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Machine Learning Training 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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Machine Learning Research, Publications, and Books - Discover the Latest in Machine Learning Research

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Machine Learning History and Fundamentals - Explore the Evolution and Building Blocks of Machine Learning

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At 4achievers in Desh, the Machine Learning course provides comprehensive coverage of the fundamentals of Machine Learning, its history, and its applications. 4Achievers course is designed to equip students with the skills and knowledge needed to become proficient in Machine Learning. With the help of this course, students can gain an understanding of the various algorithms used in Machine Learning, the data pre-processing techniques, and the model evaluation methods. 4Achievers course also provides an overview of the research works, publications, and books available on Machine Learning.