Machine Learning

450,000.00

Machine Learning is revolutionizing industries by enabling computers to learn from data and make intelligent decisions. This course provides a hands-on approach to understanding machine learning concepts, algorithms, and real-world applications. Whether you’re a beginner or an experienced professional, you’ll gain practical skills in data analysis, model building, and deployment. By the end of this course, you’ll be equipped to solve complex problems using machine learning techniques and advance your career in AI and data science.

Module 1: Introduction to Machine Learning

  • What is Machine Learning?
  • Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
  • Real-world Applications of Machine Learning
  • Machine Learning vs. Artificial Intelligence vs. Deep Learning
  • Setting Up a Machine Learning Environment

Module 2: Python for Machine Learning

  • Introduction to Python and Libraries for ML (NumPy, Pandas, Matplotlib, Scikit-learn)
  • Data Manipulation and Preprocessing
  • Data Visualization Techniques
  • Exploratory Data Analysis (EDA)

Module 3: Supervised Learning

  • Understanding Labels and Features
  • Regression Algorithms (Linear Regression, Polynomial Regression)
  • Classification Algorithms (Logistic Regression, Decision Trees, Random Forest, SVM)
  • Performance Metrics: Accuracy, Precision, Recall, F1-Score

Module 4: Unsupervised Learning

  • Clustering Techniques (K-Means, Hierarchical Clustering, DBSCAN)
  • Dimensionality Reduction (PCA, t-SNE)
  • Anomaly Detection
  • Association Rule Learning

Module 5: Feature Engineering and Model Selection

  • Handling Missing Data and Outliers
  • Feature Scaling and Encoding
  • Model Selection and Cross-Validation
  • Hyperparameter Tuning (Grid Search, Random Search)

Module 6: Deep Learning Basics

  • Introduction to Neural Networks
  • Activation Functions and Backpropagation
  • Building Neural Networks with TensorFlow & Keras
  • Introduction to Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)

Module 7: Machine Learning Model Deployment

  • Introduction to Model Deployment
  • Using Flask and FastAPI for Model Deployment
  • Deploying ML Models with Cloud Services (AWS, Google Cloud, Azure)

Module 8: Real-World Projects & Case Studies

  • Sentiment Analysis with NLP
  • Predicting House Prices
  • Image Classification with CNN
  • Fraud Detection in Banking

Who Should Enroll?

  • Beginners in Data Science & AI
  • Software Engineers and Developers
  • Data Analysts and Statisticians
  • Business Analysts and Researchers

Course Benefits

  • Hands-on Projects and Assignments
  • Industry-Recognized Certification
  • Access to Study Materials and Community Support
  • Career Guidance and Job Assistance

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