9i Technologies - 6 months / 6 Weeks Industrial Training in Java, Python, AI, Data Science, ChatGPT,Web Design & Development, Digital Marketing

Advance Course in Generative AI with Python

Generative AI refers to artificial intelligence systems capable of creating new content, such as text, images, and music, by learning from existing data.

Category Artificial Intelligence
course Mode Offline
Course Duration 6 Weeks / 6 Months

(22 reviews)

Description

Hi! Welcome to the Generative AI Course, the most advance AI course you will find in Tricity.

Here are some more details of what you get with This Course:

Introduction to Python Language – Learn the basics of Python Language, variables, loops, logic, Strings, Lists, Sets, Dictionary, Tuple, Exception handling, I/O, Modules, Regular Expressions and more...
Data Analysis – Using powerful libraries like Numpy, Pandas, Matplotlib and Scipy analyze, clean and filter the data in various formats.
5 Projects on Generative AI – Develop AI Chatbot, Recommender system, ShopAssist API,Image generation and more....

Courses Curriculum

Section 1: Introduction to Python
- 01. • Installation and Working with Python
- 02. • Understanding Python variables
- 03. • Python basic Operators.
- 04. • Understanding python blocks
- 05. • Declaring and using Numeric data types: int, float, complex
- 06. • Using String data type and String operations
- 07. Getting input from user
- 08. Converting from one data type to other
Section 2: Data Structures & Logic building
- 01. Lists, Sets and Tuples
- 02. Dictionary
- 03. Slicing and other List & dictionary manipulation functions
- 04. • Programming using string, list and dictionary in build functions
Section 3: Python Program Flow Control
- 01. • Conditional blocks using if, else and elif
- 02. • Simple for loops in python
- 03. • For loop using ranges, string, list and dictionaries
- 04. • Use of while loops in python
- 05. • Loop manipulation using pass, continue, break and else
- 06. • Powerful Lamda function in python
- 07. List and Dictionary Comprehensions
Section 4:Functions
- 01. Defining Functions
- 02. Positional, default,keyword,*args, **args
- 03. Return Types of functions
- 04. Passing functions to functions
- 05. Generator functions
- 06. Iterators
- 07. Decorators
- 08. Map, Reduce, Filter, FrozenSet, Defaultdict
Section 5: Exception Handling
- 01. Errors
- 02. Exception Handling with Try
- 03. Handling multiple Exceptions
- 04. Writing your own Exception
Section 6: File handling in Python
- 01. Reading config files in python
- 02. Writing log files in python
- 03. Understanding read functions, read(), readline() and readlines()
- 04. Understanding write functions, write() and writelines()
- 05. Manipulating file pointer using seek
- 06. Saving objects with Pickle
- 07. Programming using file operations
Section 7: Python Object Oriented Programming – OOPS
- 01. Concept of class, object and instances
- 02. Constructor, class attributes and destructors
- 03. Inheritance , overlapping and overloading operators
- 04. Adding and retrieving dynamic attributes of classes
- 05. Programming using Oops support
Section 8: Python Regular Expression
- 01. Powerful pattern matching and searching
- 02. Power of pattern searching using regex in python
- 03. Password, email, url validation using regular expression
- 04. Pattern finding programs using regular expression
Section 9: API calling with Python
- 1. Using requests
- 2. Download file contents with requests
- 3. Using requests to post data
Section 10: Web Scraping with BeautifulSoup
- 1. Navigating HTML structure with BS
- 2. Navigating the HTML structure With Beautiful Soup
- 3. Searching and Extract for specific tags With Beautiful Soup
- 4. Creating new HTML elements With Beautiful Soup
- 5. Modifying HTML with BeautifulSoup
Section 11: Numpy Library
- 01. Creating Numpy Array - Why Numpy
- 02. NumPy Array Manipulation
- 03. Matrix in NumPy
- 04. Operations on NumPy Array
- 05. Reshaping NumPy Array
- 06. Indexing NumPy Array
- 07. Arithmetic operations on NumPyArray
- 08. Sorting and Searching in NumPy Array
- 09. NumPy and Random Data
- 10. Universal Functions
Section 12: Pandas Tutorial
- 01. Pandas Installation and Dataframe basics
- 02. Different ways of creating Dataframes
- 03. Read Write Excel CSV files
- 04. Handling Missing data - fillna,dropna
- 05. Handle missing data - replace function
- 06. Group by
- 07. Concating Dataframes
- 08. Merge dataframes
- 09. Pivot Tables
- 10. Reshape dataframe using melt
- 11. Stack Unstack
- 12. Crosstab in Dataframes
- 13. Read Write data from database
- 14. Datetimeindex and resample
- 15. Time series analysis
Section 13: Matplotlib
- 01. Creating different types of plots
- 02. Line Graph in matplotlib
- 03. Stem plot in matplotlib
- 04. Bar Charts
- 05. Plotting Histograms
- 06. Scatter Plots
- 07. Stack Plots
- 08. Box Plots
- 09. Pie Charts
- 10. Dynamic time series plots
Section 14: Machine Learning
- 01. What exactly is Machine Learning
- 02. Linear Regression with Single and multiple variables
- 03. Save model using Joblib and Pickle
- 04. Dummy variables and One Hot Encoding
- 05. Training and Testing data
- 06. Logistic Regression (Single and Multiclass classification)
- 07. Decision Tree
- 08. Support Vector Machine
- 09. Random forest algorithm
- 10. K Fold Cross Validation
- 11. K Fold Cross Validation
- 12. K Means Clustering Algorithm
- 13. Naive Bayes Classifier Algorithm
- 14. Hyper parameter tuning
- 15. L1 & L2 Regularization
- 16. K nearest neighbour classification
- 17. Principal Component Analysis
- 18. Bias vs variance
- 19. Ensemble learning
- 20. Deploying Models
Section 15: Generative AI
- 01. Introduction to Generative AI
- 02. Working with ChatGPT APIs
- 03. Prompt Engineering
- 04. Designing LLM based systems
- 05. Desiging a Shoping Assistant
- 06. Building Custom Chatbot with LLMs
- 07. Search Techniques and Embeddings
- 08. Semantic Search
- 09. Vector Stores
- 10. Semantic search with Chroma
- 11. Retrieval Augmented Generation
- 12. Building Effective Search Systems
- 13. Langchain basics
- 14. Building RAG with Langchain
- 15. Llama Index
- 16. Fine Tuning large language models
- 17. Deploying AI applications
- 18. Image Generation Models

What you'll learn

Basics of Python Language
Data handling and visualization
Visualizing Data
Downloading data from APIs
I/O with Python
Data Analysis APIs
Machine Learning Models
Web scraping API
ChatGPT API
Open source LLMs

Requirements

Zeal to Code.
Basic of Programming Languages.
Python, Pycharm/Anaconda, and other APIs

Who this course is for:

Beginners to programming languages
Students eager to learn about Data Analytics
Working on real time data and filtering data
Your ultimate goal for learning AI

Reviews

Pawandeep Singh

Best Institute for Data Science

I am a student of btech IT currently doing six month training at 9i technology i am doing python with data science staff and all the faculty member are very friendly and supportive they helped me gain all the industrial exposure.

Rahul Yadav

Exceptional!

I am a student of dav college, I am doing Python data science course from here, and the teaching here is very good. And teachers are very helpful.

Sukhman Saran

Perfect Institute!

I am student of B.Tech. IT currently on industrial training at 9i Technologies. I am doing course of Python with Data Science. This institute is best for learning new skills. Staff and all the faculty members are very friendly and supportive. They helped me gain all the industrial exposure.

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Advance Course in Generative AI with Python

Advance Course in Generative AI with Python

Description

Courses Curriculum

What you'll learn

Requirements

Who this course is for:

Reviews

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