Python for Machine Learning Machine Learning with R Machine Learning Algorithms Math for Machine Learning Machine Learning Interview Questions Computer vision Artificial Intelligence Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. In simple words, ML teaches systems to think and understand like humans by learning from the data. Module 1: ML Fundamentals This section introduces the core concepts of machine learning and explains how ML relates to AI and Deep Learning. AI vs ML vs Deep Learning Module 2: Python for ML Python is widely used in machine learning because of its simple syntax and powerful libraries. Conditional Statements Libraries: NumPy , Pandas , Matplotlib , Seaborn , Scikit-learn Module 3: Mathematics for ML A basic understanding of mathematics helps explain how machine learning algorithms learn from data and optimize their performance. Basic probability Conditional Probability Probability distributions Descriptive Statistics Inferential Statistics Skewness and Kurtosis Correlation: Pearson , Spearman Linear Algebra & Calculus linear equations Differentiation Gradient Descent Module 4: Data Preparation Real-world data is often incomplete or inconsistent and needs to be prepared before modeling. This section covers data cleaning, splitting, scaling and feature preparation techniques. Feature Selection Feature Scaling Feature Engineering Feature Extraction Imbalanced Data Module 5: Exploratory Data Analysis EDA helps understand data by finding patterns, relationships and unusual observations. This section covers analysis and visualization techniques for exploring datasets. Univariate, Bivariate and Multivariate Data Visualization Correlation Analysis Module 6: Supervised Learning Supervised Learning uses labeled data to make predictions on new data. This section covers common algorithms for classification and regression tasks. 1. Linear Regression Introduction to Linear Regression Multiple Linear Regression 2. Logistic Regression Understanding Logistic Regression Cost function in Logistic Regression 3. Decision Trees Decision Tree in Machine Learning Types of Decision tree algorithms Decision Tree Regression (Implementation) Decision Tree Classification (Implementation) 4. k-Nearest Neighbors (k-NN) Introduction to KNN Decision Boundaries in K-Nearest Neighbors (KNN) Introduction to Naive Bayes Gaussian Naive Bayes Multinomial Naive Bayes Bernoulli Naive Bayes 6. Support Vector Machines (SVM) Understanding SVMs 7. Ensemble Learning EnsembleI learning Bagging & Boosting Gradient Boosting 8. Random Forest (Bagging Algorithm) Random Forest Classifier Random Forest Regression Module 7: Model Evaluation & Optimization Evaluating a model helps determine how well it performs and whether it can generalize to new data. This section covers techniques for assessing and improving model performance. Confusion Matrix Precision, Recall & F1-Score Regression Metrics Cross-validation Overfitting & Underfitting Bias-Variance Tradeoff Hyperparameter Tuning Model Selection Module 8: Unsupervised learning Unsupervised Learning helps uncover useful patterns and relationships in unlabeled data. This section introduces the main techniques used to analyze and organize such data. Association Rule Mining Dimensionality Reduction Centroid-based Methods: K-Means clustering K-Means++ clustering

Connectivity based methods: Hierarchical clustering Agglomerative Clustering Density Based methods: Distribution-based Methods : Gaussian mixture models Expectation-Maximization Algorithm 2. Dimensionality Reduction Principal Component Analysis (PCA) t-distributed Stochastic Neighbor Embedding (t-SNE) Non-negative Matrix Factorization (NMF) Independent Component Analysis (ICA) 3. Anomaly Detection & Association Rule Mining Apriori algorithm FP-Growth (Frequent Pattern-Growth) Anomaly Detection Module 9: Reinforcement Learning Reinforcement Learning is a type of machine learning in which an agent interacts with an environment and learns to make decisions by receiving rewards or penalties for its actions. 1. Fundamental Methods Markov decision processes (MDPs) Bellman equation Value iteration algorithm 2. Model-Free Methods 3. Policy-Based Methods Reinforce Algorithm Actor-Critic Algorithm Asynchronous Advantage Actor-Critic (A3C) Module 10: Semi-Supervised & Self-Supervised Learning It uses a mix of labeled and unlabeled data making it helpful when labeling data is costly or it is very limited. Semi-Supervised Learning Semi Supervised Classification Self-Supervised Learning Module 11: Time Series & Forecasting Forecasting models analyze past data to predict future trends, commonly used for time series problems like sales, demand or stock prices. Exponential Smoothing Module 12: Deployment & MLOps The trained ML model must be integrated into an application or service to make its predictions accessible. Machine learning deployment ML Applications: Streamlit , Gradio ML APIs: Flask , FastAPI End-to-End MLOps Module 13: Projects & Practice Projects help reinforce machine learning concepts by applying them to practical problems. This section provides projects and interview resources for hands-on practice. Machine Learning Projects Interview Questions and Answers Coding Interview Questions After machine learning and have hands on experience in it we can start with deep learning from here: Deep Learning Tutorial Introduction 3 min read Types 7 min read ML Pipeline 6 min read Applications 2 min read ML with Python 3 min read Numpy 3 min read Pandas 4 min read Data Preprocessing 4 min read Feature Engineering 4 min read Dimensionality Reduction 3 min read Feature Selection 4 min read Supervised Learning 4 min read Linear Regression 10 min read Logistic Regression 9 min read Decision Tree 8 min read Random Forest 4 min read Naive Bayes 6 min read Unsupervised Learning 5 min read K means Clustering 7 min read Hierarchical Clustering 6 min read DBSCAN Clustering 6 min read Apriori Algorithm 5 min read FP Growth Algorithm 4 min read ECLAT Algorithm 5 min read Evaluation Metrics 9 min read Regularization 5 min read Cross Validation 5 min read Hyperparameter Tuning 5 min read Underfitting and Overfitting 3 min read Bias and Variance 6 min read Reinforcement Learning 8 min read Semi-Supervised Learning 5 min read Self-Supervised Learning 5 min read Ensemble Learning 6 min read Interview Questions 15+ min read ML Projects 5 min read Data Science 360 Course 2 min read AI Engg Course 2 min read