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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.

Description

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Course Modules

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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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