AI with Python: Machine Learning, Deep Learning & Generative AI (LLMs) Training
AI with Python: Machine Learning, Deep Learning & Generative AI (LLMs) Training
AI INTEGRATED COURSE

AI with Python: Machine Learning, Deep Learning & Generative AI (LLMs) Training

AI Programmer

Course Overview

Artificial Intelligence (AI), often called machine intelligence, increasingly feels like a gateway to a transformative era as time progresses. This new era may see Siri and Cortana championing human rights and offering guidance that benefits society. Nonetheless, it falls upon future generations to nurture the development of robust and effective AI tools and technologies. Broadway Infosys has introduced an Artificial Intelligence Training program to equip global professionals and specialists. There are a few lucrative AI courses available globally. Being one of the leading IT institutions in the country, Broadway aims to help you overcome this challenge. Our classes are conducted by highly trusted and professional experts who provide theoretical and practical knowledge while sharing their experiences and challenges from the industry. This course provides participants with an insightful perspective on the advancements in artificial intelligence.

Skills You'll Learn

  • Python Programming: Strengthen coding skills with a focus on AI applications.
  • Machine Learning Algorithms: Implement classification, regression, and clustering.
  • Deep Learning Frameworks (TensorFlow, PyTorch): Build neural networks for advanced tasks.
  • Real-World AI Projects: Deploy intelligent solutions in practical scenarios.

Skill you'll gain

We provide industry aligned training with practical sessions & mentorship. Grow with our rich portfolio of skill enhancement courses & highly successful placements.
Rich History
Rich History
With over 16 years of excellence in IT training, we’ve transformed a myriad of careers with quality training & innovations.
Qualified Trainers
Qualified Trainers
Our faculty comprises experienced industry experts with a deep understanding of the real world experience & skills.
Support Excellence
Support Excellence
Our dedicated support team guides you all the way to the learning to placement process including growth opportunities.
Successful Alumnis
Successful Alumnis
Our graduates excel at big companies with their presence, itself being the testament to the reputed quality.

Here is what our students say

“
I feel really positive about my teacher and Broadway Infosys. The instructors are knowledgeable, supportive, and genuinely care about students growth.
Successful student from Broadway Infosys Mr. Manish Chaudhary
Mr. Manish Chaudhary
AI with Python: Machine Learning, Deep Learning & Generative AI (LLMs) Training
“
As a student of Broadway Infosys, I would like to express my sincere gratitude to all the teachers, specially our instructor, who have made my learning experience truly enriching. The knowledge, support, and encouragement I have r...
Testimonial from Mr. Ashish Verma
Mr. Ashish Verma
AI with Python: Machine Learning, Deep Learning & Generative AI (LLMs) Training

What we cover

What we cover
CLASS FORMAT
Online Classes (Day and Night)

  • What exactly is Python?
  • Python's root and its ecosystem
  • Python Installation & IDEs setting up (Google Colab, Jupyter Notebook, VSCode, PyCharm)
  • Python framework & Python syntax
  • Hands-on writing code on Google Colab

  • Data Types & Variables (String, Integer, Float, Complex, Boolean, None)
  • Input and Output Functions
  • Working with the format() method, f-strings, & escape sequences
  • Basic Arithmetic & Operators
  • Type casting, type checking, & validation

  • Conditional Statements (if, else, elif)
  • Loops (for, while)
  • Looping over tuples, strings, & dictionaries
  • Special loops in Python (for/else)
  • Using nested loops and flow control through conditions
  • Resolving real-world problems to improve skills
  • Special Statements: pass, continue, break

AI Tool:

  • Google Colab - Gemini

Lists:

  • Overview & fundamental operations
  • Indexing, slicing, & negative indexing
  • Looping through lists & conditions
  • List methods like .insert(), .append(), .remove(), .sort(), etc.
  • List comprehension with conditions

Tuples:

  • Introduction & operations
  • Indexing, slicing, & looping
  • List versus Tuple
  • Switching between lists and tuples
  • Tuple unpacking

Sets:

  • Introduction & set operations
  • Adding, removing, & discarding items
  • Set operations: union, intersection, and difference
  • Frozenset versus set

Dictionaries:

  • Introduction to dictionaries & methods like .get(), .update(), .keys(), .pop(), etc.
  • Dictionary comprehension
  • Nested dictionaries

AI Tool:

  • Gemini or Codeium

  • Defining functions through def keyword
  • Parameters, Arguments, & Return Statements
  • Returning multiple values
  • Default & keyword arguments
  • Anonymous functions (lambda)
  • Nested functions & closures
  • Scopes in Python: Local and Global

Text File Operations:

  • Reading & writing text files
  • Modes of file (r, w, a, rb, wb)
  • File path handling with the os module

Working with CSV Files:

  • Basics of CSV format and operations
  • Reading & writing CSV files with csv.reader & csv.writer
  • Using dictionaries in CSV files

Working with JSON:

  • Introduction to JSON & its structure
  • Reading & writing JSON data with the json module
  • Parsing JSON strings

AI Tool:

  • Using ChatGPT for prompt engineering

  • Classes & Objects
  • Class versus Object attributes
  • Initializing object attributes with __init__()
  • self keyword
  • Inheritance: single, multiple, and multi-level
  • Polymorphism & operator overloading
  • Function overriding & encapsulation

AI Tools:

  • Pythontutor.com

  • Try-except blocks
  • Catching specific exceptions
  • Using else & finally
  • Generating and creating custom exceptions
  • Problem-solving strategies

  • Lambda Functions
  • Generators & Iterators
  • List Comprehensions
  • Working with *args & **kwargs

Standard Libraries: os, random, math, functools, etc.

Data Manipulation with Pandas

  • Working with DataFrames
  • Reading & writing CSV files
  • Data manipulation techniques

Data Visualization

  • Using Matplotlib, Seaborn, and Plotly

AI Tools

  • Pandas Profiling

  • Designing and changing databases and tables
  • CRUD operations (CREATE, SELECT, UPDATE, DELETE)
  • Filtering data with the WHERE clause

AI Tools for SQL

  • DBeaver for SQL queries
  • Optimizing & explaining SQL queries with ChatGPT

  • Installing & configuring Git
  • Setting up local & remote repositories
  • Making commits & branching
  • Integrating local repositories to GitHub
  • Pushing changes & cloning repositories

AI Tools

  • GitHub Copilot for Git commands

  1. Web Scraping + Database + File Operations: Scrape data, store it in SQL, & export to CSV/JSON
  2. Desktop Application (Data Entry System): Develop an application to manage data in JSON/CSV format
  3. CLI Application with CRUD Operations: Design a CLI app with basic CRUD operations & database integration

  • Overview of AI, Machine Learning, and Deep Learning
  • Real-world use cases in industries like healthcare, finance, and e-commerce
  • Installation of Python, Jupyter, Google Colab, and Git

  • Types of data: structured vs. unstructured
  • Handling missing values and outliers
  • Encoding categorical variables and feature scaling
  • Exploratory Data Analysis (EDA) techniques and visualization

  • Linear Regression: theory, implementation, and evaluation
  • Logistic Regression for binary classification
  • Polynomial Regression
  • Model evaluation metrics: MSE, RMSE, R-squared, Accuracy, Precision, Recall
  • Projects in each algorithm

  • Support Vector Machines (SVM): linear and kernel methods
  • K-Nearest Neighbors (KNN): algorithm and distance metrics
  • Naive Bayes: theory, implementation, and use in spam filtering
  • Decision Trees and Random Forests: entropy, information gain, overfitting control
  • Projects in each Algorithm

  • Clustering techniques: K-Means, Hierarchical Clustering, DBSCAN
  • Dimensionality reduction with PCA and t-SNE
  • Anomaly detection and recommendation system with unsupervised models

  • Building a complete machine learning model
  • Deploying ML projects using Streamlit

  • Math essentials: vectors, matrices, activation functions, derivatives
  • Introduction to TensorFlow and Keras for model building

  • Structure and working of neural networks
  • Activation functions, forward and backward propagation
  • Regularization, dropout, and optimization techniques
  • Regression using ANN
  • Classification using ANN

  • CNN architecture and layers
  • Image preprocessing and augmentation
  • Transfer Learning
  • Basics of object detection (YOLO)
  • Image classification Project

  • Text preprocessing: tokenization, stopword removal, stemming, lemmatization
  • Word embeddings (Word2Vec, GloVe)
  • RNNs and LSTMs for sequence modeling
  • Sentiment analysis and Named Entity Recognition (NER)
  • Simple chatbot and text classification project

  • What are LLMs? Overview of GPT, BERT, T5, LLaMA
  • Transformer architecture: self-attention, encoder-decoder models
  • Pretraining vs fine-tuning vs instruction tuning

  • Prompt types: zero-shot, few-shot, chain-of-thought
  • Best practices for crafting effective prompts

  • Hugging Face Transformers and Datasets libraries
  • OpenAI API for text generation
  • LangChain for chaining prompts and workflows
  • Vector search using FAISS and ChromaDB

  • Text summarization and question answering
  • Retrieval-Augmented Generation (RAG)
  • Embedding-based semantic search
  • PDF/CSV/Website Q&A bots using LangChain
  • Chatbot development using LLMs and vector stores
CLASS DURATION
3.5 Months
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