What You Will Learn!
- The course is designed to provide students with a strong foundation in advanced Python programming, data analysis, and machine learning.
- Students will learn advanced programming concepts, including list comprehensions, file I/O operations, exception handling, and lot more advance python concepts.
- Data manipulation and analysis using the NumPy and Pandas libraries, covering data cleaning, preprocessing, and transformation techniques.
- Data visualization using Matplotlib, Seaborn, and Plotly for creating informative and visually appealing plots and charts.
- Implementation and evaluation of various machine learning algorithms, such as supervised and unsupervised learning, using the Scikit-learn library.
- Optional exploration of advanced topics like natural language processing, web scraping, time series analysis, and recommender systems for a more comprehensive u
Description
Ready to advance your Python skills? Our easy-to-follow Advanced Python course is tailored for learners of all levels, This course is crafted for students aspiring to master Python and dedicated to pursuing careers as data analysts or data scientists. It comprehensively covers advanced Python concepts, providing students with a strong foundation in programming and data analysis, focusing on data analysis, visualization, and machine learning.
Discover the power of Python in handling complex data, creating engaging visuals, and building intelligent machine-learning models.
Course Curriculum:
1. Introduction to Python:
Part 1: Dive into Python fundamentals
Part 2: Further exploration of Python basics
2. Advance Python Concepts:
List Comprehension and Generators
File Handling
Exception Handling
Object-Oriented Programming (OOPs)
Decorators and Metaclasses
3. NumPy (Expanded Library Coverage):
Arrays and Array Operations
Array Indexing and Slicing
Broadcasting and Vectorization
Mathematical Functions and Linear Algebra
Array Manipulation and Reshaping
4. Pandas (Expanded Library Coverage):
Pandas Data Structures
Data Transformation and Manipulation
Data Cleaning and Preprocessing
Joining, Merging, and Reshaping
5. Data Visualization:
Advanced Matplotlib Techniques
Seaborn for Statistical Visualization
Plotly for Interactive Visualizations
Geospatial Data Analysis
6. Machine Learning with Scikit-learn (Expanded Library Coverage):
Linear Regression
Logistic Regression
SVM, Decision Tree, Random Forest
Unsupervised Learning
Model Validation Techniques
Hyperparameter Tuning and Model Selection
7. Case Studies and Projects:
House Rent Prediction
Heart Disease Prediction
Customer Segmentation
Why Choose Our Course?
In-depth Modules Covering Python, NumPy, Pandas, Data Visualization, and Machine Learning
Hands-on Learning with Real-world Case Studies
Expert-led Sessions for Comprehensive Understanding
Unlock Your Potential in Data Science and Python Programming
With hands-on practice and expert guidance, you'll be prepared for rewarding opportunities in data science and analytics.
** Join us now to become a proficient Python data analyst and unlock a world of possibilities! **
Who Should Attend!
- Students interested in exploring data analysis, cleaning, and preprocessing techniques using Python will find this course helpful in understanding how to work w
- For students keen on expanding their knowledge beyond basic programming, this course delves into advanced Python concepts, object-oriented programming, and more
- Students those who wants to learn advanced Python concepts, and are intrested towards the field of datascience
- Students and professionals in the field of machine learning and artificial intelligence looking to strengthen their understanding of Python for implementing and
- Data analysts and data scientists seeking to leverage Python for advanced data manipulation, analysis, and visualization tasks.
- Intermediate Python developers aiming to enhance their skills and delve deeper into advanced programming concepts.
- Software engineers interested in expanding their knowledge of Python for various applications, including web development, data processing, and automation.