Machine Learning with python training
Machine Learning with python training
AI INTEGRATED COURSE

Machine Learning with python training

Data Scientist

Course Overview

Broadway Infosys's ML with Python Program trains students to develop and test machine learning models in Python. This course covers the most commonly used Python libraries, such as NumPy, Pandas, Scipy, Seaborn, and Scikit-learn, and focuses on manipulating, preprocessing, and visualizing data.

The training will expose students to supervised learning techniques, including linear regression, logistic regression, decision tree learning, and random forest, and unsupervised learning techniques , including K-means clustering and PCA.

The course is based on concepts such as model evaluation, cross-validation, and hyperparameter tuning to improve the performance of methods. Real-world applicable practical projects and assignments strengthen students' understanding and skills to solve the challenges one encounters in the machine learning space. At the end of the course, the certificate is issued upon course completion to confirm your expertise in implementing machine learning with Python.

Skills You'll Learn

  • Python Basics: You’ll learn how to write Python code, work with data types, loops, functions, and libraries.
  • Data Handling with Pandas: You’ll use Pandas to load, clean, and explore data in tables (DataFrames).
  • Machine Learning Basics: You’ll understand what ML is, how models learn, and the difference between supervised and unsupervised learning.
  • Supervised Learning: You’ll train models like Linear Regression, Decision Trees, and Random Forests to make predictions from labeled data.

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.
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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.
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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’m absolutely thrilled with my AI and Python classes at Broadway Infosys. The instructors are incredibly knowledgeable and patient and make complex concepts like machine learning and Python programming engaging and easy to...
Testimonial from Mr. Prason Shrestha
Mr. Prason Shrestha
Machine Learning with python training
“
Our instructor was very engaging. He never ignored any of my queries and tried his best to clear them up for me. His friendly attitude encouraged me to ask more questions. His way of giving examples to make difficult concepts...
Successful student from Broadway Infosys Mr. Rozon Pal
Mr. Rozon Pal
Machine Learning with python 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

  • If Else 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:

  • 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

  • Introduction to Machine Learning concepts
  • Supervised vs. Unsupervised Learning
  • Installing Anaconda and Managing Environment
  • Introduction to Spyder and Jupyter notebook
  • Familiarization with Datasets
  • Data Exploration and Analysis
  • Machine Learning Libraries
    • Numpy
    • Scikit Learn
    • Matplotlib
    • Seaborn
    • Pandas
    • Scipy

Get the dataset

  • Importing the Libraries
  • Importing the Dataset
  • Missing Data
  • Categorical Data
  • Splitting the Dataset into the Training set and Test set
  • Feature Scaling

Introduction

  • Optimization and gradient descent
  • Linear regression implementation
  • Correlation Analysis and Feature Selection
  • Evaluate Model Performance
  • Multiple Regression and Feature Importance
  • Variance Bias Trade Off - Validation Curve
  • Variance Bias Trade Off - Learning Curve
  • Cross validation

  sigmoid function

  • Cross validation
  • Confusion Matrix, Precision, Recall and F1 Score
  • Precision and Recall Tradeoff
  • The ROC Curve

  • Introduction
  • Linear SVM Classification
  • Polynomial Kernel
  • Gaussian Radial Basis Function
  • Support Vector Regression

  • Introduction
  • Lazy learning
  • Euclidean-distance
  • Implementation

  • Introduction
  • Implementation
  • Classification and clustering applications
     

  • Decision trees
  • Basics
  • Entropy
  • Information gain
  • Implementation

 

  • Introduction
  • Principal Component Analysis
  • K-means clustering
  • Hierarchical clustering

  • Introduction to Reinforcement Learning
  • Applications of Reinforcement Learning
  • Examples including AlphaGo case study

Introduction to Deep Learning

  • Artificial Neural Networks
  • Introduction to deep learning libraries such as tensorflow, keras, pytorch etc.
  • Research works in deep learning

     Simple Linear Regression Modelling with Boston Housing Data

  • Iris Flowers Classification Project
  • MNIST Project - Logistic Regression
  • Text Classification using SVM
CLASS DURATION
10 Weeks
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