Machine Learning Training

This course provides a comprehensive introduction to Machine Learning (ML) — the science of enabling computers to learn from data and make decisions without being explicitly programmed. It covers the foundational concepts, techniques, and tools used to build intelligent systems that can classify, predict, cluster, and discover patterns in data.

Learners will gain both theoretical knowledge and hands-on experience in applying popular ML algorithms using Python and industry-standard libraries like Scikit-learn, Pandas, and TensorFlow/Keras. The course blends supervised and unsupervised learning, model evaluation, and real-world projects, preparing learners for practical ML challenges.

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💡 Learning Objectives

By the end of this course, learners will be able to

Understand core concepts and types of machine learning

Preprocess and prepare real-world data for modeling

Evaluate and fine-tune machine learning models

Apply ML techniques to solve real-world problems

Implement key algorithms such as Linear Regression, Decision Trees, KNN, and Clustering

Use Python libraries (Scikit-learn, Pandas, etc.) for model development

Deploy ML models in a basic production environment

🛠️ Course Features

100% practical and
project-based learning

Real-world
capstone projects

Code reviews and
mentor support

Resume and GitHub
portfolio development

Lifetime access to
course materials (if online)

Certification upon
successful completion

Target Audience

This course is ideal for:

Assessment Methods

📦 Technologies & Tools Covered:

Python

Scikit-learn

Pandas

NumPy

Matplotlib

Seaborn

TensorFlow

Jupyter Notebooks

Google Colab

VS Code

Kaggle

GitHub

Topics Covered

Introduction to ML and its applications
Supervised learning: Regression and Classification
Unsupervised learning: Clustering and Dimensionality Reduction
Data preprocessing and feature engineering
Model evaluation and performance metrics
Ensemble learning techniques
Introduction to neural networks
Model deployment basics using Flask or cloud services

📚 Machine Learning Course Content

Module 1: Introduction to Machine Learning

What is Machine Learning?
Machine Learning vs. AI vs. Deep Learning
Types of ML: Supervised Learning Unsupervised Learning Reinforcement Learning (Intro)
Applications of Machine Learning
Typical ML Pipeline

Module 2: Python and Tools for Machine Learning

Python refresher (data types, functions, loops)
Jupyter Notebooks / Google Colab
Libraries
NumPy, Pandas – data manipulation
Matplotlib, Seaborn – data visualization
Scikit-learn – ML algorithms
TensorFlow/Keras – neural networks (intro)

Module 3: Data Preprocessing and Cleaning

Understanding datasets (CSV, JSON, SQL)
Handling missing values
Dealing with outliers
Encoding categorical variables (Label, One-hot)
Feature scaling (Standardization, Normalization)
Train-Test Split and Cross-validation

Module 4: Exploratory Data Analysis (EDA)

Module 5: Supervised Learning - Regression

Introduction to Supervised Learning
Linear Regression
Polynomial Regression
Ridge and Lasso Regression
Evaluation metrics: MAE, MSE, RMSE, R²
📌 Project: Predict house prices based on features like size, location, etc.

Module 6: Supervised Learning - Classification

Logistic Regression
K-Nearest Neighbors (KNN)
Decision Trees
Random Forests
Support Vector Machines (SVM)
Evaluation metrics: Accuracy, Precision, Recall, F1-Score, Confusion Matrix, ROC-AUC
📌 Project: Predict whether a patient has diabetes using medical records

Module 7: Unsupervised Learning

Module 8: Model Selection and Evaluation

Module 9: Ensemble Methods

Module 10: Introduction to Neural Networks

Module 11: Model Deployment Basics

Module 12: Capstone Project

End-to-end ML workflow:
Problem definition
Data collection/cleaning
Model training & evaluation
Model deployment
Examples
Fake news detection
Real estate price prediction
E-commerce recommendation system

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🪪 Course Certificate

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Ideal for fast-paced learning with daily live sessions and hands-on projects

Easily Shareable

Suitable for working professionals or students balancing other commitments

Enhance Credebility

Suitable for working professionals or students balancing other commitments

Better Opportunities

Suitable for working professionals or students balancing other commitments

Industry Leading Placement

Whether you're aspiring to become a full-stack developer, looking to enhance your career, or build your own end-to-end applications, this

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