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Course Overview.

Data Science & Machine Learning

Welcome to the ATIVS, your premier institute for IT training, certification and development. Data Science and Machine Learning course is a program that trains you to understand and use data analysis, data visualization, and algorithms. In this course you get the opportunity to learn about statistical techniques, programming languages (like Python), and tools (like TensorFlow, Pandas, and Scikit-learn). Machine Learning (ML) course is an educational program in which you are taught the concepts and techniques of machine learning. Machine learning is a technology that gives computers the ability to learn patterns based on data and take decisions without explicitly programming. In this course you understand in detail about mathematical concepts, algorithms, and practical implementations.

Course Detail

Total Duration

8 Months

Rating

5

Mode

Online

Mode

Offline

Data Science & Machine Learning Course

  • Key Topics Covered
  • Data Science Basics: Data cleaning, preprocessing, exploratory data analysis.
  • Programming Skills: Python for data manipulation and analysis.
  • Machine Learning Algorithms: Regression, classification, clustering, neural networks.
  • Data Visualization Tools: Matplotlib, Seaborn, Tableau.
  • Advanced Topics: Deep learning, NLP, reinforcement learning.
  • Real-world Projects: Industry-based projects to practice skills.
  • What is taught in Machine Learning?
  • Introduction to Machine Learning: Basics and importance.
  • Types of ML: Supervised, Unsupervised, Reinforcement Learning.
  • Mathematics for ML: Linear Algebra, Probability, Statistics, Calculus.
  • Optimization Techniques & Data Preprocessing.
  • Handling missing values, outliers, and feature engineering.
  • Algorithms Covered
  • Regression: Linear Regression, Logistic Regression.
  • Classification: Decision Trees, SVM, k-NN.
  • Clustering: k-Means, Hierarchical Clustering.
  • Neural Networks & Deep Learning Basics.
  • Model Evaluation and Tuning
  • Evaluation metrics: Accuracy, Precision, Recall, F1 Score.
  • Hyperparameter tuning and cross-validation.
  • Tools and Frameworks
  • Python programming.
  • Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch.
  • Applications
  • Recommendation systems.
  • Image recognition.
  • Natural Language Processing (NLP).
  • Who Should Take This Course?
  • Students interested in Data Science, AI, or Computer Science.
  • Professionals looking to integrate AI/ML into their career.
  • Beginners with basic coding and mathematics knowledge.