompt30:25πŸ”’
Jupyter Notebook-An Overview25:40πŸ”’
Shorcut Lkeys in Jupyter Notebook20:15πŸ”’
Data Types in Python20:15πŸ”’

Rules for Naming the Variables35:15πŸ”’
List, Tuple, Set, Dictionary28:40πŸ”’
"Introduction to Files and directories Introduction to the command prompt or terminal paths"22:30πŸ”’
"Text files Reading from a text file Opening a file using `with`"18:25πŸ”’

If, elif and else condition.30:25πŸ”’
For and While Loop25:40πŸ”’

Machine Learning Libraries30:25πŸ”’
Numpy-Hands on25:40πŸ”’

"Pandas-Hands on"45:30πŸ”’

Learn how to explore, visualize, and extract insights from data42:30πŸ”’
Data Visualization38:45πŸ”’
Matplotlib-Hands on35:20πŸ”’
Seaborn hands on28:15πŸ”’

You need to think statistically and to speak the language of your data50:20πŸ”’
Measures of Central Tendency35:45πŸ”’
Measures of Dispersion42:30πŸ”’
"IQR Statistics-Hands-On"42:30πŸ”’

Classification, Regression, Fine-tuning your model50:20πŸ”’
Supervised Learning35:45πŸ”’
Unsupervised Learning35:45πŸ”’
Linear Regression35:45πŸ”’
"Metrics in Linear Regression Hands-on in Linear Regression35:45πŸ”’

Logistic Regression50:20πŸ”’
Metrics in Logistic Regression35:45πŸ”’
Hands-on in Logistic Regression42:30πŸ”’

Linear regression50:20πŸ”’
Metrics for Linear regression35:45πŸ”’

Introduction to Data Preprocessing50:20πŸ”’
Standardizing Data35:45πŸ”’
Exploratory Data Analysis42:30πŸ”’
Missing Values42:30πŸ”’
Outliers42:30πŸ”’
"Standardization Mnormalization Feature Scaling and Selection42:30πŸ”’

Decision Tree50:20πŸ”’
Bagging35:45πŸ”’
Boosting Random Forest42:30πŸ”’

Neural Network50:20πŸ”’

Learning Outcomes:

  • Upon completing a Python curriculum learners are capable of installing and configuring Python, understanding its syntax, data types, control structures, and object‑oriented features. They will effectively use libraries such as NumPy and Pandas, perform data visualization, conduct statistical hypothesis testing and modeling (e.g., linear regression, group comparisons, correlation analysis), and interpret results for meaningful insights





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Price

β‚Ή3500

Course Title

Python

Language

English

Certification

Yes - Industry Recognized

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FAQs

The course duration is of 2 months & some course may extend to 3 months

Yes at the end of the course completion you will get certificates.

ABCPanda team will arrange a doubt clearance session accordingly.

Yes, for Recorded sessions & Live sessions access would be 1 year

No our mentors will teach from basic. If you have experience, it would add an advantage.