Overview of Machine Learning

• Definition, History, and Evolution
• Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
• Applications across Various Industries

Mathematical Foundations

• Linear Algebra:
• Vectors, Matrices, Matrix Operations
• Eigenvalues and Eigenvectors

Calculus:

• Derivatives, Gradients, Optimization Techniques

Probability Theory:

• Probability Distributions (Binomial, Normal, Poisson)
• Conditional Probability, Bayes' Theorem

Python Basics for Machine Learning

• Installation and Setup: Anaconda, Jupyter Notebooks
• Python Essentials:
  • Variables, Data Types, Operators
  • Control Flow: Loops, Conditional Statements
  • Functions, Classes, Modules
• NumPy and SciPy for Scientific Computing:
  • Arrays, Matrix Operations, Linear Algebra Functions

Data Collection and Cleaning

• Data Sources and Formats (CSV, JSON, SQL)
• Data Extraction Techniques
• Handling Missing Data:
  • Imputation Methods (Mean, Median, Mode)
  • Removing Outliers and Anomalies

Exploratory Data Analysis (EDA)

• Descriptive Statistics:
  • Measures of Central Tendency, Dispersion
  • Skewness, Kurtosis
• Data Visualization:
  • Matplotlib for Basic Plots
  • Seaborn for Statistical Visualizations
  • Plotly for Interactive Visualizations

Feature Engineering

• Transforming Variables:
  • Scaling, Normalization
  • Log Transformation, Box-Cox Transformation
• Handling Categorical Data:
  • One-Hot Encoding, Label Encoding
• Feature Selection Techniques:
  • Correlation Matrix, Univariate Selection
  • Recursive Feature Elimination (RFE), Principal Component Analysis (PCA)

Linear Models for Regression and Classification

• Linear Regression:
  • Simple Linear Regression, Multiple Linear Regression
  • Assumptions, Model Evaluation Metrics (R-squared, MAE, RMSE)
• Logistic Regression:
  • • Binary and Multiclass Classification
    • Decision Boundaries, Probability Estimation

Non-linear Models

• Decision Trees:
  • Tree Structure, Splitting Criteria (Gini Impurity, Information Gain)
  • Ensemble Methods: Random Forests, Gradient Boosting Machines (GBM)
• Support Vector Machines (SVM):
  • Linear SVM, Non-linear SVM (Kernel Tricks: Polynomial, RBF)
  • Margin, Support Vectors

Nearest Neighbors Methods

• k-Nearest Neighbors (k-NN) Algorithm:
  • Distance Metrics (Euclidean, Manhattan, Minkowski)
  • Model Evaluation and Tuning

Clustering Algorithms

• K-Means Clustering:
  • Algorithm Overview, Centroid Initialization
  • Evaluating Cluster Quality (Inertia, Silhouette Score)
• Hierarchical Clustering:
  • Agglomerative and Divisive Approaches
  • Dendrogram Visualization, Cluster Heatmaps

Dimensionality Reduction Techniques

• Principal Component Analysis (PCA):
  • Dimensionality Reduction, Eigenvalues, Eigenvectors
  • PCA for Feature Extraction, Visualization
• Singular Value Decomposition (SVD):
  • Matrix Factorization, Applications in Image Compression

Association Rule Learning

• Apriori Algorithm:
  • Market Basket Analysis, Rule Generation
  • Support, Confidence, and Lift Metrics

Model Performance Metrics

• Classification Metrics:
  • Accuracy, Precision, Recall, F1-score
  • ROC Curve, AUC (Area Under Curve)
• Regression Metrics:
  • Mean Absolute Error (MAE), Mean Squared Error (MSE), R-squared

Cross-Validation Techniques

• k-fold Cross-Validation, Stratified Cross-Validation
• Time Series Cross-Validation, Leave-One-Out Cross-Validation
• Hyperparameter Tuning:
  • Grid Search, Random Search
  • Bayesian Optimization

Neural Networks Basics

• Artificial Neural Networks (ANNs):
  • Perceptron, Activation Functions (ReLU, Sigmoid, Tanh)
  • Backpropagation Algorithm
• Deep Learning Libraries:
  • TensorFlow and Keras:
    • Building Sequential and Functional Models
    • Convolutional Neural Networks (CNNs) for Image Recognition
• PyTorch:
  • Tensor Basics, Autograd Mechanism
  • Recurrent Neural Networks (RNNs) for Sequence Modeling

Natural Language Processing (NLP)

• Text Preprocessing Techniques:
  • Tokenization, Stopword Removal, Stemming, Lemmatization
• Sentiment Analysis, Named Entity Recognition (NER)
• Word Embeddings:
  • Word2Vec, GloVe, Embedding Layers in Neural Networks

Reinforcement Learning

• Markov Decision Processes (MDPs), Q-Learning
• Deep Q-Networks (DQNs) for Game Playing and Control Tasks

Big Data and Machine Learning

• Introduction to Distributed Computing:
  • Apache Hadoop and HDFS
  • Apache Spark: RDDs, DataFrames, Spark SQL

Ethics in Data Science

• Data Privacy and Security:
  • GDPR, HIPAA Compliance
• Bias and Fairness in Machine Learning Models:
  • Debiasing Techniques, Fairness Metrics
• Transparency and Interpretability of Models:
  • Explainable AI (XAI) Techniques

Real-world Machine Learning Project

• Problem Formulation and Data Acquisition
• Data Exploration, Preprocessing, and Feature Engineering
• Model Selection, Training, and Evaluation
• Model Deployment, Presentation, and Documentation

Ethics in Data Science and Machine Learning

• Data Privacy and Security:
  • GDPR, HIPAA Compliance
• Bias and Fairness in Machine Learning Models:
  • Debiasing Techniques, Fairness Metrics
• Transparency and Interpretability of Models:
  • Explainable AI (XAI) Techniques

Real-world Machine Learning Project

• Problem Formulation and Dataset Selection
• Data Exploration, Preprocessing, and Feature Engineering
• Model Selection, Training, and Evaluation
• Model Deployment, Presentation, and Documentation

Applications of Machine Learning in Various Industries

• Healthcare, Finance, E-commerce, IoT, etc.
• Case Studies:
  • Predictive Maintenance in Manufacturing
  • Fraud Detection in Banking
  • Personalized Recommendations in E-commerce

Job Roles in Machine Learning

• Data Scientist, Machine Learning Engineer, AI Researcher, Data Analyst
  • Building a Machine Learning Portfolio
• Showcasing Projects, GitHub Repositories
  • Networking and Job Search Strategies
• Interview Preparation
  • Technical and Behavioral Interview Questions
  • Mock Interviews and Feedback Sessions