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Data Science(AI,ML,Python)

Unleash the Power of Data Science: Dive into AI & ML, PYTHON

Unlock the potential of data science with our specialized training program, designed to immerse you in the world of Artificial Intelligence (AI), Machine Learning (ML), and Python programming. Our TecheBrain offers a comprehensive curriculum that equips you with the skills and knowledge to excel in the rapidly growing field of data science.

Overview

Welcome to our comprehensive Data Science course, where you'll learn to harness the power of Artificial Intelligence (AI), Machine Learning (ML), and Python to analyze data and derive actionable insights. This course is designed for aspiring data scientists, analysts, and anyone interested in entering the rapidly growing field of data science. Whether you're a beginner or a seasoned professional, our program covers all essential aspects of data science.

Who Should Enroll?

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  • Beginners: Individuals with no prior experience in data science looking to start a new career.

  • Aspiring Data Scientists: Those with basic programming knowledge who want to deepen their understanding of data science.

  • Analysts: Business and data analysts seeking to upgrade their skills with advanced data science techniques.

  • IT Professionals: Developers and IT professionals looking to transition into data science roles.

  • Entrepreneurs: Business owners aiming to leverage data science for better decision-making and strategic planning.

Ready to embark on your journey in Data Science? Visit our enrollment page to register for the next session. Early registration is recommended due to high demand. For additional information, contact our admissions office or schedule a visit to our training institute.

Join us to transform your passion for data into a successful career in data science. Let’s uncover the potential of data together!

Data Science( AI, ML,PYTHON) Course Syllabus

  • Overview of data science and its applications
  • Importance of data-driven decision making
  • Introduction to Python programming for data science

  • Data cleaning and preparation techniques
  • Handling missing values and outliers
  • Feature engineering and transformation

  • Statistical analysis and visualization of data
  • Descriptive and inferential statistics
  • Data visualization using Matplotlib and Seaborn

  • Introduction to supervised learning
  • Linear regression and logistic regression
  • Decision trees and random forests

  • Cross-validation techniques for model evaluation
  • Hyperparameter optimization using grid search and random search
  • Model performance metrics (accuracy, precision, recall, F1-score, ROC-AUC)

  • Introduction to unsupervised learning
  • Clustering algorithms (K-means, hierarchical clustering)
  • Dimensionality reduction techniques (PCA, t-SNE)

  • Basics of neural networks and deep learning
  • Building and training a neural network with TensorFlow and Keras
  • Convolutional Neural Networks (CNNs) for image classification

  • Understanding sequential data and time series forecasting
  • Building and training RNNs with TensorFlow and Keras
  • Applications of RNNs in natural language processing (NLP)

  • Basics of reinforcement learning and Markov decision processes
  • Q-learning and Deep Q-Networks (DQNs)
  • Applications of reinforcement learning in gaming and robotics

  • Exporting and saving trained models
  • Building APIs for model deployment using Flask or Django
  • Containerization and deployment using Docker and Kubernetes

  • Understanding biases and fairness in machine learning models
  • Privacy and security concerns in data science applications
  • Responsible AI practices and guidelines

  • Apply data science techniques learned throughout the course to solve a real-world problem
  • Collect, preprocess, analyze, and model data to derive meaningful insights
  • Present findings and recommendations based on the project results
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