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Full Stack Python: Mastering Development, Data Science, and Machine Learning Excellence

From Foundational Programming to Advanced Data Analysis and Intelligent Applications

Duraion: 6 Months
Fee: Rs.48000
Level: Intermediate

Prerequisite knowledge:


What You'll Learn

  • Master the fundamentals of Python programming, including core syntax, control statements, functions, and file handling.
  • Understand and implement key data structures in Python, such as lists, tuples, sets, and dictionaries.
  • Gain proficiency in object-oriented programming (OOP) concepts and apply them to build reusable and modular code.
  • Explore the essentials of data science with Python, including data manipulation, visualization, exploratory data analysis, and statistical analysis.
  • Dive into supervised machine learning techniques, including linear regression, logistic regression, decision trees, support vector machines (SVM), and k-nearest neighbors (KNN).
  • Uncover the power of unsupervised learning through clustering algorithms, dimensionality reduction, and anomaly detection.
  • Evaluate and validate machine learning models using various metrics and cross-validation techniques.
  • Learn feature engineering and selection techniques to enhance model performance and handle different types of data.
  • Harness the capabilities of natural language processing (NLP) for sentiment analysis, named entity recognition, and topic modeling.
  • Discover the foundations of reinforcement learning and its applications in solving complex problems.
  • Build a full stack applications that integrates data science and machine learning components.
  • Apply your skills and knowledge to real-world AI case studies, gaining practical experience and insights.

By the end of this course, you'll have a strong foundation in Python programming, data science, and machine learning. You'll be equipped with the skills and practical experience needed to develop intelligent applications and tackle data-driven challenges.


Full Stack Python: Mastering Development, Data Science, and Machine Learning Excellence

Are you ready to unlock the full potential of Python? Join our comprehensive Full Stack Python course and embark on a transformative journey in development, data science, and machine learning.

Course Highlights

  • Master Python Programming: From the foundational concepts to advanced techniques, become fluent in Python syntax, control flow, functions, and file handling. Gain the skills to build robust and efficient applications.

  • Harness Data Science: Dive into the world of data science and learn how to analyze, manipulate, and visualize data using Python libraries such as Pandas and Matplotlib. Uncover hidden patterns and gain valuable insights from your data.

  • Supercharge Machine Learning: Explore the exciting field of machine learning and develop predictive models using algorithms such as linear regression, logistic regression, decision trees, support vector machines (SVM), and k-nearest neighbors (KNN). Gain the expertise to make accurate predictions and informed business decisions.

  • Unleash Artificial Intelligence: Discover the power of AI with practical hands-on projects and real-world case studies. Delve into natural language processing (NLP), sentiment analysis, named entity recognition, and topic modeling. Understand the foundations of reinforcement learning and its applications in solving complex problems.

  • Real-World Applications: Apply your skills to tackle real-world challenges and develop intelligent applications. Gain practical experience through hands-on projects and gain insights from industry experts.

Why Choose Our Full Stack Python Course?

  • Comprehensive Curriculum: Our meticulously designed curriculum covers all aspects of Python development, data science, and machine learning. Build a solid foundation and progress towards advanced topics at your own pace.

  • Experienced Instructors: Learn from seasoned instructors with extensive industry experience. Benefit from their practical insights, tips, and real-world examples that bring the concepts to life.

  • Hands-on Projects: Apply your knowledge through hands-on projects and gain valuable experience. Develop a full stack web application, analyze real datasets, and create intelligent models that solve complex problems.

  • Flexible Learning: Our physical as well as online learning platform allows you to learn from anywhere, at any time, and at your own pace. Fit your studies around your schedule and access the course materials whenever you need them.

  • Supportive Learning Community: Join a vibrant community of learners and engage in discussions, collaborate on projects, and seek guidance from instructors. Connect with like-minded individuals and expand your professional network.

Don't miss this opportunity to become a Full Stack Python expert! Enroll now and unlock the door to endless possibilities in development, data science, and machine learning.

Course Modules

Module 1: Python Programming Basics

  • Introduction to Python and its features
  • Setting up the Python environment
  • Core language syntax, including variables, data types, and operators
  • Decision making with if-else statements and switch-case
  • Control statements and loops (for loop, while loop)
  • Functions and modules, including user-defined functions and built-in modules
  • File handling and string processing, including reading/writing files and string manipulation

Module 2: Data Structures with Python

  • Introduction to data structures (lists, tuples, sets, dictionaries)
  • Operations and manipulation of data structures
  • Time and space complexity analysis
  • Sorting and searching algorithms
  • Advanced data structures (stacks, queues, linked lists)
  • Algorithm design and problem-solving techniques

Module 3: Object-Oriented Programming (OOP) with Python

  • Introduction to OOP concepts
  • Objects, classes, and inheritance
  • Encapsulation and abstraction
  • Polymorphism and method overriding
  • Exception handling

Module 4: Data Science with Python

  • Introduction to data science and its applications
  • Data manipulation and analysis with Pandas, including data cleaning, merging, and reshaping
  • Data visualization with Matplotlib and Seaborn, including creating various plots and charts
  • Exploratory data analysis techniques, including summary statistics and data exploration
  • Statistical analysis and hypothesis testing using Python libraries
  • Introduction to machine learning concepts and workflows

Module 5: Supervised Machine Learning

  • Introduction to supervised learning
  • Linear regression
  • Logistic regression
  • Decision trees and random forests
  • Support vector machines (SVM)
  • Naive Bayes classifiers
  • K-nearest Neighbors (KNN)
  • Evaluation metrics for classification and regression tasks

Module 6: Unsupervised Machine Learning

  • Introduction to unsupervised learning
  • Clustering algorithms (k-means, hierarchical clustering)
  • Dimensionality reduction techniques (PCA, t-SNE)
  • Anomaly detection
  • Association rule learning

Module 7: Model Evaluation and Validation

  • Evaluation metrics for classification and regression models
  • Cross-validation techniques (k-fold, stratified)
  • Overfitting and underfitting
  • Hyperparameter tuning
  • Model selection and ensemble methods

Module 8: Feature Engineering and Selection

  • Feature engineering techniques
  • Handling missing data
  • Encoding categorical variables
  • Feature scaling and normalization
  • Feature selection methods
  • Feature extraction using dimensionality reduction

Module 9: Natural Language Processing (NLP)

  • Introduction to NLP and its applications
  • Text preprocessing techniques (tokenization, stemming, lemmatization)
  • Bag-of-words and TF-IDF representations
  • Sentiment analysis
  • Named Entity Recognition (NER)
  • Topic modeling (LDA, LSA)
  • Word embeddings (Word2Vec, GloVe)

Module 10: Reinforcement Learning

  • Introduction to reinforcement learning
  • Markov Decision Processes (MDPs)
  • Value iteration and policy iteration
  • Q-learning and Deep Q-networks (DQN)
  • Policy gradient methods
  • Applications of reinforcement learning

Module 11: Artificial Neural Networks

  • Introduction to artificial neural networks (ANN)
  • Basics of feedforward neural networks
  • Activation functions (sigmoid, ReLU, softmax)
  • Backpropagation algorithm
  • Building and training neural network architectures
  • Regularization techniques (dropout, L1/L2 regularization)
  • Introduction to deep learning frameworks (TensorFlow, Keras, PyTorch)

Module 12: Capstone Project

  • Apply the knowledge gained throughout the course to a real-world project
  • Develop a full stack web application with data science and machine learning components
  • Implement data analysis, visualization, and machine learning techniques to solve a specific problem
  • Present and showcase the capstone project to the instructor and peers

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