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- Machine Learning
- _Unit 3
- __Bias
- __Variance
- __Generalization
- __Underfitting
- __Overfitting
- __Linear regression
- __Lasso regression
- __Ridge regression
- __Gradient descent
- __MAE
- __RMSE
- __R2
- _Unit 4
- __K-nearest neighbour
- __Support vector machine
- __Bagging
- __Boosting
- __Random Forest
- __Adaboost
- __Binary-vs-Multiclass Classification
- __Classification Problems
- __Variants of Multiclass Classification
- __Accuracy
- __Precision
- __Recall
- __Fscore
- __Cross-validation
- __Micro-Average Precision
- __Micro-Average Recall
- __Micro-Average F-score
- __Macro-Average Precision
- __Macro-Average Recall
- __Macro-Average F-score
- _Unit 5
- __K-Means
- __K-medoids
- __Hierarchical Clustering
- __Density-based Clustering
- __Spectral Clustering
- __Outlier analysis
- __Isolation factor
- __local outlier factor
- __Elbow method
- __Extrinsic Method
- __Intrinsic method
- _Unit 6
- __ANN
- __Single Layer NN
- __Multilayer Perceptron
- __Back Propagation Learning
- __Functional Link ANN
- __Radial Basis FN
- __Activation functions
- __RNN
- __CNN
- _PYQ Solution
- Design and Analysis of Algorithms
- _Unit 3
- __Greedy strategy
- __knapsack problem
- __Job scheduling
- __activity selection problem
- __Dynamic Programming
- ___Binomial coefficients
- __OBST
- __0/1 knapsack
- __Chain Matrix multiplication
- _Unit 4
- __Backtracking
- __8-queen problem
- __Graph coloring problem
- __Sum of subsets problem
- __Branch-n-Bound
- __Strategies-FIFO
- __LIFO approach
- __LC approach
- __TSP
- __knapsack problem
- _Unit 5
- __Amortized Analysis
- __Accounting Method
- __Potential Function method
- __Amortized analysis-binary counter
- __stack Time-Space tradeoff
- __Tractable Problem
- __Non-Tractable Problem
- __Randomized algorithm
- __Approximate algorithm
- __Embedded Algorithms
- __Embedded system scheduling
- __sorting algorithm
- _Unit 6
- __Multithreaded Algorithms
- __Performance measures
- __Analyzing multithreaded algorithm
- __Parallel loops
- __Race conditions
- __Problem Solving Using MA
- __Multithreaded matrix multiplication
- __Multithreaded merge sort
- __Distributed Algorithms
- __Distributed breadth first search
- __Distributed Minimum Spanning Tree
- __String Matching
- __The Naive string matching algorithm
- __The Rabin-Karp algorithm
- _PYQ Solution
- Blockchain Technology
- _Unit 3
- __Types of Blockchain Platforms
- __Bitcoin
- __Ethereum
- __Hyperledger
- __IoTA
- __Corda
- __Corda R3
- __Consensus in Blockchain
- __Consensus Approach
- __Consensus Elements
- __Consensus Algorithms
- __Proof of Work
- __Byzantine General problem
- __Proof of Stake
- __Proof of Elapsed Time
- __Proof of Activity
- __Proof of Burn
- _Unit 4
- __Bitcoin and the Cryptocurrency
- __Cryptocurrency Basics
- __Types of Cryptocurrency
- __Cryptocurrency Usage
- __Metamask
- __Coinbase
- __Binance
- _Unit 5
- __What is Ethereum
- __Types of Ethereum Networks
- __EVM
- __Smart contracts
- __Purpose and types of Smart Contracts
- __Implementing and deploying SC
- __Swarm
- __Whisper
- _Unit 6
- __Prominent Blockchain Applications
- __Retail
- __Banking and Financial Services
- __Government Sector
- __Healthcare
- __IOT
- __Energy and Utilities
- __Blockchain Integration
- _PYQ Solution
Showing posts with the label Ensemble LearningShow All
Unit 4
Ensemble Learning: Adaboost
Ensemble Learning: Adaboost AdaBoost, short for Adaptive Boosting, is a machi…
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Unit 4
Ensemble Learning: Random Forest
Ensemble Learning: Random Forest Random forest is an ensemble learning algor…
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Unit 4
Ensemble Learning: Boosting
Ensemble Learning: Boosting Boosting is an ensemble learning technique that c…
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Unit 4
Ensemble Learning: Bagging
Ensemble Learning: Bagging Bagging, also known as bootstrap aggregating, is a…
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