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Python for Machine Learning

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Syllabus

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About The Syllabus

This syllabus builds a strong foundation in Python programming, data analysis, and machine learning. Learners start with Python fundamentals and data structures, then progress to NumPy, Pandas, and data visualization to understand, manipulate, and explore real-world datasets. Through practical exercises and hands-on projects, students learn essential machine learning workflows, explore popular ML libraries, and gain experience preparing data and training their first machine learning models.

Key points

  • Python programming fundamentals and data structures
  • NumPy arrays and numerical computing
  • Data analysis and manipulation with Pandas
  • Data cleaning and preprocessing techniques
  • Data visualization with Matplotlib and Seaborn
  • Introduction to Scikit-learn and ML workflows
  • Exploring TensorFlow, PyTorch, XGBoost, and LightGBM
  • Practical customer dataset analysis and ML project

Syllabus

Topic 1: Python Environment Setup and Programming Basics
 
✓
Python Fundamentals & Environment Setup
  • What Python is and why it is used in machine learning
  • Installing Python and checking the installation
  • Installing VS Code, Jupyter Notebook, and JupyterLab
  • Creating and using virtual environments
  • Installing packages with pip
  • Running Python scripts and notebook cells
✓
Python Programming Basics
  • Variables, data types, and type conversion
  • Operators and expressions
  • Input, output, and formatted strings
  • Conditional statements
  • Loops: for and while
  • Debugging syntax, runtime, and logical errors
Topic 2: Python Data Structures and Core Programming

 

✓

Python Data Structures & Collection Operations

  • Lists and common list operations
  • Tuples and when to use them
  • Sets and handling unique values
  • Dictionaries and key-value data
  • Indexing and slicing
  • Iterating through collections

✓

Functions, Modules & Error Handling

  • Functions, parameters, and return values
  • Lambda functions and basic functional programming
  • Modules, packages, and imports
  • Exception handling using try and except

✓

File Handling & Object-Oriented Programming

  • Reading and writing text, CSV, and JSON files
  • Introduction to object-oriented programming (OOP)
Topic 3: Numerical Computing with NumPy
 
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NumPy Fundamentals & Array Creation
  • Why NumPy is faster and more suitable than regular Python lists
  • Creating one-dimensional and multi-dimensional arrays
  • Understanding array shape, dimensions, size, and data types
  • Indexing and slicing NumPy arrays
  • Reshaping, flattening, and transposing arrays
✓
NumPy Operations & Data Manipulation
  • Vectorized operations
  • NumPy broadcasting
  • Mathematical and statistical operations
  • Random number generation
  • Boolean masking and conditional filtering
  • Handling missing values in numerical arrays
✓
Matrix Operations for Machine Learning
  • Matrix operations and their role in machine learning foundations
Topic 4: Data Analysis with Pandas
 
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Pandas Fundamentals & Data Inspection
  • Introduction to Series and DataFrames
  • Loading CSV, Excel, and JSON datasets
  • Inspecting rows, columns, data types, and summaries
  • Selecting, filtering, and sorting data
  • Creating, renaming, and removing columns
✓
Data Cleaning & Transformation
  • Handling missing values
  • Detecting and removing duplicates
  • Grouping and aggregation
  • Merging, joining, and concatenating datasets
  • Applying functions to DataFrame columns
  • Datetime data handling
✓
Dataset Preparation for Machine Learning
  • Preparing datasets for machine learning
Topic 5: Data Visualization and Advanced ML Libraries
 
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Data Visualization with Matplotlib
  • Why visualization matters before model building
  • Creating line charts with Matplotlib
  • Bar charts, pie charts, and histograms
  • Scatter plots and correlation exploration
  • Customizing titles, labels, legends, colors, and figure sizes
  • Creating multiple plots with subplots
✓
Statistical Visualization with Seaborn
  • Introduction to Seaborn for statistical visualization
  • Heatmaps, box plots, pair plots, and distribution plots
✓
Introduction to Machine Learning Libraries
  • Introduction to Scikit-learn
  • Loading sample datasets with Scikit-learn
  • Basic preprocessing, train-test split, and model workflow
  • Introduction to advanced ML libraries: TensorFlow, PyTorch, XGBoost, and LightGBM
✓
Practical Capstone Project
  • Analyze a customer dataset using Pandas
  • Clean and prepare data for analysis
  • Visualize customer trends and patterns
  • Prepare features and train a first Scikit-learn model

What Our Students Say

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Learning MERN Stack at Excendra was transformative. Practical projects, clear explanations, and industry-focused mentorship helped me develop strong full-stack skills and confidence to build professional applications.
Mehran Muzaffar Software Developer COMTECH GROUP
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I completed Full Stack Development training at Excendra. Practical projects and clear teaching built my skills and confidence. With their support, I secured a job soon after the course and recommend Excendra for tech career.
Hurain Javaid Software Engineer Sun-Net Inc.
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Instructor

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Er. Firdous Ahmad

AI Enterprise Architect | Full Stack Developer - 14+ Years Experience

This course includes:

  • 54 Hours of Learn-Anywhere Videos
  • Live & Recorded Access
  • Real-World Projects
  • Certified Learning Achievement

₹0

₹2999
  • Free registration for first 100 students
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