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Machine Learning Training in Bangalore Syllabus - prepared by 4Achievers Trainers

Introduction to Machine Learning

Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees
- Support Vector Machines
- Naive Bayes Classifier
Unsupervised Learning
- Clustering
- Dimensionality Reduction
- Association Rule Learning
- Anomaly Detection
- Density Estimation
Semi-supervised Learning
- Definition of Semi-Supervised Learning
- Types of Semi-Supervised Learning
- Applications of Semi-Supervised Learning
- Challenges in Semi-Supervised Learning
- Evaluation of Semi-Supervised Learning
Reinforcement Learning
- Markov Decision Processes
- Temporal Difference Learning
- Q-Learning
- Exploration and Exploitation
- Policy Gradient Methods
Model Evaluation and Hyperparameter Tuning
- Cross Validation
- Regularization
- Grid Search
- Hyperparameter Optimization
- Model Selection
Deep Learning
- Artificial Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Autoencoders
- Reinforcement Learning
Feature Engineering
- Data Preprocessing
- Feature Extraction
- Feature Selection
- Feature Transformation
- Feature Creation

Supervised Learning

Regression
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Logistic Regression
- Regularization Techniques
Classification
- Decision Trees
- Naive Bayes
- Support Vector Machines
- Logistic Regression
- K-Nearest Neighbors
Support Vector Machines
- Introduction to SVM
- Linear SVM
- Kernel SVM
- SVM Optimization
- SVM Applications
Decision Trees
- Introduction to Decision Trees
- Entropy and Information Gain
- Building Decision Trees
- Pruning Decision Trees
- Applications of Decision Trees
Naive Bayes Classifiers
- Probabilistic Model of Naive Bayes
- Bayes Theorem and Naive Bayes
- Gaussian Naive Bayes
- Multinomial Naive Bayes
- Bernoulli Naive Bayes
Ensemble Methods
- Bagging
- Boosting
- Random Forest
- AdaBoost
- Gradient Boosting
Model Evaluation and Selection
- Cross Validation
- Bias-Variance Tradeoff
- Regularization
- Model Selection
- Hyperparameter Tuning
Feature Engineering
- Feature Selection
- Feature Extraction
- Feature Transformation
- Feature Scaling
- Feature Encoding

Unsupervised Learning

Clustering
- K-Means Clustering
- Hierarchical Clustering
- Fuzzy C-Means Clustering
- Density-Based Clustering
- Model-Based Clustering
K-Means Clustering
- Introduction to K-Means Clustering
- K-Means Algorithm
- Distance Measures
- Cluster Validation
- Applications of K-Means Clustering
Hierarchical Clustering
- Introduction to Hierarchical Clustering
- Agglomerative Clustering
- Divisive Clustering
- Linkage Criteria
- Applications of Hierarchical Clustering
Density-based Clustering
- K-Means Clustering
- DBSCAN Algorithm
- Mean-Shift Clustering
- Hierarchical Clustering
- OPTICS Algorithm
Mixture Models
- Gaussian Mixture Models
- Expectation-Maximization Algorithm
- Mixture Density Networks
- Dirichlet Process Mixture Models
- Bayesian Mixture Models
Dimensionality Reduction
- Principal Component Analysis
- Singular Value Decomposition
- Factor Analysis
- Independent Component Analysis
- Non-Negative Matrix Factorization
Principal Component Analysis
- Introduction to PCA
- Eigen Decomposition
- Singular Value Decomposition
- Dimensionality Reduction
- Reconstruction of Data
Factor Analysis
- Introduction to Factor Analysis
- Factor Extraction
- Factor Rotation
- Factor Scores
- Factor Interpretation
Multidimensional Scaling
- Introduction to Multidimensional Scaling
- Types of Multidimensional Scaling
- Metrics for Multidimensional Scaling
- Applications of Multidimensional Scaling
- Challenges of Multidimensional Scaling
Autoencoders
- Introduction to Autoencoders
- Types of Autoencoders
- Autoencoder Architectures
- Training Autoencoders
- Applications of Autoencoders

Feature Engineering

Data Preprocessing
- Data Cleaning
- Data Transformation
- Data Imputation
- Data Discretization
- Data Normalization
Feature Selection
- Feature Selection Techniques
- Filter Methods
- Wrapper Methods
- Embedded Methods
- Feature Importance
Feature Extraction
- Feature Selection
- Dimensionality Reduction
- Feature Transformation
- Feature Extraction Algorithms
- Feature Extraction Techniques
Feature Transformation
- Feature Scaling
- Feature Selection
- Dimensionality Reduction
- Feature Extraction
- Feature Creation
Feature Creation
- Feature Transformation
- Feature Selection
- Feature Extraction
- Feature Construction
- Feature Combination
Feature Scaling
- Standardization
- Normalization
- Min-Max Scaling
- Robust Scaling
- Unit Vector Scaling
Feature Interaction
- Feature Selection
- Feature Extraction
- Feature Transformation
- Feature Scaling
- Feature Interaction Analysis
Feature Encoding
- One-Hot Encoding
- Label Encoding
- Count Encoding
- Binary Encoding
- Target Encoding
Feature Aggregation
- Feature Scaling
- Feature Selection
- Feature Extraction
- Feature Transformation
- Feature Interaction

Model Evaluation & Selection

Bias-Variance Tradeoff
- Definition of Bias-Variance Tradeoff
- Bias-Variance Decomposition
- Regularization and Bias-Variance Tradeoff
- Bias-Variance Tradeoff in Model Selection
- Bias-Variance Tradeoff in Hyperparameter Tuning
Cross-Validation
- K-Fold Cross Validation
- Leave-One-Out Cross Validation
- Repeated Random Sub-Sampling
- Stratified K-Fold Cross Validation
- Time Series Cross Validation
Regularization
- L1 Regularization
- L2 Regularization
- Elastic Net Regularization
- Dropout Regularization
- Early Stopping
Hyperparameter Tuning
- Grid Search
- Random Search
- Bayesian Optimization
- Evolutionary Algorithms
- Gradient-Based Optimization
Performance Metrics
- Accuracy
- Precision
- Recall
- F1 Score
- ROC Curve
Model Selection Strategies
- Cross-Validation
- Train-Test Split
- K-Fold Cross-Validation
- Grid Search
- Hyperparameter Tuning

Deep Learning

Introduction to Deep Learning
- Artificial Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Autoencoders
- Reinforcement Learning
Artificial Neural Networks
- Introduction to Artificial Neural Networks
- Neural Network Architectures
- Activation Functions
- Training Neural Networks
- Applications of Artificial Neural Networks
Convolutional Neural Networks
- Introduction to Convolutional Neural Networks
- Convolutional Layers
- Pooling Layers
- Activation Functions
- Training Convolutional Neural Networks
Recurrent Neural Networks
- Introduction to Recurrent Neural Networks
- Architecture of Recurrent Neural Networks
- Training of Recurrent Neural Networks
- Applications of Recurrent Neural Networks
- Challenges in Recurrent Neural Networks
Generative Adversarial Networks
- Introduction to GANs
- Generative Models
- Adversarial Networks
- Training GANs
- Applications of GANs
Applications of Deep Learning
- Image Recognition
- Natural Language Processing
- Speech Recognition
- Autonomous Driving
- Recommendation Systems

Natural Language Processing

Introduction to NLP
- Tokenization
- Stemming and Lemmatization
- Parts of Speech Tagging
- Named Entity Recognition
- Text Classification
Text Preprocessing
- Tokenization
- Stop Word Removal
- Stemming
- Lemmatization
- Part-of-Speech Tagging
Text Representation
- Tokenization
- Stemming
- Lemmatization
- Stopwords Removal
- Vectorization
Vector Space Model
- Vector Representation
- Vector Space Model
- Vector Space Operations
- Vector Space Model Applications
- Vector Space Model Evaluation
Language Modeling
- Introduction to Language Modeling
- N-Gram Modeling
- Maximum Likelihood Estimation
- Smoothing Techniques
- Evaluation of Language Models
Part of Speech Tagging
- Introduction to Part of Speech Tagging
- POS Tagging Algorithms
- Evaluation of POS Tagging
- Applications of POS Tagging
- Challenges in POS Tagging
Syntax Analysis
- Lexical Analysis
- Parsing
- Context-Free Grammars
- Ambiguity Resolution
- Syntactic Tree Construction
Semantics Analysis
- Lexical Semantics
- Distributional Semantics
- Semantic Parsing
- Semantic Role Labeling
- Word Sense Disambiguation
Information Retrieval
- Text Preprocessing
- Tokenization
- Part-of-Speech Tagging
- Named Entity Recognition
- Text Classification
Text Classification
- Introduction to Text Classification
- Text Representation
- Supervised Text Classification
- Unsupervised Text Classification
- Evaluation of Text Classification
Text Summarization
- Introduction to Text Summarization
- Techniques for Text Summarization
- Text Pre-processing for Summarization
- Evaluation Metrics for Text Summarization
- Applications of Text Summarization
Applications of NLP
- Text Classification
- Text Summarization
- Named Entity Recognition
- Machine Translation
- Speech Recognition

Reinforcement Learning

Introduction to Reinforcement Learning
- Markov Decision Process
- Temporal Difference Learning
- Q-Learning
- Policy Gradient Methods
- Exploration and Exploitation Trade-off
Markov Decision Process
- Markov Decision Process (MDP)
- Value Iteration
- Policy Iteration
- Optimal Value Function
- Bellman Equation
Dynamic Programming
- Markov Decision Process
- Value Iteration
- Policy Iteration
- Monte Carlo Methods
- Temporal Difference Learning
Monte Carlo Methods
- Monte Carlo Policy Iteration
- Temporal Difference Learning
- Monte Carlo Exploring Starts
- Monte Carlo Tree Search
- Upper Confidence Bound Algorithm
Temporal Difference Learning
- TD Prediction
- TD Control
- Eligibility Traces
- TD Lambda
- TD-Gammon
Q-Learning
- Introduction to Q-Learning
- Q-Table
- Temporal Difference Learning
- Q-Learning Algorithm
- Q-Learning Applications
Exploration and Exploitation Trade-Off
- Multi-armed Bandit Problem
- Exploration Strategies
- Upper Confidence Bound Algorithm
- Thompson Sampling
- Epsilon-Greedy Algorithm
Deep Reinforcement Learning
- Markov Decision Process
- Temporal Difference Learning
- Q-Learning
- Policy Gradient Methods
- Deep Q-Networks

Time Series Analysis

Introduction to Time Series Analysis
- Time Series Components
- Stationarity and Differencing
- Autocorrelation and Partial Autocorrelation
- ARIMA Model
- Forecasting with ARIMA Model
Time Series Components
- Components of Time Series
- Autocorrelation and Partial Autocorrelation
- Stationarity and Differencing
- ARIMA Modeling
- Seasonal ARIMA Modeling
Time Series Decomposition
- Components of Time Series
- Stationarity of Time Series
- Decomposition of Time Series
- Trend Analysis
- Seasonality Analysis
ARIMA Modeling
- Autocorrelation and Partial Autocorrelation
- Stationarity and Differencing
- Identification of ARIMA Model
- Estimation of ARIMA Model
- Model Diagnostics and Forecasting
Forecasting with ARIMA Models
- Introduction to Time Series Analysis
- Autocorrelation and Partial Autocorrelation
- Stationarity and Differencing
- ARIMA Modeling
- Forecasting with ARIMA Models
Exponential Smoothing
- Introduction to Exponential Smoothing
- Single Exponential Smoothing
- Double Exponential Smoothing
- Triple Exponential Smoothing
- Holt-Winters Method
Autoregressive Neural Networks
- Introduction to Autoregressive Neural Networks
- Model Architecture of Autoregressive Neural Networks
- Training Autoregressive Neural Networks
- Applications of Autoregressive Neural Networks
- Evaluation of Autoregressive Neural Networks
Long Short-Term Memory (LSTM) Networks
- Introduction to LSTM Networks
- Recurrent Neural Networks (RNNs)
- LSTM Architecture
- Training LSTM Networks
- Applications of LSTM Networks
Evaluating Time Series Forecasts
- Introduction to Time Series Forecasting
- Time Series Decomposition
- ARIMA Modeling
- Evaluation Metrics for Time Series Forecasting
- Forecast Combination Techniques

Deployment of Machine Learning Models

Model Deployment Overview
- Model Deployment Process
- Model Deployment Strategies
- Model Deployment Frameworks
- Model Deployment Challenges
- Model Deployment Best Practices
Model Deployment Strategies
- Model Versioning
- Model Packaging
- Model Deployment
- Model Monitoring
- Model Maintenance
Automating Model Deployment
- Introduction to Automating Model Deployment
- Model Deployment Strategies
- Model Deployment Frameworks
- Model Deployment Pipelines
- Model Deployment Monitoring
Model Deployment in Production
- Overview of Model Deployment
- Model Deployment Strategies
- Model Versioning
- Model Monitoring
- Model Maintenance
Model Monitoring and Maintenance
- Model Versioning
- Model Performance Monitoring
- Model Retraining
- Model Drift Detection
- Model A/B Testing

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Machine Learning Prerequisites, Tutorials, and Study Materials - Learn the Fundamentals of Machine Learning

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