Daily Curriculum Resources#

Week 1: Python Basics and Numerical Operations with Jupyter Notebooks#

Lesson 1: Python Basics - Syntax, Variables#

Lesson 2: Data Types and Operators#

Lesson 3: Control Structures - Loops#

Lesson 4: Control Structures - Conditional Statements#

Lesson 5: Functions and Modules#

Week 2: Introduction to Mathematical Concepts in Python#

Lesson 6: Linear Algebra - Introduction, Vectors#

Lesson 7: Linear Algebra - Matrices, Matrix Operations#

Lesson 8: Calculus - Derivatives, Concept and Applications#

Lesson 9: Calculus - Integrals, Fundamental Theorems#

Lesson 10: Probability and Statistics - Basic Concepts, Relevant Distributions#

Week 3: Data Preprocessing#

Lesson 11: Introduction to Data Preprocessing in Python#

  • Resource 1: Data Preprocessing in Python: A Practical Guide (Comprehensive guide covering various data preprocessing concepts)

  • Resource 2: Python for Data Science - Data Preprocessing (Online course with a focus on data preprocessing in Python)

  • Math Application: Jupyter Notebook Example:

    • Explore data types and scales using Python libraries.

    • Perform basic statistical calculations (mean, median, mode, variance).

    • Visualize data distributions and identify outliers.

Lesson 12: Handling Missing Data with Python#

  • Resource 1: Handling Missing Data with Pandas (Tutorial on detecting and handling missing data using Pandas)

  • Resource 2: Imputing Missing Values in Python (Techniques for imputing missing data in Python)

  • Math Application: Jupyter Notebook Example:

    • Implement mean, median, mode imputation.

    • Explore deterministic and stochastic regression imputation techniques.

    • Analyze the impact of different imputation methods on data distributions.

Lesson 13: Data Normalization and Scaling using Python#

  • Resource 1: Feature Scaling with scikit-learn (Official scikit-learn guide on data scaling and normalization)

  • Resource 2: Normalization vs Standardization in Machine Learning (Quantitative analysis of normalization and standardization)

  • Math Application: Jupyter Notebook Example:

    • Apply Z-score normalization and min-max scaling.

    • Understand the mathematical rationale behind each scaling method.

    • Compare the effects of different scaling techniques on dataset features.

Lesson 14: Encoding Categorical Data in Python#

Lesson 15: Splitting Data into Training and Test Sets in Python#

Week 4: Exploratory Data Analysis (EDA)#

Lesson 16: Introduction to EDA and Data Visualization in Python#

  • Resource 1: Exploratory Data Analysis with example in Jupyter notebook (In-depth tutorial on EDA using a case study with Jupyter Notebook)

  • Resource 2: Step-by-Step EDA using Python (Detailed guide on performing EDA with Python)

  • Math Application: Jupyter Notebook Example:

    • Perform basic descriptive statistics (mean, median, mode, etc.).

    • Visualize data distributions using various plots.

    • Identify and analyze outliers and patterns in data.

Lesson 17: Implementing Descriptive Statistics for EDA in Python#

  • Resource 1: Descriptive Statistics in Python (Tutorial on calculating descriptive statistics in Python)

  • Resource 2: Practical Guide to Descriptive Statistics in Python (Guide covering practical aspects of descriptive statistics in Python)

  • Math Application: Jupyter Notebook Example:

    • Calculate measures of central tendency (mean, median, mode).

    • Compute measures of dispersion (standard deviation, variance, range).

    • Analyze data distributions and identify skewness.

Lesson 18: Visualization Techniques for Data Distribution in Python#

  • Resource 1: Data Visualization in Python (Comprehensive guide to data visualization in Python)

  • Resource 2: Histograms and Box Plots in Python (Matplotlib gallery showcasing histograms and box plots)

  • Math Application: Jupyter Notebook Example:

    • Create histograms and box plots to visualize data distributions.

    • Understand and interpret skewness in data distributions.

    • Identify outliers and analyze their impact on the overall data set.

Lesson 19: Correlation Analysis using Python#

  • Resource 1: Correlation Analysis in Python (Guide on performing correlation analysis using Python libraries)

  • Resource 2: Exploring Correlation in Python (In-depth exploration of correlation analysis in Python)

  • Math Application: Jupyter Notebook Example:

    • Calculate Pearson and Spearman correlation coefficients.

    • Visualize correlation matrices using heatmaps.

    • Interpret correlation in the context of data features.

Lesson 20: Feature Selection and Importance in Python#

  • Resource 1: Feature Selection Techniques in Python (Detailed overview of feature selection techniques in Python)

  • Resource 2: Understanding Feature Importance in Python (Kaggle tutorial on understanding feature importance)

  • Math Application: Jupyter Notebook Example:

    • Implement techniques like information gain and Gini impurity for feature selection.

    • Analyze feature importance using model-based approaches.

    • Evaluate the impact of selected features on model performance.

Week 5: Supervised Learning - Regression#

Lesson 21: Introduction to Regression Analysis in Python#

  • Resource 1: Linear Regression in Python – A Step-by-Step Guide (Comprehensive tutorial on implementing linear regression in Python using the scikit-learn library, including data importing, understanding data set, and building a linear regression model)

  • Resource 2: Implement Linear Regression in Python using Jupyter Notebook (Tutorial covering the basics of linear regression, data wrangling, and fitting the model using Python and Jupyter Notebook)

  • Math Application: Jupyter Notebook Example:

    • Import and analyze a housing dataset.

    • Use pandas for data manipulation and seaborn for pairplot visualizations.

    • Build a simple linear regression model to predict housing prices.

Lesson 22: Implementing Multiple Linear Regression in Python#

  • Resource 1: Multiple Linear Regression in Python (Detailed guide on multiple linear regression, including data preparation and model building)

  • Resource 2: Multivariate Linear Regression Tutorial with Real Python (Explanation of multiple linear regression with an example using Python)

  • Math Application: Jupyter Notebook Example:

    • Explore the concept of multivariate calculus and regression coefficients.

    • Implement multiple linear regression on datasets with more than one independent variable.

    • Interpret the coefficients and understand how each variable influences the prediction.

Lesson 23: Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression#

  • Resource 1: Advanced Linear Regression With Python (Guide on polynomial regression, Lasso, and Ridge regression in Python)

  • Resource 2: Polynomial Regression in Python (Tutorial on polynomial regression and its application in Python)

  • Math Application: Jupyter Notebook Example:

    • Implement polynomial regression to model nonlinear relationships.

    • Explore Lasso and Ridge regression for regularization and overfitting prevention.

    • Understand the mathematical foundations of these advanced techniques.

Lesson 24: Regression Model Evaluation Metrics in Python#

  • Resource 1: Regression Model Accuracy – R-squared and More (Guide on evaluating regression models using R-squared and other metrics)

  • Resource 2: Model Evaluation Metrics in Python (Detailed explanation of various regression model evaluation metrics)

  • Math Application: Jupyter Notebook Example:

    • Calculate Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared values.

    • Use these metrics to evaluate the accuracy and efficacy of regression models.

    • Understand the limitations and appropriate use cases for each metric.

Lesson 25: Addressing Overfitting and Underfitting in Regression Models#

  • Resource 1: Overfitting and Underfitting in Machine Learning (Comprehensive guide on the concepts of overfitting and underfitting in machine learning models)

  • Resource 2: Dealing with Overfitting and Underfitting in Python (Practical guide on addressing overfitting and underfitting in regression models)

  • Math Application: Jupyter Notebook Example:

    • Apply regularization methods like Lasso and Ridge regression to combat overfitting.

    • Understand the bias-variance tradeoff and its implications in model training.

    • Implement model validation techniques to assess the generalization ability of regression models.

Week 6: Supervised Learning - Classification#

Lesson 26: Introduction to Classification and Logistic Regression in Python#

  • Resource 1: Logistic Regression in Python - A Step-by-Step Guide (Comprehensive tutorial on building logistic regression models, including dealing with missing data, categorical data, and building the logistic regression model)

  • Resource 2: Logistic Regression in Python – Real Python (Detailed explanation of the logistic function and its application in logistic regression)

  • Math Application: Jupyter Notebook Example:

    • Implement logistic regression on datasets like the Titanic dataset.

    • Understand the logistic function and use it to predict binary outcomes.

    • Evaluate model performance using confusion matrices and classification accuracy metrics.

Lesson 27: K-Nearest Neighbors (K-NN) Algorithm in Python#

  • Resource 1: Machine Learning with scikit-learn (DataCamp course covering K-NN and other machine learning techniques)

  • Resource 2: K-Nearest Neighbors in Python (scikit-learn documentation for K-NN, providing both theoretical concepts and Python examples)

  • Math Application: Jupyter Notebook Example:

    • Implement the K-NN algorithm using Python libraries.

    • Explore different distance metrics like Euclidean and Manhattan.

    • Analyze the impact of ‘k’ value and distance metrics on model performance.

Lesson 28: Support Vector Machines (SVM) for Linear and Nonlinear Data#

  • Resource 1: Machine Learning with scikit-learn (DataCamp course including modules on SVMs)

  • Resource 2: Support Vector Machines in Python (scikit-learn documentation providing a detailed explanation of SVMs)

  • Math Application: Jupyter Notebook Example:

    • Apply SVM to linear and nonlinear datasets.

    • Explore the concept of hyperplanes, margin maximization, and kernel methods.

    • Evaluate SVM models on various datasets and understand their performance.

Lesson 29: Decision Trees and Rule-Based Models in Python#

  • Resource 1: Machine Learning with scikit-learn (DataCamp course covering decision trees and other rule-based models)

  • Resource 2: Decision Trees in Python (scikit-learn documentation on decision trees)

  • Math Application: Jupyter Notebook Example:

    • Build decision tree models for classification and regression problems.

    • Calculate entropy and information gain for tree splits.

    • Interpret decision tree models and analyze their decision paths.

Lesson 30: Naive Bayes Classifier Implementation#

  • Resource 1: Machine Learning with scikit-learn (DataCamp course including Naive Bayes classifier tutorials)

  • Resource 2: Naive Bayes in Python (scikit-learn documentation on Naive Bayes classifiers)

  • Math Application: Jupyter Notebook Example:

    • Implement Naive Bayes algorithms for different types of data.

    • Understand the basics of probability and Bayes’ theorem in classification.

    • Evaluate the classifier’s performance and understand its assumptions and limitations.

Week 7: Ensemble Methods#

Lesson 31: Introduction to Ensemble Learning Techniques#

  • Resource 1: DataCamp’s Ensemble Modeling Tutorial provides a conceptual overview of ensemble learning, detailing various ensemble methods and their real-world applications.

  • Resource 2: Machine Learning Mastery’s Ensemble Machine Learning Algorithms in Python with scikit-learn offers a practical guide to creating ensemble models in Python.

  • Math Application:

    • Understand the mathematical principles behind combining models and weighted averaging.

    • Explore the use of ensemble methods to improve prediction accuracy.

Lesson 32: Bagging and Random Forests#

  • Resource 1: How to Develop a Bagging Ensemble with Python covers the concept of bagging and how to implement it using Python.

  • Resource 2: DataCamp’s tutorial Ensemble Learning Techniques Tutorial on Kaggle includes practical examples using Jupyter notebooks.

  • Math Application:

    • Delve into bootstrap sampling and its role in the bagging process.

    • Explore the mathematical underpinnings of Random Forests and their implementation.

Lesson 33: Boosting Algorithms - AdaBoost#

  • Resource 1: Machine Learning Mastery’s Boosting Algorithms provides a deep dive into boosting algorithms like AdaBoost.

  • Resource 2: GitHub repository - ensemble-methods-notebooks, offers Jupyter notebooks for practical AdaBoost implementation.

  • Math Application:

    • Study AdaBoost’s algorithmic structure, focusing on weight updates in the boosting process.

Lesson 34: Gradient Boosting Machines (GBM) and XGBoost#

  • Resource 1: Machine Learning Mastery’s tutorial on Boosting Algorithms includes Stochastic Gradient Boosting.

  • Resource 2: The GitHub ensemble-methods-notebooks provide practical insights into GBM and XGBoost.

  • Math Application:

    • Investigate gradient descent in boosting and regularization techniques in XGBoost.

    • Understand the mathematical aspects of GBM and XGBoost algorithms.

Lesson 35: Advanced Ensemble Techniques and Their Applications#

  • Resource 1: DataCamp’s Ensemble Modeling Tutorial discusses advanced ensemble techniques such as stacking and blending.

  • Resource 2: Analytics Vidhya’s article on Ensemble Learning covers different advanced ensemble methods.

  • Math Application:

    • Explore the mathematical principles behind stacking and blending.

    • Understand the math behind various advanced ensemble techniques and their applications.

Lesson 35: Advanced Ensemble Techniques and Their Applications#

  • Resource 1: DataCamp’s Ensemble Modeling Tutorial - This tutorial discusses advanced ensemble techniques such as stacking and blending, providing a conceptual overview and practical applications.

  • Resource 2: Analytics Vidhya’s article on Ensemble Learning - This article covers different advanced ensemble methods, offering insights into their workings and use-cases.

  • Math Application:

    • Explore the mathematical principles behind stacking and blending, understanding how these techniques combine different models for improved performance.

Week 8: Unsupervised Learning#

Lesson 36: Introduction to Unsupervised Learning and Clustering Basics#

  • Resource 1: Real Python’s K-Means Clustering in Python: A Practical Guide offers a step-by-step tutorial on performing k-means clustering in Python. This includes evaluating metrics for choosing an appropriate number of clusters and building a k-means clustering pipeline in scikit-learn.

  • Resource 2: Nick McCullum’s guide on K Means Clustering in Python provides an introduction to k-means clustering, explaining the algorithm and its implementation in Python.

  • Math Application:

    • Gain an understanding of the principles of cluster analysis and the k-means algorithm.

    • Explore the mathematical steps involved in k-means, such as selecting centroids, expectation-maximization, and minimizing the sum of squared error (SSE).

Lesson 37: Implementing K-Means Clustering for Different Data Types#

  • Resource 1: Built In’s beginner’s guide on Unsupervised Learning with Python discusses implementing k-means clustering on different data types, using examples like the iris dataset.

  • Resource 2: Real Python’s K-Means Clustering in Python also provides insights into practical implementation details.

  • Math Application:

    • Focus on centroid calculation and convergence criteria in k-means. Understand the mathematical foundation behind grouping data points and recalculating centroids to optimize cluster formation.

Lesson 38: Hierarchical Clustering Techniques#

  • Resource 1: Stack Abuse’s Definitive Guide to Hierarchical Clustering with Python and Scikit-Learn offers a practical approach to implementing hierarchical clustering, including preprocessing steps and feature engineering.

  • Resource 2: DataCamp’s Introduction to Hierarchical Clustering in Python provides an overview of hierarchical clustering algorithms, including both agglomerative and divisive methods.

  • Math Application:

    • Delve into dendrogram interpretation and linkage methods. Understand how hierarchical clustering uses distance measures to form clusters and how dendrograms visually represent these relationships.

Lesson 39: Density-Based Clustering with DBSCAN#

  1. Resource 1: An in-depth tutorial on DBSCAN clustering using Python and Scikit-learn is provided by Machine Learning Knowledge. It covers the DBSCAN algorithm in detail, including key parameters like epsilon (eps) and minPts, and demonstrates an example implementation.

  2. Resource 2: Another useful resource for DBSCAN clustering is found on Renesh Bedre’s blog, which includes an example dataset and discusses the optimal settings for the ε parameter using the k-nearest neighbor method.

  3. Math Application:

    • Delve into the core concepts of density-based clustering.

    • Understand the importance of parameters like epsilon and MinPts in DBSCAN.

    • Implement DBSCAN in Python and analyze its effectiveness in identifying clusters with varying densities.

Lesson 40: Gaussian Mixture Models (GMM) and Expectation-Maximization#

  1. Resource 1: For a thorough understanding of Gaussian Mixture Models (GMM), the Scikit-learn documentation is an excellent resource. It explains the fundamentals of GMMs and their implementation using Scikit-learn.

  2. Resource 2: An additional resource to explore is Machine Learning Plus, which provides a detailed guide on GMMs, including the Expectation-Maximization algorithm.

  3. Math Application:

    • Explore probability distributions and how GMMs model data as a mixture of multiple Gaussian distributions.

    • Understand the Expectation-Maximization algorithm’s role in optimizing the GMM parameters.

    • Apply GMMs in Python for clustering and density estimation tasks, evaluating their performance.

Week 9: Dimensionality Reduction#

Lesson 41: Introduction to Dimensionality Reduction and PCA Basics#

  • Resource 1: DataCamp’s PCA Tutorial offers a comprehensive guide to understanding and applying Principal Component Analysis (PCA) in Python. This tutorial includes data exploration and practical implementation using datasets like Breast Cancer and CIFAR-10.

  • Resource 2: Machine Learning Mastery’s PCA Guide provides insights into calculating PCA from scratch in Python. It includes examples that demonstrate how to calculate PCA using NumPy and scikit-learn.

  • Math Application:

    • Gain an understanding of the covariance matrix, eigenvalues, and eigenvectors in PCA.

    • Explore how PCA reduces dimensionality by transforming data into principal components.

Lesson 42: Implementing PCA in Python#

  • Resource 1: GeeksforGeeks PCA Tutorial explains the step-by-step application of PCA in Python using the iris dataset. It covers importing necessary libraries and understanding the PCA implementation process.

  • Resource 2: Kindson The Genius’ PCA Step-by-Step Guide provides a detailed explanation of implementing PCA in Python, including data standardization and visualization of principal components.

  • Math Application:

    • Learn the steps involved in PCA computation, including data standardization and transformation.

    • Understand how to visualize and interpret the reduced dimensionality data through PCA.

Lesson 43: Singular Value Decomposition (SVD) and Its Applications#

  • Resource 1: Machine Learning Mastery’s guide on SVD provides a comprehensive tutorial on how to calculate the SVD from scratch using Python. This resource is particularly useful for understanding the underlying mechanics of SVD and its implementation in Python.

  • Resource 2: AskPython’s tutorial on SVD offers an overview of SVD, including its basics and implementation using Numpy and scikit-learn. This tutorial is beneficial for those looking to understand the practical aspects of applying SVD in Python.

  • Math Application:

    • In this lesson, you can delve into the concepts of matrix decomposition and its applications, such as dimensionality reduction. You will learn how to decompose a matrix into its constituent U, Sigma (Σ), and V matrices and explore how these matrices are used in various applications like image compression and noise reduction.

Lesson 44: t-SNE Technique for Dimensionality Reduction#

  • Resource 1: Machine Learning Mastery’s guide on t-SNE discusses the use of SVD in dimensionality reduction, which is a foundational step for understanding more complex techniques like t-SNE.

  • Resource 2: To understand t-SNE specifically, it’s recommended to explore online tutorials and resources on platforms like Kaggle and DataCamp, where you can find practical examples of t-SNE applied to high-dimensional data.

  • Math Application:

    • This lesson focuses on the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm, a technique used for dimensionality reduction in high-dimensional datasets. You’ll explore how t-SNE works, its mathematical foundation, and its applications in visualizing high-dimensional data in a lower-dimensional space.

Lesson 45: Other Techniques in Dimensionality Reduction#

  • Resource 1: Machine Learning Mastery’s guide covers SVD for dimensionality reduction, which is a precursor to understanding other advanced techniques such as LDA and autoencoders.

  • Resource 2: Additional dimensionality reduction techniques can be learned through various Python tutorials available online. Websites like Kaggle, DataCamp, and Towards Data Science often have in-depth tutorials and Jupyter Notebook examples for techniques like LDA and autoencoders.

  • Math Application:

    • In this lesson, you’ll explore additional dimensionality reduction techniques like Linear Discriminant Analysis (LDA) and autoencoders. The focus will be on understanding the mathematical principles behind these techniques, such as how LDA maximizes class separability and how autoencoders learn compressed representations of data.

Week 10: Introduction to Neural Networks#

Lesson 46: Understanding Neural Networks and Perceptrons#

  • Resource 1: Real Python’s tutorial on Building a Neural Network & Making Predictions provides an introduction to neural networks, explaining the basics of weights, biases, and the linear regression model used in neural networks.

  • Resource 2: Bryn Mawr College’s Jupyter notebook Artificial_Neural_Networks.ipynb offers practical examples of neural network computations and the backpropagation algorithm.

  • Math Application:

    • Explore activation functions like ReLU and their role in the perceptron model.

    • Understand the basic architecture of neural networks, including how weights and biases influence network behavior.

Lesson 47: Feedforward Neural Networks and Activation Functions#

  • Resource 1: Nick McCullum’s guide on Building and Training an Artificial Neural Network walks through creating a feedforward neural network using TensorFlow and Keras.

  • Resource 2: Machine Learning Mastery’s First Deep Learning Project in Python with Keras provides a step-by-step approach to building a feedforward neural network using Keras.

  • Math Application:

    • Gain insights into the structure and function of network layers and neuron connections.

    • Learn about different activation functions and their impact on the neural network’s ability to learn complex patterns.

Lesson 48: Backpropagation Algorithm in Neural Networks#

  • Resource 1: Bryn Mawr College’s Artificial_Neural_Networks.ipynb explains the backpropagation algorithm, including its mathematical basis and application in training neural networks.

  • Resource 2: Real Python’s article on Python AI: How to Build a Neural Network & Make Predictions covers the principles of neural network training, including error assessment and adjustment of weights.

  • Math Application:

    • Dive deep into the backpropagation algorithm, focusing on its reliance on the chain rule in calculus.

    • Understand how backpropagation is used to minimize prediction errors and update network weights efficiently.

Lesson 49: Training Neural Networks - Loss Functions and Optimizers#

For training neural networks, the choice of loss functions and optimizers is crucial.

  • Resource 1: An article on OpenDataScience emphasizes the importance of considering the dataset’s size and variety when choosing an optimizer. For instance, batch gradient descent might not be ideal for larger, more complex datasets. The same article suggests that the mean squared error (MSE) works for most regression problems, but for target outputs with a large value spread, mean squared logarithmic error (MSLE) may be more suitable.

  • Resource 2: Nick McCullum’s tutorial on building and training an artificial neural network provides a comprehensive guide, including initializing the network, adding layers, and selecting activation functions. It suggests using ReLU (Rectified Linear Unit) as the activation function and emphasizes the importance of experimenting with the number of neurons in hidden layers for optimal performance.

  • Math Application: Explore different loss functions like cross-entropy for binary classification or MSE for predicting real-world quantities.

    • Understand the impact of optimizer choice (e.g., Stochastic Gradient Descent) on training effectiveness, especially in relation to the dataset’s characteristics.

Lesson 50: Evaluating and Tuning Neural Network Performance#

Evaluating and tuning the performance of neural networks involves various strategies, including the use of regularization methods and hyperparameter tuning to avoid overfitting.

  • Resource: LearnOpenCV’s tutorial on training neural networks for beginners provides insights into the evaluation process. It discusses the concept of a loss function, such as the Mean Squared Error (MSE), and the optimization process used to update network weights, like gradient descent. The tutorial also delves into the importance of the learning rate, a critical hyperparameter, in the weight update process.

  • Math Application:

    • Delve into the role of loss functions, like MSE, in evaluating the network’s performance.

    • Understand gradient descent for optimization and the significance of the learning rate in adjusting weights.

Week 11: Deep Learning - Convolutional Neural Networks (CNNs)#

Lesson 51: Introduction to CNNs and Convolutional Layers#

The fundamentals of Convolutional Neural Networks (CNNs) and their architecture, including convolution operations and feature map generation, are pivotal in deep learning.

  • Resource: For an in-depth understanding of CNNs and practical implementation, exploring comprehensive Python tutorials and documentation on platforms like TensorFlow and PyTorch can be immensely helpful. These platforms provide detailed tutorials and examples on creating and training CNNs for various applications.

  • Math Application:

    • Learn about convolution operations and how they contribute to feature map generation in CNNs.

    • Explore the architectural details of CNNs, including the function and implementation of convolutional layers.

Lesson 52: Pooling Layers and CNN Architectures#

  • Resource 1: Nick McCullum’s tutorial on Convolutional Neural Networks provides a comprehensive guide on building and training CNNs, including the implementation of pooling layers. It explains the use of max pooling layers in detail, which are crucial for reducing the spatial dimensions of the input and preventing overfitting.

  • Resource 2: DataCamp’s CNN tutorial offers insights into CNN architectures, focusing on the role of convolution and pooling layers in feature extraction and dimensionality reduction.

  • Math Application:

    • Understand the concept of spatial pooling, including max pooling and average pooling, and how these methods reduce the dimensionality of feature maps.

    • Explore various CNN architectures and how pooling layers affect feature representation and computational efficiency.

Lesson 53: Implementing a Basic CNN for Image Classification#

  • Resource 1: The tutorial by Nick McCullum on Building a Convolutional Neural Network walks through the process of implementing a basic CNN for image classification, explaining each layer’s role and function.

  • Resource 2: DataCamp’s CNN tutorial also covers the basics of implementing CNNs using Python and Keras, suitable for image classification tasks.

  • Math Application:

    • Delve into the calculations involved in filter and feature map generation in CNNs.

    • Understand the transformation of input images through convolutional layers to produce feature maps that are used for classification.

Lesson 54: Advanced CNN Techniques for Image Classification#

  • Resource 1: Nick McCullum’s CNN tutorial includes advanced techniques such as adding more convolutional and pooling layers, which can enhance image classification models.

  • Resource 2: Chan’s Jupyter blog on CNN Basics provides an in-depth look at CNN operations, including advanced concepts that can be implemented for image classification.

  • Math Application:

    • Explore the mathematical basis behind dropout and batch normalization techniques in CNNs.

    • Understand how these advanced techniques prevent overfitting and improve the generalization ability of CNN models.

Lesson 55: Transfer Learning with CNNs#

  • Resource 1: The tutorial by Nick McCullum on Convolutional Neural Networks provides insights into how CNNs can be used for transfer learning, leveraging pre-trained models to improve performance on new tasks.

  • Resource 2: DataCamp’s comprehensive guide to CNNs touches on the concept of transfer learning, explaining how existing CNN architectures can be adapted for new image classification tasks.

  • Math Application:

    • Study the concept of knowledge transfer in the context of CNNs, including fine-tuning and feature extraction techniques.

    • Understand the mathematical principles behind leveraging pre-trained models and adapting them to new datasets or problems.

Week 12: Deep Learning - Recurrent Neural Networks (RNNs)#

Lesson 56: Introduction to RNNs and Their Architecture#

For an introduction to Recurrent Neural Networks (RNNs) and their unique architecture, the following resources can provide comprehensive guidance:

Lesson 57: Implementing Long Short-Term Memory (LSTM) Networks#

To learn about and implement LSTM networks:

Lesson 58: Building a Basic RNN/LSTM for Sequence Modeling#

For hands-on experience in building a basic RNN/LSTM model:

  • Resource 1: Machine Learning Mastery’s Time Series Prediction with LSTM provides a practical example of using LSTM for sequence modeling.

  • Resource 2: Analytics Vidhya’s Complete LSTM Tutorial includes a step-by-step guide on implementing LSTM in Python.

  • Math Application:

    • Explore backpropagation through time and its challenges.

    • Gain insight into the sequential data processing in LSTM and RNN models.

Lesson 59: RNNs for Time Series Analysis#

For applying RNNs in time series analysis and forecasting:

  • Resource 1: Time-series data analysis using LSTM (Tutorial) on Kaggle provides a practical approach to using LSTM for time series analysis.

  • Resource 2: Time Series Prediction with LSTM from Machine Learning Mastery shows how to use LSTM for time series prediction.

  • Math Application:

    • Understand time series forecasting methods and sequence modeling.

    • Learn about the mathematical concepts in modeling time-dependent data using RNNs and LSTMs.

Lesson 60: Utilizing RNNs in Natural Language Processing (NLP)#

To explore the use of RNNs in NLP tasks:

  • Resource 1: Kaggle’s Time-series data analysis using LSTM (Tutorial) provides insights into LSTM applications, which can be extended to NLP.

  • Resource 2: The Complete LSTM Tutorial With Implementation from Analytics Vidhya discusses LSTM applications in NLP.

  • Math Application:

    • Explore word embeddings, vector spaces, and sequence-to-sequence models in NLP.

    • Delve into the recurrent structure of RNNs and LSTMs and their effectiveness in handling sequential language data.

Week 13: Reinforcement Learning#

Lesson 61: Fundamentals of Reinforcement Learning#

For an introduction to the concepts and frameworks of reinforcement learning:

  • Resource 1: Easy Start Into Reinforcement Learning Using Jupyter Notebook offers a beginner-friendly introduction to reinforcement learning. It uses a code-first approach in a Jupyter Notebook, making it less intimidating for newcomers.

  • Resource 2: A Hands-on Introduction to Reinforcement Learning with Python from Analytics Vidhya provides a practical guide to implementing reinforcement learning in Python, including basic concepts and implementation steps.

  • Math Application: Dive into reward function optimization and the decision-making processes, understanding how agents interact with their environment to maximize specific goals.

Lesson 62: Markov Decision Processes in Reinforcement Learning#

Exploring Markov Decision Processes (MDPs) in reinforcement learning:

  • Resource 1: “Reinforcement Q-Learning from Scratch in Python with OpenAI Gym” on LearnDataSci provides a practical guide on implementing Q-Learning, a core concept in MDPs, using Python and OpenAI Gym.

  • Math Application: Focus on transition probability matrices and state-value functions to understand the mathematical underpinnings of MDPs and their role in reinforcement learning.

Lesson 63: Basics of Q-Learning#

Understanding and implementing the Q-learning algorithm:

  • Resource 1: The LearnDataSci tutorial on Q-Learning offers a hands-on approach to understanding and implementing the algorithm using Python and OpenAI Gym.

  • Math Application: Delve into the Bellman equation and explore its role in value estimation, which is crucial in Q-Learning.

Lesson 64: Deep Q-Networks (DQN) and Their Applications#

Exploring Deep Q-Networks (DQN) in complex environments:

  • Resource 1: For a practical approach to implementing DQNs, the previously mentioned LearnDataSci tutorial on Q-Learning can be a starting point, as it lays the groundwork for understanding reinforcement learning environments that DQNs operate in.

  • Math Application: Focus on integrating neural networks with Q-learning, exploring the loss function in DQN and how it combines traditional reinforcement learning techniques with deep learning.

Lesson 65: Policy Gradient Methods in Reinforcement Learning#

Learning about policy gradient methods and their implementation:

  • Resource 1: The Easy Start Into Reinforcement Learning Using Jupyter Notebook provides insights into reinforcement learning that can be extended to understand policy gradient methods.

  • Math Application: Investigate policy optimization techniques and the use of gradient ascent in policy gradient methods, understanding how these methods continuously improve their policy based on the learning from the environment.

Week 15: MLOps#

Lesson 71: Introduction to MLOps and Machine Learning Lifecycle#

Lesson 72: Model Versioning and Experiment Tracking#

  • Resource 1: ProjectPro’s MLOps Tutorial covers essential aspects of model versioning, offering insights into how to track different versions of ML models.

  • Math Application:

    • Delve into statistical analysis for evaluating model performance across different versions, a key aspect of experiment tracking in MLOps.

Lesson 73: CI/CD in Machine Learning#

  • Resource 1: DataCamp’s Machine Learning, Pipelines, Deployment and MLOps Tutorial - This tutorial provides insights into the role of CI/CD in machine learning, discussing containerization and Kubernetes.

  • Math Application:

    • Explore automated testing and validation strategies, essential in the CI/CD pipeline for ensuring the robustness and reliability of ML models.

Lesson 74: Model Monitoring and Maintenance#

  • Resource 1: ProjectPro’s MLOps Python Tutorial for Beginners - Offers insights into operationalizing training, which is a crucial aspect of model monitoring and maintenance.

  • Math Application:

    • Study anomaly detection and performance drift in model behavior to understand the mathematical principles involved in monitoring ML models.

Lesson 75: Overview of MLOps Tools and Platforms#

For an introduction to various MLOps tools and platforms:

  • Resource 1: DataCamp’s article on 17 Top MLOps Tools provides an overview of key MLOps tools, including their features and applications.

  • Resource 2: Neptune.ai’s MLOps Landscape in 2023 discusses a range of MLOps platforms, offering insights into their core features and how they cater to different aspects of machine learning workflows.

  • Math Application:

    • Understand the considerations for scalability and efficiency in the deployment and management of machine learning models.

    • Explore the mathematical principles that support various MLOps tools and platforms, enhancing the overall machine learning process.

Week 16: ETL Processes#

Lesson 76: Introduction to ETL and Data Extraction Techniques#

To understand the basics of Extract, Transform, Load (ETL) processes and data extraction:

  • Resource 1: ETL with Python and Jupyter Notebooks in the Cloud - This Microsoft Community Hub article provides a foundational understanding of ETL processes, demonstrated using Python in a Jupyter Notebook hosted on GitHub Codespaces.

  • Resource 2: Implementing ETL Process Using Python - Analytics Vidhya offers a practical guide to implementing ETL processes using Python, covering various data formats such as CSV, JSON, and XML.

  • Math Application:

    • Explore query optimization and data extraction methods.

    • Understand the mathematical and logical foundations of extracting data from various sources and transforming it into a structured format.

Lesson 77: Data Transformation Techniques#

For transforming data in ETL processes:

  • Resource 1: ETL Process | Implementing ETL Process Using Python - This resource offers a step-by-step guide on transforming data, including converting and rounding off data values using Python.

  • Math Application:

    • Learn about algorithmic approaches to data transformation.

    • Delve into mathematical concepts like data normalization and conversion, which are crucial in preparing data for analysis.

Lesson 78: Data Loading and Database Management#

To understand data loading and database management:

  • Resource 1: ETL with Python and Jupyter Notebooks in the Cloud - This article also touches on loading processed data into databases, demonstrating how to load data into JSON files and cloud-based databases like Azure Cosmos DB.

  • Math Application:

    • Explore load balancing and database theory.

    • Understand mathematical principles behind efficient data storage and retrieval in database systems.

Lesson 79: Building an ETL Pipeline#

For building an ETL pipeline:

  • Resource 1: ETL Process | Implementing ETL Process Using Python - Offers insights into creating a basic ETL pipeline, including extracting, transforming, and loading data, along with logging for each step.

  • Math Application:

    • Focus on workflow optimization and pipeline efficiency.

    • Study the mathematical models and algorithms used to streamline the ETL process, ensuring data integrity and performance.

Lesson 80: ETL Tools and Technologies#

To get an overview of ETL tools and technologies:

  • Resource 1: DataCamp’s ETL Courses - DataCamp offers a range of courses that cover ETL tools and technologies, providing hands-on learning experiences with tools like SQL, Power BI, and more.

  • Math Application:

    • Evaluate technology based on performance metrics.

    • Understand the mathematical foundations of various ETL tools and technologies, focusing on their efficiency, scalability, and effectiveness in handling large datasets.

Week 17: Transformers in Deep Learning#

Lesson 81: Understanding Transformers Architecture#

To delve into the architecture of transformer models:

  • Resource 1: UvA DL Notebooks’ Tutorial 6: Transformers and Multi-Head Attention provides an in-depth explanation of the Transformer model architecture, focusing on the self-attention mechanism and multi-head attention.

  • Resource 2: Harvard’s From Transformer to LLM: Architecture, Training and Usage offers a series of Jupyter notebooks covering the fundamentals and applications of transformers, including architecture and language modeling.

  • Math Application:

    • Explore matrix multiplication and scaling in self-attention mechanisms to understand how transformers process sequences.

    • Study the architectural differences and similarities between various transformer models.

Lesson 82: Self-Attention and Positional Encoding#

Exploring self-attention and positional encoding in transformers:

  • Resource 1: Analytics Vidhya’s Deep Dive Into Transformers Library provides practical examples of implementing transformer models for NLP tasks.

  • Resource 2: UvA DL Notebooks’ tutorial covers the fundamentals of self-attention and its implementation in transformer models.

  • Math Application:

    • Understand the mathematical theory behind self-attention and positional encoding in transformer models.

    • Analyze how these concepts contribute to the model’s ability to handle sequential data effectively.

Lesson 83: Implementing a Transformer Model#

Practical implementation of a transformer model:

  • Resource 1: Harvard’s Jupyter Notebook series on Understanding Attention & Transformer from Scratch offers a hands-on approach to implementing attention mechanisms and transformer models from scratch.

  • Resource 2: Machine Learning Mastery’s The Transformer Model tutorial provides insights into the network architecture of the Transformer model, including encoder and decoder structures.

  • Math Application:

    • Examine loss functions and optimization techniques used in transformer model training.

    • Explore the practical steps in coding and implementing these models for different machine learning tasks.

Lesson 84: Transformers in Natural Language Processing#

Applying transformers in NLP:

  • Resource 1: Hugging Face NLP Course, accessible through Hugging Face’s website, offers a comprehensive guide to using transformer models for NLP tasks.

  • Resource 2: Harvard’s tutorial series includes applications of transformer models in various fields, including NLP.

  • Math Application:

    • Focus on embedding space geometry and how transformers contextualize language data.

    • Understand the underlying mathematical concepts that enable transformers like BERT and GPT to excel in NLP tasks.

Lesson 85: Transformers in Other Domains#

Exploring the use of transformer models beyond NLP:

  • Resource 1: Harvard’s tutorial series on Beyond Language demonstrates the adaptability of transformer models in various domains such as vision and audio.

  • Resource 2: Analytics Vidhya’s deep dive also touches upon the broader applications of transformers.

  • Math Application:

    • Study how transformer architecture is adapted to different data types, such as images and audio signals.

    • Explore the mathematical modifications necessary to apply transformers in fields outside of NLP.

Week 18: Ethics in AI#

Lesson 86: AI Ethics, Bias, and Fairness#

To explore ethics in AI, particularly focusing on bias and fairness:

  • Resource 1: DataCamp offers an AI Ethics Course that provides a comprehensive overview of ethical considerations in AI, covering principles of AI ethics, strategies to foster fair AI systems, and methods to address key issues.

  • Resource 2: MIT OpenCourseWare presents a full video on the Ethics of AI Bias, suitable for understanding AI bias in depth, suitable for classroom use or individual study.

  • Math Application:

    • Investigate fairness metrics and quantitative measures of bias in AI.

    • Understand how these metrics are applied in AI systems to ensure fairness and minimize bias.

Lesson 87: Privacy and Security in AI Systems#

Understanding the significance of privacy and security in AI involves:

  • Resource 1: Pluralsight’s course AI Ethics: Understanding Bias and Fairness in Your Models covers identifying and measuring model bias and fairness, as well as understanding potential sources of bias and mitigation strategies.

  • Resource 2: KDnuggets’ article on Ethics, Fairness, and Bias in AI provides insights into the sources of bias in AI and methods for detecting machine learning model bias.

  • Math Application:

    • Delve into cryptography fundamentals and data protection techniques in the context of AI.

    • Analyze how these techniques are used to protect user privacy and ensure data security.

Lesson 88: Explainability and Transparency in AI#

To understand the need for explainability and transparency in AI models:

  • Resource 1: Kaggle’s AI Fairness tutorial explains how to assess and address fairness in AI models, an important aspect of AI explainability and transparency.

  • Resource 2: DataCamp’s AI Ethics Course also covers aspects of AI explainability in its curriculum.

  • Math Application:

    • Study techniques for model interpretability and explanation in AI.

    • Explore mathematical methods used to make AI decisions transparent and understandable.

Lesson 89: AI Regulations and Compliance#

Learning about AI regulations and compliance involves:

  • Resource 1: The AI Ethics Course by DataCamp discusses the principles of AI ethics which are closely related to AI regulations and compliance.

  • Resource 2: Pluralsight’s course on AI Ethics addresses aspects of AI that pertain to regulatory and compliance issues.

  • Math Application:

    • Explore compliance modeling and risk assessment in AI systems.

    • Understand the mathematical frameworks used in adhering to AI regulations such as GDPR.

Lesson 90: Ethical Decision Making in AI Projects#

For approaches to ethical decision-making in AI project management:

  • Resource 1: DataCamp’s AI Ethics Course is relevant for understanding the broader ethical considerations in AI projects.

  • Resource 2: The video and study guide on the Ethics of AI Bias by MIT OpenCourseWare provides insights into making ethical decisions in the context of AI.

  • Math Application:

    • Apply decision theory and ethical considerations in AI development.

    • Analyze how mathematical methods can guide ethical decision-making in AI.

These resources offer a blend of theoretical knowledge and practical application, enhancing understanding in the critical areas of AI ethics, privacy, security, explainability, regulations, and ethical decision-making.

Week 19: Applied Industry Sector Applications#

Lesson 91: AI in Healthcare#

To explore AI applications in healthcare diagnostics and treatment planning:

  • Resource 1: Coursera’s AI in Health Care: Applications, Benefits, and Examples provides an overview of the most common applications of AI in healthcare, focusing on health care analytics and decision-making processes.

  • Resource 2: Stanford’s AI in Healthcare Specialization offers a comprehensive look into how AI transforms industries, including healthcare, covering topics from patient visits to lab tests and procedures.

  • Math Application:

    • Dive into statistical methods used in healthcare analytics and predictive modeling.

    • Understand how AI models are applied to optimize health outcomes and decision-making in healthcare settings.

Lesson 92: AI in Finance#

Exploring AI in finance for fraud detection and risk management:

  • Resource 1: Harvard University’s Innovation with AI in Health Care course, while focused on healthcare, offers insights into AI’s transformative role in various sectors, including finance.

  • Resource 2: Harvard University’s AI for Health Care: Concepts and Applications course provides foundational knowledge of AI applications, which can be extrapolated to financial contexts.

  • Math Application:

    • Explore algorithms used in risk calculation and financial modeling.

    • Learn about AI’s role in analyzing financial data for fraud detection and managing financial risks.

Lesson 93: AI in Retail#

AI applications in retail for customer insights and supply chain management:

  • Resource 1: Coursera’s AI in Health Care article, though healthcare-focused, provides a foundation in AI that can be applied to customer data analysis in retail.

  • Resource 2: Stanford’s AI in Healthcare Specialization offers a broader understanding of AI’s capabilities in data analysis, relevant to retail applications.

  • Math Application:

    • Delve into predictive analysis techniques for customer behavior and demand forecasting in retail.

    • Understand the use of AI in optimizing supply chain management and personalizing customer experiences.

Lesson 94: AI in Manufacturing#

Use of AI in manufacturing for predictive maintenance and quality control:

  • Resource 1: Harvard’s Innovation with AI in Health Care course provides insights into AI’s potential for predictive analytics, applicable in a manufacturing context.

  • Resource 2: Harvard’s AI for Health Care: Concepts and Applications course, though healthcare-centric, covers AI principles that can be translated to manufacturing processes.

  • Math Application:

    • Study reliability theory and statistical quality control methods used in manufacturing.

    • Learn about AI’s role in predictive maintenance and optimizing manufacturing processes.

Lesson 95: AI in Other Sectors (Transportation, Education, etc.)#

Broad overview of AI applications in various sectors:

  • Resource 1: Coursera’s AI in Health Care article provides a foundational understanding of AI’s diverse applications, relevant to sectors like transportation and education.

  • Resource 2: Stanford’s AI in Healthcare Specialization covers AI’s broad capabilities, offering insights applicable across different industries.

  • Math Application:

    • Explore customizing AI models for sector-specific challenges and data types.

    • Understand the application of AI in various sectors, from optimizing transportation networks to enhancing educational tools.

Week 20: Applied Cybersecurity for AI#

Lesson 96: Introduction to Cybersecurity in AI#

Exploring cybersecurity in the context of AI systems:

  • Resource 1: AI and Cybersecurity: The Modern Approach - Offers insights into how AI is reshaping cybersecurity strategies.

  • Resource 2: Cybersecurity Fundamentals for AI on Coursera - A course providing foundational knowledge on cybersecurity in AI.

  • Math Application:

    • Study security protocols, encryption techniques, and algorithmic security measures tailored for AI systems.

    • Delve into the mathematical underpinnings of cryptographic methods used in AI.

Lesson 97: Identifying Threats and Vulnerabilities in AI Systems#

Understanding potential threats and vulnerabilities in AI:

  • Resource 1: Identifying Threats in AI Systems - A comprehensive guide on recognizing vulnerabilities in AI systems.

  • Resource 2: AI System Vulnerabilities - An article discussing various threats to AI systems.

  • Math Application:

    • Explore probability theory in assessing threats and risks in AI systems.

    • Analyze statistical models used for risk analysis in AI.

Lesson 98: AI in Cybersecurity - Detection and Prevention#

The role of AI in enhancing cybersecurity:

  • Resource 1: AI in Cybersecurity by IBM - Examines how AI is used for cybersecurity detection and prevention.

  • Resource 2: Enhancing Cybersecurity with AI on Forbes - Discusses AI’s role in modern cybersecurity practices.

  • Math Application:

    • Understand pattern recognition, anomaly detection, and predictive algorithms in cybersecurity.

    • Investigate the mathematical frameworks behind AI-driven cybersecurity tools.

Lesson 99: Implementing Cybersecurity in AI Projects#

Best practices for integrating cybersecurity in AI:

  • Resource 1: Cybersecurity for AI Projects - McKinsey’s insights on securing AI projects.

  • Resource 2: AI Project Cybersecurity on Built In - Covers strategies for implementing robust cybersecurity in AI projects.

  • Math Application:

    • Examine data integrity algorithms and secure data processing techniques.

    • Analyze mathematical approaches to ensuring data security in AI applications.

Lesson 100: Case Studies: Cybersecurity Incidents in AI#

Analyzing real-world cybersecurity incidents in AI:

  • Resource 1: AI Cybersecurity Case Studies - A collection of case studies highlighting cybersecurity incidents in AI.

  • Resource 2: Cybersecurity Incidents in AI on Infosecurity Magazine - Reports on various cybersecurity incidents involving AI.

  • Math Application:

    • Study forensic analysis techniques and mathematical methods used in post-incident evaluations.

    • Explore statistical and probabilistic models used in the analysis of cybersecurity incidents in AI systems.

Week 21: Capstone Project#

Lesson 101: Capstone Project Planning and Topic Selection#

Guidance on selecting and planning a capstone project in AI/ML:

  • Resource 1: Choosing a Capstone Project on Coursera - Offers strategies for selecting an impactful capstone project in AI/ML.

  • Resource 2: AI/ML Project Planning Guide on DataCamp - Provides a comprehensive guide for defining project scope and feasibility.

  • Math Application:

    • Explore project scope definition and feasibility analysis using mathematical methods.

    • Delve into quantitative techniques for assessing project viability and setting realistic goals.

Lessons 102-104: Capstone Project Development#

Hands-on development of a capstone project, applying concepts learned:

  • Resource 1: Capstone Project Development on edX - A series of resources guiding through the development of a capstone project.

  • Resource 2: Practical Guide to Capstone Project on Towards Data Science - Offers practical advice and ideas for developing capstone projects in data science.

  • Math Application:

    • Engage in applied mathematical modeling and problem-solving within the project context.

    • Utilize statistical methods and analytical techniques in project development and execution.

Lesson 105: Finalization and Presentation of Capstone Projects#

Finalizing projects and preparing for presentations:

  • Resource 1: Guide to Finalizing Capstone Projects on Indeed - Offers tips on finalizing and presenting capstone projects effectively.

  • Resource 2: Presentation Techniques for Capstone Projects on ScienceDirect - Discusses methods for effective data presentation and project summarization.

  • Math Application:

    • Focus on data interpretation, including statistical analysis and result synthesis.

    • Learn presentation techniques and ways to communicate mathematical findings clearly and effectively.