TechX — Technology & Software

Machine Learning With Python

Data In. Decisions Out.

A focused programme on building machine learning models from scratch using Python. Go from data preprocessing and statistical foundations to deploying production-ready ML pipelines. Perfect for students who want deep practical expertise in supervised, unsupervised, and ensemble learning methods.

12 Sessions
3 Certifications
Expert Mentors
100% Project-Based

What you'll learn

Master Python libraries for data science — NumPy, Pandas, Matplotlib, Seaborn
Build supervised learning models: regression, classification, and decision trees
Apply unsupervised techniques — clustering, PCA, and dimensionality reduction
Tune and evaluate models with cross-validation and hyperparameter optimisation
Work with real-world datasets and handle missing data, outliers, and imbalanced classes
Build ensemble models using Random Forest, XGBoost, and Gradient Boosting
Deploy trained models as REST APIs using Flask and FastAPI
Build an end-to-end ML project for portfolio and interview readiness

Course curriculum

Introduction to Machine Learning

  • What is Machine Learning?
  • History and Significance of Machine Learning
  • Types of Machine Learning
  • Applications of Machine Learning
  • Machine Learning Workflow
  • Python in Machine Learning
  • Career Opportunities in Machine Learning
  • Practical: Setting Up the Python Environment for Machine Learning

Python Fundamentals for Machine Learning

  • Python Syntax
  • Variables and Data Types
  • Operators
  • Conditional Statements
  • Loops
  • Functions
  • Lists, Tuples, and Dictionaries
  • Practical: Writing Basic Python Programs

Python Libraries for Machine Learning

  • Introduction to NumPy
  • NumPy Arrays
  • Introduction to Pandas
  • DataFrames and Series
  • Data Manipulation
  • Introduction to Matplotlib
  • Data Visualization
  • Practical: Data Analysis and Visualization Using Python Libraries

Data Preprocessing

  • Introduction to Data Preprocessing
  • Data Collection
  • Data Cleaning
  • Handling Missing Values
  • Data Transformation
  • Feature Scaling
  • Data Splitting
  • Practical: Preparing a Dataset for Machine Learning

Exploratory Data Analysis (EDA)

  • Introduction to EDA
  • Statistical Analysis
  • Data Visualization Techniques
  • Correlation Analysis
  • Feature Selection
  • Pattern Identification
  • Data Interpretation
  • Practical: Performing Exploratory Data Analysis

Supervised Learning

  • Introduction to Supervised Learning
  • Regression
  • Classification
  • Training and Testing Data
  • Model Development
  • Model Evaluation
  • Performance Metrics
  • Practical: Building a Simple Regression Model

Classification Algorithms

  • Logistic Regression
  • Decision Trees
  • K-Nearest Neighbors (KNN)
  • Naive Bayes
  • Support Vector Machines (SVM)
  • Classification Metrics
  • Practical: Developing a Classification Model

Unsupervised Learning

  • Introduction to Unsupervised Learning
  • Clustering
  • K-Means Clustering
  • Hierarchical Clustering
  • Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • Practical: Implementing Clustering Techniques

Model Evaluation and Optimization

  • Model Validation
  • Overfitting and Underfitting
  • Cross-Validation
  • Hyperparameter Tuning
  • Bias and Variance
  • Performance Improvement
  • Practical: Evaluating and Optimizing Machine Learning Models

Introduction to Deep Learning

  • What is Deep Learning?
  • Artificial Neural Networks
  • Perceptrons
  • Hidden Layers
  • Activation Functions
  • TensorFlow and Keras Overview
  • Practical: Building a Simple Neural Network

Machine Learning Applications

  • Computer Vision
  • Natural Language Processing
  • Recommendation Systems
  • Predictive Analytics
  • Industrial Applications
  • Real-World Case Studies
  • Practical: Developing an Application-Based Machine Learning Model

Major Project and Career Preparation

  • Machine Learning Project Planning
  • Dataset Selection
  • Model Development
  • Testing and Evaluation
  • Project Documentation
  • Project Presentation
  • Career Opportunities in Machine Learning
  • Practical: Major Project – Build, Train, and Evaluate a Machine Learning Model

Certifications you'll earn

Certificate of Training
Certificate of Internship
Certificate of Excellence

24/7 Mentor Support + Placement Assistance

Get help from experienced industry mentors whenever you need it. Placement guidance is built into every programme.

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