Daily Challenge Curriculum#

Module 1: Introduction to Python and Basic Mathematics#

Weeks 1-2#

  • Focus: Basic Python programming skills and foundational mathematics.

  • Topics include basic Python syntax, data types, control structures, linear algebra, calculus, and statistics.

Week 1: Python Basics and Numerical Operations#

  • Day 1: Python Basics - Syntax, Variables

    • Explore Python syntax and variable assignment.

    • Math Focus: Basic arithmetic operations in Python.

  • Day 2: Data Types and Operators

    • Understanding different data types in Python.

    • Math Focus: Using logical operators for basic calculations.

  • Day 3: Control Structures - Loops

    • Introduction to looping constructs in Python (for, while).

    • Math Focus: Looping through mathematical sequences.

  • Day 4: Control Structures - Conditional Statements

    • Using if, elif, and else statements for control flow.

    • Math Focus: Implementing mathematical conditions in Python.

  • Day 5: Functions and Modules

    • Defining and using functions; introduction to Python modules.

    • Math Focus: Writing functions for mathematical formulas.

Week 2: Introduction to Mathematical Concepts in Python#

  • Day 6: Linear Algebra - Introduction, Vectors

    • Basics of linear algebra; working with vectors in Python.

    • Math Application: Performing vector operations using Python.

  • Day 7: Linear Algebra - Matrices, Matrix Operations

    • Introduction to matrices and matrix operations in Python.

    • Math Application: Implementing matrix operations in Python.

  • Day 8: Calculus - Derivatives, Concept and Applications

    • Understanding the concept of derivatives in calculus.

    • Math Application: Implementing derivatives using Python.

  • Day 9: Calculus - Integrals, Fundamental Theorems

    • Basics of integrals and their application in calculus.

    • Math Application: Performing simple integrations in Python.

  • Day 10: Probability and Statistics - Basic Concepts, Relevant Distributions

    • Exploring basic concepts in probability and statistics.

    • Math Application: Performing basic statistical calculations and understanding distributions in Python.

Module 2: Data Preprocessing and Exploratory Data Analysis#

Weeks 3-4#

  • Focus: Techniques for preparing and exploring data.

  • Topics include data preprocessing methods, exploratory data analysis, visualization, and descriptive statistics.

Week 3: Data Preprocessing#

  • Day 11: Introduction to Data Preprocessing in Python

    • Explore the concepts and importance of data preprocessing.

    • Math Focus: Understanding data types, scales, and basic statistics in Python.

  • Day 12: Splitting Data into Training and Test Sets in Python

    • Techniques for splitting data into training and test sets.

    • Math Focus: Random sampling methods and stratified sampling principles.

  • Day 13: Handling Missing Data with Python

    • Techniques for detecting and handling missing data.

    • Math Focus: Imputation techniques and their mathematical rationale.

  • Day 14: Data Normalization and Scaling using Python

    • Learn about data normalization and feature scaling.

    • Math Focus: Z-score normalization, min-max scaling, and their mathematical foundations.

  • Day 15: Encoding Categorical Data in Python

    • Understand and implement categorical data encoding.

    • Math Focus: Binary and one-hot encoding, label encoding, and their mathematical implications.

Week 4: Exploratory Data Analysis (EDA)#

  • Day 16: Introduction to EDA and Data Visualization in Python

    • Basics of exploratory data analysis and data visualization techniques.

    • Math Focus: Descriptive statistics and graphical representation of data.

  • Day 17: Implementing Descriptive Statistics for EDA in Python

    • Practical implementation of descriptive statistics in Python.

    • Math Focus: Measures of central tendency and dispersion.

  • Day 18: Visualization Techniques for Data Distribution in Python

    • Create various types of plots to visualize data distributions.

    • Math Focus: Histograms, box plots, and understanding data distributions.

  • Day 19: Correlation Analysis using Python

    • Explore correlation analysis and its implementation.

    • Math Focus: Correlation coefficients and interpreting correlation in data.

  • Day 20: Feature Selection and Importance in Python

    • Techniques for feature selection and understanding feature importance.

    • Math Focus: Information gain, Gini impurity, and feature importance metrics.

Module 3: Supervised Learning - Regression and Classification#

Weeks 5-6#

  • Focus: Key concepts and algorithms in supervised learning.

  • Topics include regression, classification algorithms, decision trees, SVM, and ensemble methods.

Week 5: Supervised Learning - Regression#

  • Day 21: Introduction to Regression Analysis in Python

    • Basics of regression analysis and simple linear regression.

    • Math Focus: Linear equation fundamentals and fitting models to data.

  • Day 22: Implementing Multiple Linear Regression in Python

    • Understand and implement multiple linear regression.

    • Math Focus: Multivariate calculus and regression coefficients interpretation.

  • Day 23: Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression

    • Explore advanced regression techniques and their applications.

    • Math Focus: Polynomial functions, Lasso and Ridge regularization techniques.

  • Day 24: Regression Model Evaluation Metrics in Python

    • Key metrics for evaluating regression models.

    • Math Focus: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared.

  • Day 25: Addressing Overfitting and Underfitting in Regression Models

    • Strategies to combat overfitting and underfitting in regression.

    • Math Focus: Bias-variance tradeoff and regularization methods.

Week 6: Supervised Learning - Classification#

  • Day 26: Introduction to Classification and Logistic Regression in Python

    • Introduction to classification algorithms in machine learning.

    • Math Focus: Understanding the logistic function and its application in logistic regression.

  • Day 27: K-Nearest Neighbors (K-NN) Algorithm in Python

    • Learning and implementing the K-NN algorithm for classification.

    • Math Focus: Distance metrics (Euclidean, Manhattan) used in K-NN.

  • Day 28: Support Vector Machines (SVM) for Linear and Nonlinear Data

    • Implementing SVM for both linear and nonlinearly separable data.

    • Math Focus: Concept of hyperplanes, margin maximization, and kernel tricks.

  • Day 29: Decision Trees and Rule-Based Models in Python

    • Building and interpreting decision tree models.

    • Math Focus: Understanding entropy and information gain calculations.

  • Day 30: Naive Bayes Classifier Implementation

    • Implementing the Naive Bayes classification algorithm.

    • Math Focus: Basics of probability and Bayes’ theorem in the context of classification.

Module 4: Unsupervised Learning and Dimensionality Reduction#

Weeks 7-9#

  • Focus: Unsupervised learning techniques and reducing data complexity.

  • Topics include clustering, Gaussian Mixture Models, PCA, and t-SNE.

Week 7: Ensemble Methods#

  • Day 31: Introduction to Ensemble Learning Techniques

    • Overview of ensemble learning and its importance in machine learning.

    • Math Focus: Concept of model combination and weighted averaging.

  • Day 32: Bagging and Random Forests

    • Understanding and implementing bagging and random forest algorithms.

    • Math Focus: Bootstrap sampling and its role in bagging.

  • Day 33: Boosting Algorithms - AdaBoost

    • Learning the AdaBoost algorithm and its implementation.

    • Math Focus: Boosting algorithms and weight updates.

  • Day 34: Gradient Boosting Machines (GBM) and XGBoost

    • Implementing GBM and XGBoost algorithms.

    • Math Focus: Gradient descent in boosting and regularization techniques in XGBoost.

  • Day 35: Advanced Ensemble Techniques and Their Applications

    • Exploring advanced concepts in ensemble learning.

    • Math Focus: Stacking, blending, and their mathematical principles.

Week 8: Unsupervised Learning#

  • Day 36: Introduction to Unsupervised Learning and Clustering Basics

    • Basics of unsupervised learning and clustering.

    • Math Focus: Principles of cluster analysis and k-means algorithm.

  • Day 37: Implementing K-Means Clustering for Different Data Types

    • Practical implementation of k-means for various datasets.

    • Math Focus: Centroid calculation and convergence criteria in k-means.

  • Day 38: Hierarchical Clustering Techniques

    • Understanding and implementing hierarchical clustering.

    • Math Focus: Dendrogram interpretation and linkage methods.

  • Day 39: Density-Based Clustering with DBSCAN

    • Learning about and implementing the DBSCAN algorithm.

    • Math Focus: Core concepts of density-based clustering.

  • Day 40: Gaussian Mixture Models (GMM) and Expectation-Maximization

    • Introduction to GMM and its implementation.

    • Math Focus: Probability distributions and the expectation-maximization algorithm.

Week 9: Dimensionality Reduction#

  • Day 41: Introduction to Dimensionality Reduction and PCA Basics

    • Fundamentals of dimensionality reduction and principal component analysis (PCA).

    • Math Focus: Understanding covariance matrix, eigenvalues, and eigenvectors.

  • Day 42: Implementing PCA in Python

    • Practical application of PCA using Python.

    • Math Focus: Steps involved in PCA computation.

  • Day 43: Singular Value Decomposition (SVD) and Its Applications

    • Understanding and implementing SVD.

    • Math Focus: Concepts of matrix decomposition and its applications.

  • Day 44: t-SNE Technique for Dimensionality Reduction

    • Learning about t-SNE and its implementation for high-dimensional data.

    • Math Focus: t-Distributed Stochastic Neighbor Embedding algorithm.

  • Day 45: Other Techniques in Dimensionality Reduction

    • Exploring additional dimensionality reduction techniques like LDA, autoencoders.

    • Math Focus: Mathematical principles behind these techniques and their use-cases.

Module 5: Deep Learning Foundations#

Weeks 10-12#

  • Focus: Core concepts and architectures in deep learning.

  • Topics include neural networks, CNNs, RNNs, and applications in image and sequence processing.

Week 10: Introduction to Neural Networks#

  • Day 46: Understanding Neural Networks and Perceptrons

    • Basics of neural networks and the perceptron model.

    • Math Focus: Activation functions and their role in neural networks.

  • Day 47: Feedforward Neural Networks and Activation Functions

    • Implementing feedforward neural networks.

    • Math Focus: Understanding network layers, neuron connections, and activation functions.

  • Day 48: Backpropagation Algorithm in Neural Networks

    • Deep dive into the backpropagation algorithm.

    • Math Focus: The chain rule in calculus and its application in neural network training.

  • Day 49: Training Neural Networks - Loss Functions and Optimizers

    • Techniques for training neural networks, focusing on loss functions and optimizers.

    • Math Focus: Different types of loss functions and optimization algorithms.

  • Day 50: Evaluating and Tuning Neural Network Performance

    • Strategies for evaluating and improving the performance of neural networks.

    • Math Focus: Regularization methods, hyperparameter tuning, and avoiding overfitting.

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

  • Day 51: Introduction to CNNs and Convolutional Layers

    • Fundamentals of Convolutional Neural Networks (CNNs) and their architecture.

    • Math Focus: Convolution operations and feature map generation.

  • Day 52: Pooling Layers and CNN Architectures

    • Exploring pooling layers and various CNN architectures.

    • Math Focus: Spatial pooling concepts and their effects on feature maps.

  • Day 53: Implementing a Basic CNN for Image Classification

    • Hands-on implementation of a basic CNN for image classification tasks.

    • Math Focus: Understanding filter and feature map calculations in CNNs.

  • Day 54: Advanced CNN Techniques for Image Classification

    • Advanced techniques in CNNs for enhancing image classification models.

    • Math Focus: Dropout, batch normalization, and their mathematical basis.

  • Day 55: Transfer Learning with CNNs

    • Introduction to the concept of transfer learning in the context of CNNs.

    • Math Focus: Knowledge transfer, fine-tuning, and feature extraction in deep learning.

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

  • Day 56: Introduction to RNNs and Their Architecture

    • Basics of Recurrent Neural Networks (RNNs) and their unique architecture.

    • Math Focus: Understanding sequences and recurrence relations in RNNs.

  • Day 57: Implementing Long Short-Term Memory (LSTM) Networks

    • Learning about and implementing LSTM networks.

    • Math Focus: LSTM cell calculations and handling long-term dependencies.

  • Day 58: Building a Basic RNN/LSTM for Sequence Modeling

    • Hands-on experience in building a basic RNN/LSTM model.

    • Math Focus: Backpropagation through time and its challenges.

  • Day 59: RNNs for Time Series Analysis

    • Application of RNNs in time series analysis and forecasting.

    • Math Focus: Time series forecasting methods and sequence modeling.

  • Day 60: Utilizing RNNs in Natural Language Processing (NLP)

    • Exploring the use of RNNs in various NLP tasks.

    • Math Focus: Word embeddings, vector spaces, and sequence-to-sequence models.

Weeks 13-14#

  • Focus: Advanced topics and emerging trends in machine learning.

  • Topics include reinforcement learning, transfer learning, GANs, and attention mechanisms.

Week 13: Reinforcement Learning#

  • Day 61: Fundamentals of Reinforcement Learning

    • Introduction to the concepts and frameworks of reinforcement learning.

    • Math Focus: Understanding reward function optimization and decision-making processes.

  • Day 62: Markov Decision Processes in Reinforcement Learning

    • Deep dive into Markov Decision Processes (MDPs) and their role in RL.

    • Math Focus: Transition probability matrices and state-value functions.

  • Day 63: Basics of Q-Learning

    • Understanding and implementing the Q-learning algorithm.

    • Math Focus: The Bellman equation and its role in value estimation.

  • Day 64: Deep Q-Networks (DQN) and Their Applications

    • Exploring Deep Q-Networks and their use in complex environments.

    • Math Focus: Integrating neural networks with Q-learning (loss function in DQN).

  • Day 65: Policy Gradient Methods in Reinforcement Learning

    • Learning about policy gradient methods and their implementation.

    • Math Focus: Policy optimization techniques and gradient ascent.

Module 7: Practical Aspects of Machine Learning#

Weeks 15-17#

  • Focus: Operationalizing machine learning models and understanding transformers.

  • Topics include MLOps, ETL processes, and implementation of transformer models.

Week 15: MLOps#

  • Day 71: Introduction to MLOps and Machine Learning Lifecycle

    • Overview of MLOps and its role in the ML lifecycle.

    • Math Focus: Metrics for model evaluation and performance.

  • Day 72: Model Versioning and Experiment Tracking

    • Techniques for versioning models and tracking experiments in ML projects.

    • Math Focus: Statistical analysis of model performance.

  • Day 73: CI/CD in Machine Learning

    • Understanding Continuous Integration and Continuous Delivery (CI/CD) in the context of ML.

    • Math Focus: Automated testing and validation strategies.

  • Day 74: Model Monitoring and Maintenance

    • Strategies for monitoring and maintaining ML models in production.

    • Math Focus: Anomaly detection and performance drift in model behavior.

  • Day 75: Overview of MLOps Tools and Platforms

    • Introduction to various MLOps tools and platforms.

    • Math Focus: Considerations for scalability and efficiency.

Week 16: ETL Processes#

  • Day 76: Introduction to ETL and Data Extraction Techniques

    • Basics of Extract, Transform, Load (ETL) processes and data extraction.

    • Math Focus: Query optimization and data extraction methods.

  • Day 77: Data Transformation Techniques

    • Techniques for transforming data in ETL processes.

    • Math Focus: Algorithmic approaches to data transformation.

  • Day 78: Data Loading and Database Management

    • Understanding the data loading phase and database management.

    • Math Focus: Load balancing and database theory.

  • Day 79: Building an ETL Pipeline

    • Practical steps in building an ETL pipeline.

    • Math Focus: Workflow optimization and pipeline efficiency.

  • Day 80: ETL Tools and Technologies

    • Overview of tools and technologies used in ETL processes.

    • Math Focus: Evaluating technology based on performance metrics.

Week 17: Transformers in Deep Learning#

  • Day 81: Understanding Transformers Architecture

    • Deep dive into the architecture of transformer models.

    • Math Focus: Matrix multiplication and scaling in self-attention mechanisms.

  • Day 82: Self-Attention and Positional Encoding

    • Exploring the concepts of self-attention and positional encoding in transformers.

    • Math Focus: Mathematical theory behind encoding mechanisms.

  • Day 83: Implementing a Transformer Model

    • Practical implementation of a transformer model for various tasks.

    • Math Focus: Loss functions and optimization in transformer training.

  • Day 84: Transformers in Natural Language Processing

    • Application of transformers in NLP (e.g., BERT, GPT models).

    • Math Focus: Embedding space geometry and contextual representation.

  • Day 85: Transformers in Other Domains

    • Exploring the use of transformer models beyond NLP (e.g., Vision Transformers).

    • Math Focus: Adapting transformer architecture to different data types.

Module 8: Applied AI and Ethical Considerations#

Weeks 18-19#

  • Focus: Application of AI in various industries and ethical considerations.

  • Topics include AI applications in healthcare, finance, retail, manufacturing, and ethical issues

Week 18: Ethics in AI#

  • Day 86: AI Ethics, Bias, and Fairness

    • Introduction to ethics in AI, focusing on bias and fairness.

    • Math Focus: Fairness metrics and quantitative measures of bias.

  • Day 87: Privacy and Security in AI Systems

    • Understanding the importance of privacy and security in AI.

    • Math Focus: Cryptography fundamentals and data protection techniques.

  • Day 88: Explainability and Transparency in AI

    • The need for explainability and transparency in AI models.

    • Math Focus: Techniques for model interpretability and explanation.

  • Day 89: AI Regulations and Compliance

    • Overview of regulations and policies affecting AI (e.g., GDPR).

    • Math Focus: Compliance modeling and risk assessment in AI systems.

  • Day 90: Ethical Decision Making in AI Projects

    • Approaches to ethical decision-making in AI project management.

    • Math Focus: Decision theory and ethical considerations in AI development.

Week 19: Applied Industry Sector Applications#

  • Day 91: AI in Healthcare

    • Application of AI in healthcare diagnostics and treatment planning.

    • Math Focus: Statistical methods and predictive modeling in health data.

  • Day 92: AI in Finance

    • Exploring the use of AI in finance for fraud detection and risk management.

    • Math Focus: Algorithms for risk calculation and financial modeling.

  • Day 93: AI in Retail

    • AI applications in retail for customer insights and supply chain management.

    • Math Focus: Predictive analysis and demand forecasting in retail.

  • Day 94: AI in Manufacturing

    • Use of AI in manufacturing for predictive maintenance and quality control.

    • Math Focus: Reliability theory and statistical quality control methods.

  • Day 95: AI in Other Sectors (Transportation, Education, etc.)

    • Broad overview of AI applications in various sectors like transportation and education.

    • Math Focus: Customizing AI models for sector-specific challenges and data types.

Module 9: Capstone Project#

Weeks 20-21#

  • Focus: Application of learned concepts in a comprehensive project.

  • A practical project encompassing data analysis, model building, and evaluation.

Week 20: Applied Cybersecurity for AI#

  • Day 96: Introduction to Cybersecurity in AI

    • Basics of cybersecurity in the context of AI systems.

    • Math Focus: Security protocols, encryption, and algorithmic security measures.

  • Day 97: Identifying Threats and Vulnerabilities in AI Systems

    • Understanding potential threats and vulnerabilities specific to AI systems.

    • Math Focus: Probability theory in threat assessment and risk analysis.

  • Day 98: AI in Cybersecurity - Detection and Prevention

    • The role of AI in enhancing cybersecurity measures.

    • Math Focus: Pattern recognition, anomaly detection, and predictive algorithms.

  • Day 99: Implementing Cybersecurity in AI Projects

    • Best practices for integrating cybersecurity measures in AI development.

    • Math Focus: Data integrity algorithms and secure data processing.

  • Day 100: Case Studies: Cybersecurity Incidents in AI

    • Analysis of real-world cybersecurity incidents involving AI systems.

    • Math Focus: Forensic analysis techniques and post-incident evaluation.

Week 21: Capstone Project#

  • Day 101: Capstone Project Planning and Topic Selection

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

    • Math Focus: Project scope definition and feasibility analysis using mathematical methods.

  • Days 102-104: Capstone Project Development

    • Hands-on development of a capstone project, applying concepts learned throughout the course.

    • Math Focus: Applied mathematical modeling and problem-solving in project context.

  • Day 105: Finalization and Presentation of Capstone Projects

    • Finalizing projects and preparing for presentations.

    • Math Focus: Data interpretation, presentation techniques, and results analysis.