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Machine Learning Course

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Machine Learning Course – Learn AI and Data Science Algorithms

Online/Offline

About Machine Learning Course

The Machine Learning Course is designed to give you a strong foundation in building intelligent systems using Python, algorithms, and data-driven models. You will learn supervised and unsupervised learning, regression, classification, clustering, and model evaluation.

Machine Learning is the driving force behind AI, data science, and predictive analytics, used by top companies worldwide. Learning it opens doors to high-demand careers in data and AI fields.

By the end of this course, you’ll be able to implement machine learning models, analyze data patterns, and solve real-world problems using intelligent systems.

Machine Learning Course Modules

  • What is Machine Learning?
  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement
  • Applications of Machine Learning in Real Life
  • Overview of ML Workflow and Pipeline

  • Python Basics Refresher
  • Libraries: NumPy, Pandas, Matplotlib, Seaborn
  • Data Handling and Preprocessing
  • Data Visualization Techniques

  • Handling Missing Data
  • Encoding Categorical Variables
  • Feature Scaling: Standardization and Normalization
  • Feature Selection Techniques

  • Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Model Evaluation Metrics: MSE, RMSE, R²

  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Decision Trees
  • Random Forest Classifier
  • Model Evaluation: Accuracy, Precision, Recall, F1-Score, Confusion Matrix

  • Clustering Techniques: K-Means, Hierarchical Clustering
  • Dimensionality Reduction: PCA
  • Association Rule Mining
  • Applications of Unsupervised Learning

  • Train-Test Split and Cross-Validation
  • Bias-Variance Tradeoff
  • Hyperparameter Tuning: Grid Search, Random Search
  • Overfitting & Underfitting Solutions

  • Artificial Neural Networks Basics
  • Activation Functions
  • Forward and Backward Propagation
  • Simple Neural Network Implementation in Python

  • Support Vector Machines (SVM)
  • Ensemble Methods: Bagging, Boosting, XGBoost
  • Gradient Descent Optimization
  • Introduction to Deep Learning

  • End-to-End ML Project Workflow
  • Data Collection, Cleaning, and Analysis
  • Building and Evaluating ML Models
  • Deploying ML Models & Portfolio Showcase

FAQ

It’s a branch of AI where systems learn from data and improve without explicit programming.

Yes, knowledge of Python and math (linear algebra, probability, calculus) is helpful.

It covers supervised learning, unsupervised learning, neural networks, and model evaluation.

Python libraries like Scikit-learn, TensorFlow, and PyTorch.

3–6 months for basics, longer for advanced mastery.

Yes, ML is one of the hottest fields with applications in healthcare, finance, and tech.

Yes, projects like spam detection, recommendation systems, and predictive models are included.

Yes, ML engineers are in high demand worldwide.

You can move to Deep Learning, AI, or specialized fields like NLP and Computer Vision.

Because it powers AI, automation, and decision-making systems across industries.
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Machine Learning

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