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Can You Master Everything?

A Random dose of Knowledge

The more you learn, the more you want to learn; the more wisdom you gain, the wiser you are; the more active you are, the more you need a nap. Right?

No, wait.

We’re living in the era of technology where new things, new research, new models are coming out day by day so do you really think that you can be master everything?

The Answer is NO. You can’t master everything but you can (and should) become a better version of yourself.

Let me explain HOW?

So let’s Start with Machine learning Algorithms

Photo by Alex Knight on Unsplash

I’m just going to talk about the most popular machine learning algorithms.

Now, what is an Algorithm?

In Mathematics and computer science, an algorithm is a finite sequence of well-defined, computer-implementable instructions, typically to solve a class of problems or to perform a computation. Algorithms are always unambiguous and are used as specifications for performing, calculations, data processing, automated reasoning, and other tasks, (source: Wiki)

In Machine Learning we have three different types of learning algorithms

Supervised Learning

In supervised learning, we have input data called training data and has a known label associated with the training data such as cat/dog, spam/not-spam

In which we train the model in training data and test it with testing data.

Unsupervised Learning

In Unsupervised learning, we have only input data but we don’t know the label associated with the input data such as Clustering

Semi-Supervised Learning

In Semi-Supervised learning, we have input data as a mixture of labeled and unlabeled examples.

Machine learning Algos

Regression Algorithms

Regression Algorithms predict the output values based on input features from the data fed in the system. The algorithms build a model on the features of training data and using the model we predict the values for new data.

Ordinary Least Square Regression (OLSR)
Linear Regression
Logistic Regression
Stepwise Regression
Locally Estimated Scatterplot Smoothing (MARS)
Multivariate Adaptive Regression Splines (LOESS)

Memory-Based(Instance-Based) Learning Algorithms

Instead of Performing explicit generalization, compare new problem instances with instances seen in training, Such models typically build up a database and compare new data to the databases using a similarity measure in order to find the best match and make a prediction.

KNN (K- Nearest Neighbor)
SVM (Support Vector Machine)
SOM (Self-Organizing Map)
LWL (Locally Weighted Learning)

Decision Tree Algorithms

They belong to the family of supervised learning algorithms. The goal of using a Decision Tree is the create a training model that can use to predict the class or values of the target variable by learning simple decision rules inferred from prior data

CART (Classification And Regression Tree)
ID3 (Iterative Dichotomiser 3)
CHAID (Chi-Squared Automatic Interaction Detection)
C4.5 and C5.0 (Successor of ID3)

Clustering Algorithms

Cluster Analysis or Clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.

K-Means Clustering
Hierarchical Clustering
K-Medians
Gaussian Mixture Model (GMM)
DBSCAN
Spectral Clustering

Bayesian Algorithms

In Bayesian Algorithms, every pair of features being classified is independent of each other. It is a Statistical model where we use probability to represent all uncertainty within the model, both the uncertainty regarding the output but also the uncertainty regarding the input to the model.

Naive Bayes
Gaussian Naive Bayes
BN (Bayesian Network)
BBN (Bayesian Belief Network)
Multinomial Naive Bayes

Ensemble Algorithms

Ensemble methods use multiple learning, algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.

Random Forest
Bagging Algorithms (Bootstrapped Aggregation)
AdaBoost
Boosting
GBM (Gradient Boosting Machines)
(GBRT) Gradient Boosting Regression Tree

Dimensionality Reduction Algorithms

In Machine Learning there are often too many parameters on the basis of which we predict the output. These factors are basically variables called features. The higher the parameters the harder it gets to work on the training set and visualize it and sometimes the most of the features are redundant. So Dimensionality reduction helps us to reduce the number of redundant parameters.

LDA (Linear Discriminant Analysis)
PCA (Principal Component Analysis)
GDA (Generalized Discriminant Analysis)
Filter, Wrapper, Embedded Methods
Multidimensional Scaling (MDS)

Association Rule Algorithms or Mining Algorithms

Association Algorithms are the rule bases algorithms for discovering interesting relations between variables in large databases.

Apriori Algorithms
Eclat Algorithms

Regularization Algorithms

These algorithms are used in the regression to reduce the complexity of the model and the coefficients of the independent features.

L1 Regularization (Lasso Regression)
L2 Regularization (Ridge Regression)
Elastic Net
Least-Angle Regression (LARS)

Now Let’s Talk about Deep Learning

Photo by Moritz Kindler on Unsplash

Deep Learning is the Subset of Machine Learning, it has networks capable of learning unsupervised from data that is unstructured or unlabeled.

Artificial Neural Network Algorithms

ANN or Artificial Neural Network Algorithms usually called a simple neural network, An ANN is the collection of connected units or nodes called artificial neurons, which loosely work like the model of our biological brain.

Neural Network
Shallow Neural Network (ANN’s)
Multilayer Perceptrons (MLP)
Back-Propagation
Gradient Descent
Stochastic Gradient Descent
Radial Basis Function Network (RBFN)

Deep Learning Algorithms

CNN (Convolutional Network Network)
RNNs (Recurrent Neural Network)
LSTMs (Long Short-Term Memory)
Encoder-Decoder
Attention Model
Stacked Auto-Encoders
DBM (Deep Boltzmann Machine)
DBN (Deep Belief Networks)

Deep Learning for Images/Video

Photo by USGS on Unsplash

This technique allows machines to identify and extract features for images. So that our model can learn from the image’s features and analysis.

Things you can do with the Images/Video

Image Classification
Video Classification
Face Application
Detect Faces
Detect LandMarks
Optical Character Recognition (OCR)
Object Detection
Object Tracking
Image Segmentation
Medical Image Classification
Internet-of-things (IoT)

Deep Learning for Text

Photo by Surendran MP on Unsplash

Text Classification
Vector Space Model
Machine Translation and Document Search
Autocorrect
POS tagging (Part of Speech)
HMM (Hidden Markov Models)
Autocomplete and Word Embeddings
NER (Named Entity Recognition)
Neural Machine Translation
Text Summarization
Question Answering
Chatbot

Time Series Forecasting

Photo by Nick Chong on Unsplash

In Mathematics, a time series is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data (Source WiKi)

In a simple way “We have historical data and we want to predict the new output based on the historical data”

Time Series Algorithms

“You need to be very good at Statistics”

AR (Auto Regression)
MA (Moving Average)
ARMA (Auto-Regressive Moving Average)
ARIMA (Auto-Regressive Integrated Moving Average)
SARIMA (Seasonal Auto-Regressive Integrated Moving Average)
ES (Exponential Smoothing)

This is not the End here,

We haven’t even touched so many areas like Sound Processing, Genetic Algorithm, Graphs, Reinforcement learning, AR, VR in Deep learning,

and Much Much more……..

So do you really think that you can be a master in each and every area? Well Instead of learning each and every topic try to be master it in one or two areas

Things that can help:-

Machine Learning:- https://www.coursera.org/learn/machine-learning

Deep Learning:- https://www.coursera.org/specializations/deep-learning

Natural Language Processing:- https://www.coursera.org/specializations/natural-language-processing

AI For Medical:- https://www.coursera.org/learn/ai-for-medical-diagnosis

Image/Video Processing:- https://www.pyimagesearch.com/

Machine learning blog:- https://machinelearningmastery.com/

My Youtube Channel:- https://youtube.com/himanshutripathi

That’s it for now 👏👏. See you in the next Article.

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