GAINTECH NEXUS • MACHINE LEARNING

Build Skills.
Build Projects.
Build Your Career.

A structured Machine Learning journey.

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ProjectsPractical Learning
MACHINE LEARNING CAREER PROGRAM

From Basics to Advanced Machine Learning

Machine LearningStatisticsSupervised Learning Unsupervised LearningSemi-Supervised LearningMachine Learning Workflow
One page. Complete course clarity.

Explore curriculum, plans, projects and career support before you enrol.

Machine LearningStructured learning path
Detailed SyllabusTopic-by-topic curriculum
Practical ProjectsLearn by building
Career SupportResume & interview assistance
WHY GAINTECH NEXUS

Learn the skills. Understand the concepts. Build the projects.

Designed as a practical learning journey rather than a list of disconnected tools.

01

Concept to Application

Learn fundamentals first, then apply them to real data and practical problems.

02

Industry-Relevant Stack

Python, SQL, Power BI, Machine Learning and advanced AI topics in one learning path.

03

Project-Based Learning

Build portfolio-ready projects while learning the tools and techniques.

04

Career Preparation

Resume preparation, interview practice and placement assistance as applicable to your plan.

DETAILED CURRICULUM

Complete Machine Learning Syllabus

A detailed, long-form curriculum so students can clearly see what they will learn.

01

Statistics

Develop the statistical thinking required for data-driven decision making.

Foundations

  • Math & Probability
  • Statistical Thinking
  • Population vs Sample
  • Sample & Its Types
  • 5-Point Summary

Inference

  • Inferential Statistics
  • Hypothesis Testing
  • Type I & Type II Errors
  • Binomial Distribution
  • Normal Distribution

Mathematical Foundation

  • Linear Algebra
  • Probability Concepts
  • Statistical Interpretation
02

Machine Learning

Understand, train and compare machine learning models.

ML Foundations

  • Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Semi-Supervised Learning
  • Machine Learning Workflow
  • Dataset & Feature Understanding
  • Target Variable & Feature Selection
  • Missing Value Handling
  • Duplicate & Outlier Handling
  • Data Cleaning for Machine Learning
  • Encoding Categorical Variables
  • Label Encoding
  • One-Hot Encoding
  • Feature Preparation
  • Feature Scaling
  • Standardization & Normalization
  • Train-Test Split
  • Cross Validation Basics
  • Feature Selection
  • Dimensionality Reduction
  • PCA (Principal Component Analysis)
  • Model Training & Prediction
  • Overfitting & Underfitting
  • Bias & Variance
  • Model Evaluation Basics

Supervised Learning

  • Introduction to Supervised Learning
  • Regression vs Classification
  • Linear Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Cost Function & Error Measurement
  • Logistic Regression
  • Classification using Logistic Regression
  • Decision Tree
  • Splitting Criteria: Gini Impurity & Entropy
  • Tree Depth & Pruning
  • Random Forest
  • Bagging & Ensemble Learning
  • Feature Importance
  • K-Nearest Neighbors (KNN)
  • Distance Metrics
  • Choosing the Value of K
  • Support Vector Machine (SVM)
  • Linear & Non-Linear SVM
  • Kernel Functions
  • Hyperplane & Margin
  • XGBoost
  • Gradient Boosting Concept
  • Boosting Parameters
  • Naive Bayes
  • Bayes Theorem
  • Gaussian, Multinomial & Bernoulli Naive Bayes

Unsupervised Learning

  • Introduction to Unsupervised Learning
  • Clustering Concepts
  • K-Means Clustering
  • Choosing the Optimal Number of Clusters
  • Elbow Method
  • Centroid Initialization
  • Distance Metrics in Clustering
  • Cluster Assignment & Centroid Updating
  • K-Means++ Initialization
  • Hierarchical Clustering
  • Agglomerative Clustering
  • Dendrograms
  • DBSCAN
  • Core Points, Border Points & Noise
  • Density-Based Clustering
  • Clustering with High-Dimensional Data
  • Feature Scaling for Clustering
  • PCA for Clustering
  • Cluster Visualization
  • Silhouette Score
  • Inertia / Within-Cluster Sum of Squares
  • Davies-Bouldin Index
  • Cluster Interpretation & Profiling
  • Practical Clustering Projects
03

Model Evaluation

Model Performance Evaluation.

Classification Evaluation

  • Classification Concepts
  • Binary Classification
  • Multiclass Classification
  • Multilabel Classification
  • Confusion Matrix
  • True Positive, True Negative, False Positive & False Negative
  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • Classification Report
  • ROC Curve
  • ROC-AUC Score
  • Precision-Recall Curve
  • Log Loss
  • Classification Threshold
  • Probability-Based Predictions
  • Class Imbalance & Evaluation
  • Cross-Validation for Classification
  • Model Performance Comparison

Regression Evaluation

  • Regression Concepts
  • Simple Linear Regression
  • Multiple Linear Regression
  • Regression Line & Predictions
  • Mean Absolute Error (MAE)
  • Mean Squared Error (MSE)
  • Root Mean Squared Error (RMSE)
  • R² Score
  • Adjusted R² Score
  • Mean Absolute Percentage Error (MAPE)
  • Residual Analysis
  • Actual vs Predicted Analysis
  • Regression Error Distribution
  • Overfitting & Underfitting in Regression
  • Feature Importance in Regression
  • Cross-Validation for Regression
  • Prediction Interval Concepts
  • Model Performance Comparison
PRACTICAL LEARNING

Build Projects While You Learn

Project work can be aligned with the topics covered in each learning stage.

PROJECT 01

Data Analysis & EDA

Clean a real dataset, perform exploratory analysis and present meaningful insights.

PROJECT 02

Regression Project

Build and evaluate a regression model with proper preprocessing and performance metrics.

PROJECT 03

Classification Project

Build a classification solution and analyse model performance using multiple metrics.

PROJECT 04

Power BI Dashboard

Transform business data into an interactive dashboard for decision making.

PROJECT 05

NLP / Text Project

Apply text preprocessing and NLP techniques to a practical text dataset.

PROJECT 06

Computer Vision / Forecasting

Apply advanced concepts to an image or time-series based case study.

CAREER SUPPORT

Learning doesn't stop at the syllabus.

Depending on the selected plan, GainTech Nexus provides career-oriented support to help learners prepare for opportunities.

✓Placement Assistance
✓Resume Preparation Session
✓Mock Interview Support
✓Practical Project Guidance
READY TO START?

Choose your learning plan.

Talk to GainTech Nexus and find the plan that fits your goal.

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