From Zero to Pro Data Science & AI Advanced Full Course
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Data Science Modules – Introduction and Brief OverviewWhat Will We CoverModule 1: Data Collection – The Foundation of Data ScienceMod 2: Data Cleaning and Preprocessing – Turning Raw Data into Usable InsightsModule 3: Data Exploration and Analysis (EDA)Module 4: Feature Engineering – Transforming Data into InsightsModule 5: Data Visualization – Communicating Insights EffectivelyModule 6: Machine Learning and Modeling – Building Intelligent SystemsModule 7: Model Evaluation and Validation – Ensuring Reliable PredictionsModule 8: Model Deployment – Bringing Machine Learning Models to LifeModule 9: Big Data Technologies – Managing and Analyzing Massive DatasetsModule 10: Data Ethics and Governance – Responsible AI and Data PracticesModule 11: Business Understanding and Domain ExpertiseMod 12: Communication and Storytelling – Turning Data into Impactful NarrativesWhats Next: Bootcamp Deep Dive
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Week 1: Python Programming BasicsIntroduction to Week 1 Python Programming BasicsDay 1: Introduction to Python and Development SetupDay 2: Control Flow in PythonDay 3: Functions and ModulesDay 4: Data Structures (Lists, Tuples, Dictionaries, Sets)Day 5: Working with StringsDay 6: File HandlingDay 7: Pythonic Code and Project WorkCoding ExerciseResources for the Entire Course
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Week 2: Data Science EssentialsIntroduction to Week 2 Data Science EssentialsDay 1: Introduction to NumPy for Numerical ComputingDay 2: Advanced NumPy OperationsDay 3: Introduction to Pandas for Data ManipulationDay 4: Data Cleaning and Preparation with PandasDay 5: Data Aggregation and Grouping in PandasDay 6: Data Visualization with Matplotlib and SeabornDay 7: Exploratory Data Analysis (EDA) Project
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Week 3: Mathematics for Machine LearningIntroduction to Week 3 Mathematics for Machine LearningDay 1: Linear Algebra FundamentalsDay 2: Advanced Linear Algebra ConceptsDay 3: Calculus for Machine Learning (Derivatives)Day 4: Calculus for Machine Learning (Integrals and Optimization)Day 5: Probability Theory and DistributionsDay 6: Statistics FundamentalsDay 7: Math-Driven Mini Project – Linear Regression from Scratch
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Week 4: Probability and Statistics for Machine LearningIntroduction to Week 4 Probability and Statistics for Machine LearningDay 1: Probability Theory and Random VariablesDay 2: Probability Distributions in Machine LearningDay 3: Statistical Inference – Estimation and Confidence IntervalsDay 4: Hypothesis Testing and P-ValuesDay 5: Types of Hypothesis TestsDay 6: Correlation and Regression AnalysisDay 7: Statistical Analysis Project – Analyzing Real-World Data
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Week 5: Introduction to Machine LearningIntroduction to Week 5 Introduction to Machine LearningDay 1: Machine Learning Basics and TerminologyDay 2: Introduction to Supervised Learning and Regression ModelsDay 3: Advanced Regression Models – Polynomial Regression and RegularizationDay 4: Introduction to Classification and Logistic RegressionDay 5: Model Evaluation and Cross-ValidationMore Than Accuracy: Communicating Model Performance to Non-ExpertsDay 6: k-Nearest Neighbors (k-NN) AlgorithmDay 7: Supervised Learning Mini Project
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Week 6: Feature Engineering and Model EvaluationIntroduction to Week 6 Feature Engineering and Model EvaluationDay 1: Introduction to Feature EngineeringDay 2: Data Scaling and NormalizationDay 3: Encoding Categorical VariablesDay 4: Feature Selection TechniquesWhy This, Not That: Explaining Feature Importance to Domain ExpertsDay 5: Creating and Transforming FeaturesDay 6: Model Evaluation TechniquesDay 7: Cross-Validation and Hyperparameter Tuning
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Week 7: Advanced Machine Learning AlgorithmsIntroduction to Week 7 Advanced Machine Learning AlgorithmsDay 1: Introduction to Ensemble LearningDay 2: Bagging and Random ForestsDay 3: Boosting and Gradient BoostingDay 4: Introduction to XGBoostDay 5: LightGBM and CatBoostDay 6: Handling Imbalanced DataDay 7: Ensemble Learning Project – Comparing Models on a Real Dataset
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Week 8: Model Tuning and OptimizationIntroduction to Week 8 Model Tuning and OptimizationDay 1: Introduction to Hyperparameter TuningDay 2: Grid Search and Random SearchDay 3: Advanced Hyperparameter Tuning with Bayesian OptimizationSmarter Search: Defending Hyperparameter Optimization StrategyDay 4: Regularization Techniques for Model OptimizationDay 5: Cross-Validation and Model Evaluation TechniquesDay 6: Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCVDay 7: Optimization Project – Building and Tuning a Final Model
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Week 9: Neural Networks and Deep Learning FundamentalsIntroduction to Week 9 Neural Networks and Deep Learning FundamentalsDay 1: Introduction to Deep Learning and Neural NetworksDay 2: Forward Propagation and Activation FunctionsDay 3: Loss Functions and BackpropagationDay 4: Gradient Descent and Optimization TechniquesDay 5: Building Neural Networks with TensorFlow and KerasDay 6: Building Neural Networks with PyTorchDay 7: Neural Network Project – Image Classification on CIFAR-10Build with Intent: Justifying Your Neural Network Architecture
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Week 10: Convolutional Neural Networks (CNNs)Introduction to Week 10 Convolutional Neural Networks (CNNs)Day 1: Introduction to Convolutional Neural NetworksDay 2: Convolutional Layers and FiltersDay 3: Pooling Layers and Dimensionality ReductionDay 4: Building CNN Architectures with Keras and TensorFlowDay 5: Building CNN Architectures with PyTorchDay 6: Regularization and Data Augmentation for CNNsDay 7: CNN Project – Image Classification on Fashion MNIST or CIFAR-10
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Week 11: Recurrent Neural Networks (RNNs) and Sequence ModelingIntroduction to Week 11 Recurrent Neural Networks (RNNs) and Sequence ModelingDay 1: Introduction to Sequence Modeling and RNNsDay 2: Understanding RNN Architecture and Backpropagation Through Time (BPTT)Day 3: Long Short-Term Memory (LSTM) NetworksDay 4: Gated Recurrent Units (GRUs)Day 5: Text Preprocessing and Word Embeddings for RNNsDay 6: Sequence-to-Sequence Models and ApplicationsDay 7: RNN Project – Text Generation or Sentiment Analysis
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Week 12: Transformers and Attention MechanismsIntroduction to Week 12 Transformers and Attention MechanismsDay 1: Introduction to Attention MechanismsDay 2: Introduction to Transformers ArchitectureDay 3: Self-Attention and Multi-Head Attention in TransformersDay 4: Positional Encoding and Feed-Forward NetworksDay 5: Hands-On with Pre-Trained Transformers – BERT and GPTDay 6: Advanced Transformers – BERT Variants and GPT-3Day 7: Transformer Project – Text Summarization or TranslationBERT or GPT? Advising on the Best Tool for Document Summarization
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Week 13: Transfer Learning and Fine-TuningIntroduction to Week 13 Transfer Learning and Fine-TuningDay 1: Introduction to Transfer LearningDay 2: Transfer Learning in Computer VisionDay 3: Fine-Tuning Techniques in Computer VisionDay 4: Transfer Learning in NLPDay 5: Fine-Tuning Techniques in NLPDay 6: Domain Adaptation and Transfer Learning ChallengesDay 7: Transfer Learning Project – Fine-Tuning for a Custom TaskStart Smart: Making the Case for Transfer Learning in Production
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Machine Learning Algorithms and ImplementationsIntroduction to Machine Learning Algorithms1. Linear Regression Implementation in Python2. Ridge and Lasso Regression Implementation in Python3. Polynomial Regression Implementation in Python4. Logistic Regression Implementation in Python5. K-Nearest Neighbors (KNN) Implementation in Python6. Support Vector Machines (SVM) Implementation in Python7. Decision Trees Implementation in Python8. Random Forests Implementation in Python9. Gradient Boosting Implementation in Python10. Naive Bayes Implementation in Python11. K-Means Clustering Implementation in Python12. Hierarchical Clustering Implementation in Python13. DBSCAN (Density-Based Spatial Clustering of Applications w Noise)14. Gaussian Mixture Models(GMM) Implementation in Python15. Principal Component Analysis (PCA) Implementation in Python16. t-Distributed Stochastic Neighbor Embedding (t-SNE) Implementation in Python17. Autoencoders Implementation in Python18. Self-Training Implementation in Python19. Q-Learning Implementation in Python20. Deep Q-Networks (DQN) Implementation in Python21. Policy Gradient Methods Implementation in Python22. One-Class SVM Implementation in Python23. Isolation Forest Implementation in Python24. Convolutional Neural Networks (CNNs) Implementation in Python25. Recurrent Neural Networks (RNNs) Implementation in Python26. Long Short-Term Memory (LSTM) Implementation in Python27. Transformers Implementation in Python
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Complete TensorFlow Tutorials1. What is Machine Learning in the context of TensorFlow?2. Introduction to TensorFlow3. TensorFlow vs. Other Machine Learning frameworks4. Installing TensorFlow5. Setting up your Development Environment6. Verifying the Installation7. Introduction to Tensors8. Tensor Operations9. Constants, Variables, and Placeholders10. TensorFlow Computational Graph11. Creating and Running a TensorFlow Session12. Managing Graphs and Sessions13. Building a Simple Feedforward Neural Network14. Activation Functions15. Loss Functions and Optimizers16. Introduction to Keras API17. Building Complex Models with Keras18. Training and Evaluating Models19. Introduction to CNNs20. Building and Training CNNs with TensorFlow21. Transfer Learning with Pre-trained CNNs22. Introduction to RNNs23. Building and Training RNNs with TensorFlow24. Applications of RNNs: Language Modeling, Time Series Prediction25. Saving and Loading Models26. TensorFlow Serving for Model Deployment27. TensorFlow Lite for Mobile and Embedded Devices28. Introduction to Distributed Computing with TensorFlow29. TensorFlow’s Distributed Execution Framework30. Scaling TensorFlow with TensorFlow Serving and Kubernetes31. Introduction to TFX32. Building End-to-End ML Pipelines with TFX33. Model Validation, Transform, and Serving with TFX34. Image Classification35. Natural Language Processing36. Recommender Systems37. Object Detection38. Building a Sentiment Analysis Model39. Creating an Image Recognition System40. Developing a Time Series Prediction Model41. Implementing a Chatbot42. Generative Adversarial Networks (GANs)43. Reinforcement Learning with TensorFlow44. Quantum Machine Learning with TensorFlow Quantum45. TensorFlow Documentation and Tutorials46. Online Courses and Books47. TensorFlow Community and Forums48. Summary of Key Concepts49. Next Steps in Your TensorFlow Journey
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Complete PyTorch Tutorials0. What will we cover1. Introduction to PyTorch2. Getting Started with PyTorch3. Working with Tensors4. Autograd and Dynamic Computation Graphs5. Building Simple Neural Networks6. Loading and Preprocessing Data7. Model Evaluation and Validation8. Advanced Neural Network Architectures9. Transfer Learning and Fine-Tuning10. Handling Complex Data11. Model Deployment and Production12. Debugging and Troubleshooting13. Distributed Training and Performance Optimization14. Custom Layers and Loss Functions15. Research-oriented Techniques16. Integration with Other Libraries17. Contributing to PyTorch and Community Engagement
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Hands-on Projects on Data Science in Python1: Basic Calculator using Python2: Image Classifier using Keras and TensorFlow3: Simple Chatbot using predefined responses4: Spam Email Detector using Scikit-learn5: Handwritten Digit Recognition with MNIST dataset6: Sentiment Analysis on text data using NLTK7: Movie Recommendation System using cosine similarity8: Predict House Prices with Linear Regression9: Weather Forecasting using historical data10: Basic Neural Network from scratch11: Stock Price Prediction using historical data w/ simple Linear Regression12: Predict Diabetes using logistic regression13: Dog vs. Cat Classifier with CNN14: Tic-Tac-Toe AI using Minimax Algorithm15: Credit Card Fraud Detection using Scikit-learn16: Iris Flower Classification using decision trees17: Simple Personal Assistant using Python speech libraries18: Text Summarizer using Gensim19: Fake Product Review Detection using NLP techniques20: Detect Emotion in Text using Natural Language Toolkit (NLTK)21: Book Recommendation System using collaborative filtering22: Predict Car Prices using Random Forest23: Identify Fake News using Naive Bayes24: Create a Resume Scanner using keyword extraction25: Customer Churn Prediction using classification algorithms26: Named Entity Recognition (NER) using spaCy27: Predict Employee Attrition using XGBoost28. Disease Prediction (e.g., Heart Disease) using ML algorithms29. Movie Rating Prediction using Collaborative Filtering30. Automatic Essay Grading using BERT
Welcome to the Data Science Mastery: Complete Data Science Bootcamp 2025! This comprehensive Data Science Bootcamp is designed to equip you with end-to-end data science skills, empowering you to become a skilled Data Scientist ready to tackle real-world challenges. Whether you’re an absolute beginner or looking to sharpen your expertise, this Data Science Bootcamp offers a structured, hands-on learning experience to guide you from fundamentals to advanced techniques.(AI)
In this Data Science Bootcamp 2025, you’ll start with the core fundamentals of Data Science, including Python programming, data preprocessing, data visualization, and exploratory data analysis (EDA). As you progress, you’ll explore advanced topics like machine learning algorithms, deep learning, natural language processing (NLP), and time series analysis. You’ll also gain hands-on experience with industry-standard Data Science tools and libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch.
This Data Science Bootcamp emphasizes practical learning, with real-world projects integrated into every module. You’ll work with large datasets, optimize machine learning models, and learn to deploy data science solutions effectively.
Why Choose the Data Science Mastery Bootcamp?
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Comprehensive Curriculum: Cover Python, Data Visualization, Machine Learning, and Deep Learning
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Hands-On Projects: Real-world Data Science projects in every module
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Master Data Science Tools: Learn Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch
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Structured Learning Path: Beginner-friendly to advanced Data Science techniques
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Real-World Applications: Solve real-world problems using Data Science solutions
By the end of the Data Science Mastery Bootcamp 2025, you’ll have the confidence and hands-on experience to build Data Science models, analyze complex datasets, and drive data-driven decisions in any industry.
Whether you’re aiming to become a Data Scientist, a Machine Learning Engineer, or a leader in data-driven innovation, this Data Science Bootcamp is your gateway to success in the Data Science industry.
Join the Data Revolution Today – Enroll in the Data Science Mastery: Complete Data Science Bootcamp 2025 and take your first step towards becoming a Data Science expert!
What's included
- 48.5 hours on-demand video
- 1 article
- 3 downloadable resources
- Access on mobile and TV
- Certificate of completion