gjkaur/machine_learning_roadmap_from_novice_to_pro

Machine Learning Roadmap: From Novice to Pro

Machine Learning Roadmap: From Novice to Pro 🚀

Welcome to the Machine Learning Roadmap repository! Here, you'll find a curated collection of machine learning content and projects designed to take you from a novice to a pro in the field of machine learning. Each topic provides practical hands-on experience, helping you gain mastery in machine learning concepts and techniques.

Part 1

• What is Regression? 📈: An introduction to regression analysis and its significance in data analysis.
• Types of Regression 🔄: A brief overview of different types of regression techniques and when to use them.
• What is Mean, Variance, and Standard Deviation? 📉: Essential statistical measures that play a crucial role in regression analysis.
• Correlation and Causation 🤝: Understanding the difference between correlation and causation in data analysis.
• What are Observational and Experimental Data? 📊: Exploring the distinctions between observational and experimental data collection methods.
• Formula for Regression 📝: An introduction to the basic formula used for linear regression.
• Building a Simple Linear Regression Model 🧮: Step-by-step guidance on constructing a simple linear regression model.
• Understanding Interpolation and Extrapolation 📈: Learning how to use regression for interpolation and extrapolation of data.
• What are Lurking Variables? 🕵️‍♂️: An examination of lurking variables and their impact on regression analysis.
• Derivation for Least Square Estimates 📐: A mathematical derivation of the least square estimate used in linear regression.
• The Gauss Markov Theorem 📚: An explanation of the Gauss-Markov theorem and its significance in regression analysis.
• Point Estimators of Regression 🎯: An overview of point estimators for regression coefficients.
• Sampling Distributions of Regression Coefficients 📈: Understanding the distribution of regression coefficients.
• F-Statistics 📊: An introduction to F-statistics and its use in regression analysis.
• ANOVA Partitioning 📈: Exploring the analysis of variance (ANOVA) partitioning in regression.
• Coefficient of Determination (R-Squared) 📈: Understanding R-squared as a measure of goodness of fit in regression models.
• Diagnostic and Remedial Measures 🧰: Learning about diagnostic tools and remedies for common regression issues.

Part 2

• What is Multiple Linear Regression?: An introduction to multiple linear regression and its significance in predictive modeling.
• General Linear Regression Model 📊: Understanding the general framework of linear regression models.
• Matrix Representation for General Linear Regression Model 🧮: Representing linear regression models using matrices and vectors.
• Matrix Representation of Least Squares 📉: How to express the least squares method using matrix notation.
• Understanding Types of Predictive Variables 📈: Exploring different types of predictive variables in the context of multiple linear regression.
• F-Test 📊: Introduction to the F-test and its use in model evaluation and comparison.
• Coefficient of Multiple Determination 🎯: Understanding the coefficient of multiple determination (R-squared) as a measure of model fit.
• Adjusted R-Squared 📈: An exploration of adjusted R-squared, a modification of R-squared for multiple regression models.
• What are Scatterplots? 🌐: Using scatterplots for visualizing relationships between variables.
• What is a Correlation Matrix? 📊: Introduction to correlation matrices and their importance in understanding variable relationships.
• Understanding Multicollinearity 🧐: Identifying and addressing multicollinearity issues in multiple linear regression.
• ANOVA Partitioning 📈: Exploring analysis of variance (ANOVA) partitioning in the context of multiple regression.
• Diagnostic and Remedial Measures 🛠️: Strategies and tools for diagnosing and addressing common issues in regression models.
• What are Indicator Variables? 🚥: An overview of indicator variables and their role in regression modeling.
• Various Criteria for Model Selection 📊: Discussing different criteria for selecting the best regression model, including R-squared, Mallow's Cp, AIC, BIC, and PRESS.
• Building a Multiple Linear Regression Model 🏗️: Step-by-step guidance on constructing a multiple linear regression model, from data preparation to evaluation.

Part 3

• What is Regression? 📈: Understanding the fundamentals of regression and its importance in data analysis.
• Applications of Regression 🚀: Exploring real-world applications where regression models are widely used.
• Different Types of Regression 🔄: An overview of various regression techniques and their specific use cases.
• Regression vs. Classification 📊📈: Understanding the key differences between regression and classification problems.
• Linear Regression Explained 📈: A deep dive into linear regression, one of the foundational regression techniques.
• Loss Function in Regression 📉: Exploring loss functions used for training regression models.
• Gradient Descent Demystified 🚀: Understanding the gradient descent optimization algorithm and its role in regression.
• Drawbacks of Linear Regression 🤔: Identifying limitations and drawbacks of linear regression models.
• Bias and Variance in Modeling 🎯: Delving into the concepts of bias and variance in the context of model performance.
• Ridge and Lasso Regression 🏞️: Exploring regularization techniques like ridge and lasso regression.
• Introduction to Decision Trees 🌲: Understanding decision trees and their role in predictive modeling.
• Decision Tree Terminology 🌳: Familiarizing yourself with important terms and concepts related to decision trees.
• Advantages and Disadvantages of Decision Trees ✅❌: Weighing the pros and cons of using decision trees in your models.
• Importing Data and Libraries 📊: Learn how to import datasets and the necessary Python libraries for regression analysis.
• Handling Missing Data 🛠️: Strategies and techniques for handling missing data within your dataset.
• Exploring Feature Correlation 📊: Analyzing the relationships between different features using correlation.
• Building Regression Models from Scratch 🏗️: Step-by-step guidance on constructing regression models using the NumPy module.
• Model Evaluation with Metrics 📏📈: Gaining confidence in your models by assessing performance with metrics like Mean Squared Error (MSE) and R-squared.

Part 4

• What is a Distribution Plot? 📈: Understanding distribution plots and their significance in data analysis.
• What is a Boxplot? 📦: Exploring boxplots and their role in visualizing data distribution and outliers.
• What is a Violin Plot? 🎻: An overview of violin plots as a visualization tool for data distribution.
• How to Detect Outliers? 🔍: Strategies and techniques for identifying outliers in your dataset.
• How to Treat Outliers? 🛠️: Methods for handling outliers and their impact on your analysis.
• What is Pandas Imputer? 🐼: Introduction to pandas imputer for handling missing data in your dataset.
• What is Iterative Imputer? 🔄: Understanding iterative imputation as an advanced method for filling missing data.
• What is a KNN Imputer? 🤝: Exploring K-nearest neighbors imputation for missing data.
• What is an LGBM Imputer? 🌳: Introduction to LightGBM imputation for missing data.
• Univariate Analysis 📈: Analyzing individual variables to understand their distributions and characteristics.
• Chatterjee Correlation 📊: Exploring Chatterjee's correlation as an alternative to traditional correlation measures.
• What is ANOVA? 📊: Understanding analysis of variance (ANOVA) and its role in statistical analysis.
• Implementation of ANOVA 📈: Step-by-step guidance on implementing ANOVA for your datasets.
• Data Preprocessing 🛠️: Techniques for preprocessing your data before applying regression models.
• What is AIC? 📏: Introduction to the Akaike Information Criterion (AIC) for model selection.
• What is Likelihood? 📈: Understanding likelihood as a fundamental concept in statistics and modeling.

Part 5

• Understanding the Basics of Classification 📚:Introduction to classification and its importance in machine learning.
• Introduction to Logistic Regression 📈: An overview of logistic regression as a classification algorithm.
• Understanding the Logit Function 📊: Explanation of the logit function, which is central to logistic regression.
• Coefficients in Logistic Regression 🔍: How logistic regression calculates coefficients for predictive modeling.
• Concept of Maximum Log-Likelihood 🎯: Understanding the concept of maximum likelihood estimation in logistic regression.
• Performance Metrics 📊: Explore various performance metrics like confusion matrix, recall, accuracy, precision, f1-score, AUC, and ROC curve.
• Importing the Dataset and Required Libraries 📦: Learn how to import datasets and the necessary Python libraries for classification analysis.
• Basic Exploratory Data Analysis (EDA) 📊: Perform basic exploratory data analysis using Python libraries like matplotlib and seaborn for data interpretation and visualization.
• Data Inspection and Cleaning 🧹: Strategies and techniques for inspecting and cleaning your dataset to prepare it for modeling.
• Building the Model 🏗️: Use Python libraries such as statsmodels and scikit-learn to build logistic regression models.
• Dataset Splitting 🧩: Split your dataset into training and testing sets using scikit-learn.
• Model Training and Prediction 🚀: Train your model using classification techniques like logistic regression and make predictions.
• Model Evaluation 📏: Gain confidence in your model's performance by assessing its accuracy, confusion matrix, recall, precision, and f1-score.
• Handling Unbalanced Data ⚖️: Explore methods for dealing with unbalanced datasets, a common issue in classification.
• Feature Selection 📈: Perform feature selection using multiple methods to improve model efficiency and interpretability.
• Saving the Best Model 📦: Save your trained model in a pickle format for future use and deployment.

Part 6

• Introduction to Decision Trees 🌳: Let's kick things off by understanding the fundamentals of decision trees in data science.
• Measures of Impurity 📊: Delve into the metrics that help us measure impurity and make crucial decisions in tree building.
• Working of Decision Trees 💡: Get under the hood and explore how decision trees make predictions and classifications.
• Classification and Regression Trees (CART) 🧮: Learn about the versatile CART algorithm that handles both classification and regression tasks.
• C5.0 and CHAID Algorithms 🤖: Discover two more decision tree algorithms, C5.0 and CHAID, and their unique characteristics.
• Comparing Decision Tree Types 🌟: Compare different types of decision trees concerning measures of impurity and suitability.
• Visualizations with Python 📊🐍: Utilize Python libraries, particularly Matplotlib, to create captivating data visualizations.
• Data Prep & Cleaning 🧹🔍: Ensure your dataset is pristine through thorough inspection and cleaning.
• Building the Decision Tree Model 🛠️: Learn to construct decision tree models using the versatile sklearn library.
• Data Splitting 📊🎯: Split your dataset into training and testing subsets using sklearn.
• Making Predictions 🎯💡: Train your decision tree model and harness it for making data-driven predictions.
• Model Confidence 🎉: Evaluate your model's performance using essential metrics like accuracy scores, confusion matrices, recall, precision, and F1 scores.
• Handling Unbalanced Data ⚖️: Tackle unbalanced datasets with the SMOTE method, ensuring reliable model training.
• Feature Importance 🌐: Explore the concept of feature importance, identifying key factors influencing your decisions.

Part 7

• What is Classification? 🎯: Classification is a fundamental machine learning task where the goal is to categorize data into predefined classes or labels. It's used for various applications, including spam detection, image recognition, and medical diagnosis.
• Types of Classification 📊: Explore different types of classification algorithms, such as binary classification, multi-class classification, and multi-label classification. Each type serves specific use cases and challenges.
• Understanding the Business Context and Objective 🏢: Before diving into classification, it's crucial to understand the business context and objectives. Aligning machine learning goals with business goals ensures meaningful results.
• Data Cleaning 🧹: Clean and preprocess your data to ensure it's suitable for classification. Address issues like missing values, outliers, and inconsistent formatting.
• What is Data Imbalance? ⚖️: Learn about data imbalance, a common issue where some classes have significantly fewer samples than others. Imbalanced datasets can lead to biased models.
• How to Deal with Imbalanced Data? 🔄: Explore techniques to handle imbalanced data, including resampling methods like oversampling and undersampling, and algorithm-level approaches.
• Feature Encoding 🧾: Understand how to encode categorical features into numerical formats that machine learning algorithms can process effectively.
• Importance of Splitting Data 📂: Splitting your dataset into training and testing sets is essential for model evaluation. Learn why it's crucial and how to do it correctly.
• K Nearest Neighbours (KNN) Algorithm 🤝: Discover the K Nearest Neighbours algorithm, a simple yet powerful classification technique based on similarity among data points.
• Naive Bayes Algorithm 📈: Explore the Naive Bayes algorithm, a probabilistic method commonly used for text classification and spam filtering.
• Logistic Regression 📊: Dive into Logistic Regression, a linear classification algorithm used to model the probability of an instance belonging to a particular class.
• Decision Tree Classifier 🌲: Learn about Decision Tree classifiers, which use tree-like structures to make decisions based on feature values.
• Confusion Matrix 📉: Understand the confusion matrix, a valuable tool for evaluating classification model performance and assessing true positives, true negatives, false positives, and false negatives.
• Accuracy Measurement 🎯: Measure the overall accuracy of your classification model, which is the ratio of correctly predicted instances to total instances.
• Precision, Recall, F1 Score 📈: Explore precision, recall, and F1 score as important metrics for assessing the quality of your classification model, especially when dealing with imbalanced data.
• Feature Importance 📌: Determine feature importance to understand which features have the most significant impact on your classification model's predictions.
• Model Predictions 🧙‍♂️: Make predictions using your trained classification model on new data. Understand how to interpret model predictions effectively.
• Model Evaluation 🧐: Evaluate the performance of your classification model using various metrics and techniques, ensuring it meets the desired criteria.

Part 8

• What is Ensembling? 🧙‍♂️: Understanding the concept of ensemble learning and its importance in machine learning.
• What is Bagging? 🎒: A deep dive into bagging (Bootstrap Aggregating) as a popular ensemble technique.
• Understanding Random Forest model 🌲: Getting to know the Random Forest algorithm, a powerful ensemble method.
• Building Random Forest model 🌲: Step-by-step guidance on constructing a Random Forest model.
• What are problems with bagging and how to overcome them? 🤔: Identifying common issues with bagging and strategies for overcoming them.
• What is Boosting? 🚀: An introduction to boosting as another ensemble technique.
• Fundamentals of AdaBoost 🚀: Understanding the AdaBoost (Adaptive Boosting) algorithm and its principles.
• Building AdaBoost model 🚀: A detailed walkthrough of creating an AdaBoost model.
• XGBoost algorithm 🚀: Exploring the XGBoost algorithm, a widely used gradient boosting framework.
• Building XGBoost model 🚀: Step-by-step instructions for building an XGBoost model.
• Understanding XGBoost hyperparameter Gamma 🚀: Delving into the Gamma hyperparameter in XGBoost and its significance.
• Understanding XGBoost hyperparameter Lambda 🚀: Explaining the Lambda hyperparameter in XGBoost and its role.
• What is hyperparameter tuning? 🛠️: Introduction to the concept of hyperparameter tuning for optimizing models.
• GridSearch optimization 🛠️: Using GridSearchCV for hyperparameter tuning.
• RandomSearch optimization 🛠️: Employing RandomizedSearchCV for hyperparameter optimization.
• Bayesian optimization 🛠️: Leveraging Bayesian optimization for hyperparameter tuning.
• Hyperparameter tuning for RandomForest model 🛠️: Fine-tuning hyperparameters specifically for Random Forest models.
• Hyperparameter tuning for XGBoost model using hyperopt 🛠️: A guide on tuning hyperparameters for XGBoost models using hyperopt.
• Feature importance 🎯: Understanding how to assess feature importance in machine learning models.

I hope this roadmap helps you on your journey to becoming a machine learning pro! 🌟