#PCA and DBSCAN

[govindhtech]

Intel Extension For Scikit-learn: Time Series PCA & DBSCAN

Intel studies time series data clustering using density-based spatial clustering of applications with noise (DBSCAN) and PCA for dimensionality reduction. This approach detects patterns in time series data like city traffic flow without labelling. Intel Extension for Scikit-learn boosts performance. Machinery, human behaviour, and other quantitative elements often produce time series data patterns. Manually identifying these patterns is tough. PCA and DBSCAN are unsupervised learning methods that discover these patterns.

Data Creation

It generates synthetic waveform data for time series replication. Data consists of three waveforms supplemented with noise to simulate real-world unpredictability. The authors utilise Gaël Varoquaux's scikit-learn agglomerative clustering example. You may buy it under CC0 or BSD-3Clause.

Intel Extension for Scikit-learn speeds PCA and DBSCAN

PCA and DBSCAN may be accelerated with Intel Extension for Scikit-learn patching. Python module Scikit-learn does machine learning. The Intel Extension for Scikit-learn accelerates scikit-learn applications on Intel CPUs and GPUs in single- and multi-node setups. This plugin dynamically adjusts scikit-learn estimators to improve machine learning training and inference by 100x with equivalent mathematical soundness.

The Intel Extension for Scikit-learn uses the API, which may be activated via the command line or by modifying a few lines in your Python application before importing it:

To use patch_sklearn, import it from sklearnex.

Reduce Dimensionality using PCA

Intel uses PCA to reduce dimensionality and retain 99% of the dataset's variance before clustering 90 samples with 2,000 features:

It uses a pairplot to locate clusters in condensed data:

pd import pandas import seaborn sns

df = pd.DataFrame(XPC, columns=[‘PC1’, ‘PC2’, ‘PC3’, ‘PC4’]) sns.pairplot(df) plt.show()

A DBSCAN cluster

Intel chooses PC1 and PC2 for DBSCAN clustering because the pairplot splits the clusters. Also offered is a DBSCAN EPS parameter estimation. It chose 50 because the PC1 vs PC0 image suggests that the observed clusters should be separated by 50:

Clustered data may be plotted to assess DBSCAN's cluster detection.

Compared to Ground Truth

The graphic shows how effectively DBSCAN matches ground truth data and finds credible coloured groupings. Clustering recovered the data's patterns in this case. It effectively finds and categorises time series patterns using DBSCAN for clustering and PCA for dimensionality reduction. This approach allows data structure recognition without labelled samples.

Intel Scikit-learn Extension

Speed up scikit-learn for data analytics and ML

Python machine learning module scikit-learn is also known as sklearn. For Intel CPUs and GPUs, the Intel Extension for Scikit-learn seamlessly speeds single- and multi-node applications. This extension package dynamically patches scikit-learn estimators to improve machine learning methods.

The AI Tools plugin lets you use machine learning with AI packages.

This scikit-learn plugin lets you:

Increase inference and training 100-fold while retaining mathematical accuracy.

Continue using open-source scikit-learn API.

Enable and disable the extension with a few lines of code or the command line.

AI and machine learning development tools from Intel include scikit-learn and the Intel Extension for Scikit-learn.

Features

Replace present estimators with mathematically comparable accelerated ones to speed up scikit-learn (sklearn). Algorithm Supported

The Intel oneAPI Data Analytics Library (oneDAL) powers the accelerations, so you may run it on any x86 or Intel GPU.

Decide acceleration application:

Patch any compatible algorithm from the command line without changing code.

Two lines of Python code patch all compatible algorithms.

Your script should fix just specified algorithms.

Apply global patches and unpatches to all scikit-learn apps.

[masongrizchel]

Coding Diaries: How to Build a Machine Learning Model

A Step-by-Step Guide to Building a Machine Learning Model

Machine learning transforms industries by enabling computers to learn from data and make accurate predictions. But before deploying an intelligent system, you must understand how to build a machine-learning model from scratch. This guide will walk you through each step—from data collection to model evaluation—so you can develop an effective and reliable model.

Step 1: Data Preparation

The foundation of any machine learning model is high-quality data. Raw data is often messy, containing missing values, irrelevant features, or inconsistencies. To ensure a strong model, follow these steps:

✅ Data Cleaning – Handle missing values, remove duplicates, and correct inconsistencies.

✅ Exploratory Data Analysis (EDA) – Understand the dataset's patterns, distributions, and relationships using statistical methods and visualizations.

✅ Feature Selection & Engineering – Remove redundant or unimportant features and create new features that improve predictive power.

✅ Dimensionality Reduction – Techniques like Principal Component Analysis (PCA) help simplify data without losing critical information.

By the end of this step, your dataset should be structured and ready for training.

Step 2: Splitting the Data

To ensure your model can generalize well to unseen data, you must divide your dataset into:

🔹 Training Set (80%) – Used to train the model.

🔹 Test Set (20%) – Used to evaluate the model’s performance on new data.

Some workflows also include a validation set, which is used for fine-tuning hyperparameters before final testing.

Step 3: Choosing the Right Algorithm

Selecting the right machine learning algorithm depends on your problem type:

🔹 Classification (e.g., spam detection, fraud detection)

Logistic Regression

Support Vector Machines (SVM)

Decision Trees (DT)

Random Forest (RF)

K-Nearest Neighbors (KNN)

Neural Networks

🔹 Regression (e.g., predicting house prices, stock prices)

Linear Regression

Ridge and Lasso Regression

Gradient Boosting Machines (GBM)

Deep Learning Models

🔹 Clustering (e.g., customer segmentation, anomaly detection)

K-Means Clustering

Hierarchical Clustering

DBSCAN

Step 4: Training the Model

Once an algorithm is selected, the model must be trained using the training set. This involves:

✔ Fitting the model to data – The algorithm learns the relationship between input and target variables.

✔ Optimizing hyperparameters – Adjusting settings like learning rate, depth of trees, or number of neighbors to improve performance.

✔ Feature Selection – Keeping only the most informative features for better efficiency and accuracy.

✔ Cross-validation – Testing the model on different subsets of the training data to avoid overfitting.

Step 5: Evaluating the Model

Once trained, the model must be tested to assess its performance. Different metrics are used based on the problem type:

🔹 For Classification Problems

Accuracy – Percentage of correctly predicted instances.

Precision & Recall (Sensitivity) – Measure how well the model detects positives.

Specificity – Ability to correctly classify negatives.

Matthews Correlation Coefficient (MCC) – A balanced metric for imbalanced datasets.

🔹 For Regression Problems

Mean Squared Error (MSE) – Measures average squared prediction error.

Root Mean Squared Error (RMSE) – Interpretable error measure (lower is better).

R² Score (Coefficient of Determination) – Indicates how well the model explains variance in data.

If the model does not perform well, adjustments can be made by refining hyperparameters, selecting better features, or trying different algorithms.

Step 6: Making Predictions and Deployment

Once the model performs well on the test set, it can be used to make predictions on new, unseen data. At this stage, you may also:

✔ Deploy the model – Integrate it into applications, APIs, or cloud-based platforms.

✔ Monitor and improve – Continuously track performance and retrain the model with new data.

Final Thoughts

Building a machine learning model is an iterative process. Data preparation, algorithm selection, training, and evaluation all play critical roles in creating a model that performs well in real-world scenarios.

🚀 Key Takeaways:

✔ Data quality and feature selection are crucial for accuracy.

✔ Splitting data ensures the model can generalize well.

✔ The choice of algorithm depends on the problem type.

✔ Proper evaluation metrics help fine-tune and optimize performance.

By following these steps, you can develop robust machine-learning models that make accurate and meaningful predictions. Ready to start building your own? 🚀

[learning-code-ficusoft]

Introduction to Machine Learning with Python and Scikit-Learn

Machine Learning (ML) is revolutionizing industries by enabling computers to learn patterns from data and make predictions without explicit programming. Python, with its rich ecosystem of libraries, is one of the most popular languages for ML, and Scikit-Learn is a powerful tool that simplifies the implementation of ML models.

This guide introduces ML concepts, walks through key steps in an ML project, and demonstrates how to use Scikit-Learn.

1. What is Machine Learning?

Machine Learning is a subset of Artificial Intelligence (AI) that enables systems to learn from data and improve their performance over time.

Types of Machine Learning

Supervised Learning — The model learns from labeled data (e.g., predicting house prices based on features).

Unsupervised Learning — The model finds patterns in unlabeled data (e.g., customer segmentation).

Reinforcement Learning — The model learns through trial and error, maximizing rewards (e.g., self-driving cars).

2. Why Use Scikit-Learn?

Scikit-Learn is a powerful Python library for ML because: ✅ It provides simple and efficient tools for data analysis and modeling. ✅ It supports various ML algorithms, including regression, classification, clustering, and more. ✅ It integrates well with NumPy, Pandas, and Matplotlib for seamless data processing.

Installation

To install Scikit-Learn, use:bashpip install scikit-learn

3. Key Steps in a Machine Learning Project

Step 1: Import Required Libraries

pythonimport numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error

Step 2: Load and Explore Data

Let’s use a sample dataset from Scikit-Learn:pythonfrom sklearn.datasets import load_diabetes# Load dataset data = load_diabetes() df = pd.DataFrame(data.data, columns=data.feature_names) df['target'] = data.target # Add target column# Display first five rows print(df.head())

Step 3: Preprocess Data

Data preprocessing includes handling missing values, scaling features, and splitting data for training and testing.python# Split data into features (X) and target (y) X = df.drop('target', axis=1) y = df['target']# Split into training and testing sets (80% train, 20% test) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# Standardize features (recommended for ML algorithms) scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test)

Step 4: Train a Machine Learning Model

We’ll use Linear Regression, a simple ML model for predicting continuous values.python# Train the model model = LinearRegression() model.fit(X_train, y_train)# Make predictions y_pred = model.predict(X_test)

Step 5: Evaluate Model Performance

To measure accuracy, we use Mean Squared Error (MSE):pythonmse = mean_squared_error(y_test, y_pred) print(f"Mean Squared Error: {mse:.2f}")

4. Other Machine Learning Models in Scikit-Learn

Scikit-Learn supports various ML algorithms:

Classification: Logistic Regression, Random Forest, SVM

Regression: Linear Regression, Decision Tree, Ridge

Clustering: K-Means, DBSCAN

Dimensionality Reduction: PCA, t-SNE

Example: Using a Random Forest Classifierpythonfrom sklearn.ensemble import RandomForestClassifierclf = RandomForestClassifier(n_estimators=100, random_state=42) clf.fit(X_train, y_train) predictions = clf.predict(X_test)

5. Conclusion

Scikit-Learn makes it easy to implement machine learning models with minimal code. Whether you’re performing data preprocessing, model training, or evaluation, Scikit-Learn provides a comprehensive set of tools to get started quickly.

WEBSITE: https://www.ficusoft.in/python-training-in-chennai/

[paraproject01]

Demystifying Machine Learning: A Beginner's Guide to Projects

Introduction: Machine Learning (ML) is an exciting field that has rapidly gained popularity in recent years. However, for beginners, diving into the world of ML projects can seem daunting. With countless algorithms, libraries, and techniques to choose from, where does one even begin? In this beginner's guide, we'll demystify machine learning projects and provide a roadmap for getting started.

Understanding Machine Learning:

Definition of Machine Learning

Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning

Core Concepts: Training, Testing, and Evaluation

Setting Up Your Environment:

Choosing a Programming Language: Python vs. R

Installing Necessary Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, etc.

Selecting an Integrated Development Environment (IDE): Jupyter Notebook, Spyder, PyCharm, etc.

Identifying a Project Idea:

Identifying Your Interests: Image Recognition, Natural Language Processing (NLP), Predictive Modeling, etc.

Exploring Datasets: Kaggle, UCI Machine Learning Repository, OpenML, etc.

Brainstorming Project Ideas: Sentiment Analysis, Spam Detection, Stock Price Prediction, etc.

Preprocessing Data:

Data Cleaning: Handling Missing Values, Outliers, and Duplicate Entries

Feature Engineering: Creating Relevant Features for Model Training

Data Transformation: Scaling, Normalization, Encoding Categorical Variables

Choosing the Right Algorithm:

Supervised Learning Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests, etc.

Unsupervised Learning Algorithms: K-Means Clustering, Principal Component Analysis (PCA), DBSCAN, etc.

Reinforcement Learning Algorithms: Q-Learning, Deep Q-Networks (DQN), etc.

Model Training and Evaluation:

Splitting Data into Training and Testing Sets

Training the Model

Evaluating Model Performance: Accuracy, Precision, Recall, F1-Score, ROC-AUC, etc.

Fine-Tuning and Optimization:

Hyperparameter Tuning: Grid Search, Random Search, Bayesian Optimization

Model Selection: Cross-Validation Techniques

Handling Overfitting and Underfitting

Deployment and Application:

Saving and Exporting Trained Models

Building User Interfaces or APIs for Model Deployment

Continuous Monitoring and Updating

Resources for Further Learning:

Online Courses and Tutorials

Books and Textbooks

Community Forums and Q&A Platforms

Conclusion: Embarking on a machine learning project as a beginner can be intimidating, but it's also incredibly rewarding. By following the steps outlined in this guide, you'll be equipped with the knowledge and tools necessary to tackle your first ML project with confidence. Remember, the key to success in machine learning is persistence, experimentation, and continuous learning. So, roll up your sleeves, dive in, and let the journey begin!

Visit Para Projects to get Machine Learning Budget Friendly Projects.

[anusha-g]

Data Science

Course Syllabus:

Introduction to Data Science

Understanding Data Science and its significance

Data Science lifecycle and methodologies

Tools and technologies in Data Science

Data Exploration and Visualization

Data collection and preprocessing

Exploratory Data Analysis (EDA)

Data visualization with tools like Matplotlib and Seaborn

Statistical Analysis

Descriptive and inferential statistics

Probability and hypothesis testing

Statistical modeling and significance

Machine Learning Fundamentals

Introduction to Machine Learning

Supervised, Unsupervised, and Semi-supervised learning

Model evaluation and selection

Data Preprocessing and Feature Engineering

Data cleaning and transformation

Feature selection and engineering techniques

Handling missing data and outliers

Supervised Learning

Linear and logistic regression

Decision trees and random forests

Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN)

Unsupervised Learning

Clustering techniques: K-Means, Hierarchical, and DBSCAN

Dimensionality reduction with Principal Component Analysis (PCA)

Recommender systems

Natural Language Processing (NLP)

Text preprocessing and tokenization

Sentiment analysis and text classification

Building chatbots with NLP

Big Data and Data Science Tools

Introduction to Big Data and Apache Hadoop

Data Science with Apache Spark

Cloud-based data analysis platforms

Enroll Today: Join us in mastering Data Science and unlock the potential of data to drive insights and innovation.

[holydetectivestarfish]

No free lunch and why there are so many AI calculations

Before we start: There are a great deal of ways of characterizing the calculations, and it ultimately depends on you what you need to pick and what is best for you.

In computerized reasoning science, there's a hypothesis named, No Free Lunch. According to it, there is no ideal calculation that functions admirably for all undertakings: from regular discourse acknowledgment to getting by in the climate. That is the reason there's a requirement for various instruments.

Calculations can be gathered by their similitudes or learning style. We here give you a brief look at the calculations gathered in light of their learning style.

The purpose for this: it is more natural for a newbie. Characterization of ML calculations in view of likenesses:

Four gatherings of ML calculations

Along these lines, there are typically 4 gatherings of AI calculations in light of how they learn.

Regulated Learning Regulated implies that somebody as an instructor assists the program all through the preparation with handling. There is a preparation set with named information. For instance, you need to help the PC to put green, red, and blue gloves into various bins.

To start with, you need to show the PC every one of the things and determine what's going on with everything. Then, run the program on an approval set that checks whether the learned capability was right.

The program makes attestations and while observing that the ends are off-base, the software engineer adjusts it. Until the model accomplishes an ideal degree of exactness on the preparation information, the preparation interaction proceeds.

Software engineers every now and again utilize this sort of learning for arrangement and relapse.

Calculation models:

Gullible Bayes,

Support Vector Machine,

Choice Tree,

K-Closest Neighbors

Strategic Relapse,

Direct and Polynomial relapses.

Utilized for: spam sifting, PC vision, language discovery, search, and grouping.

Solo Learning In solo realizing, there is compelling reason need to give any highlights to the program and permitting it to autonomously look for designs. Attempt to figure out this way, assume you have a major crate of clothing that the framework needs to isolate into various classes: socks, Shirts, pants.

Bunching is this. Furthermore, we oftentimes utilize solo figuring out how to separate information into bunches by similitude.

For wise information examination, unaided learning is likewise great. Indeed, even the program can in some cases perceive designs that would be missed by people as a result of the powerlessness to handle a lot of mathematical information.

For instance, UL can be utilized to track down deceitful exchanges, limits, and conjecture deals or investigate inclinations of clients in view of their set of experiences. The software engineers themselves don't have the foggiest idea what are they attempting to find however without a doubt there are a few examples and the framework can recognize them.

Calculation models:

K-implies bunching,

DBSCAN,

Mean-Shift,

Particular Worth Deterioration (SVD),

Head Part Investigation (PCA),

Inert Dirichlet allotment (LDA),

Idle Semantic Investigation, FP-development.

Utilized for: division of information, inconsistency discovery, proposal frameworks, risk the board, counterfeit picture investigation.

Semi-regulated Learning As the title is proposing, semi-regulated learning implies that the info information is a combination of named and unlabelled examples.

The ideal forecast result is in the psyche of the developer yet the model ought to track down examples to structure the information and make expectations itself.

Support Learning Support learning is basically the same as people learn for example through the path. We people don't require consistent management to advance really like in administered learning. We advance actually by getting positive or negative support signals because of our activities. For instance, solely after feeling torment, a youngster learns not to contact a hot container.

One of the most interesting pieces of Support Learning is, it permits you to move back from preparing on static datasets. All things being equal, the framework can learn in powerful and loud conditions like game universes or this present reality.

Also Read : Man-made brainpower ,AI and Profound learning

Games are extremely valuable for support learning research. This is on the grounds that they give ideal information rich conditions. The score in games is ideal award signs to prepare reward-spurred ways of behaving. For instance, Mario.

Calculation models:

Utilized for: self-driving vehicles, games robots, asset the executives.

Summarizing Man-made reasoning has currently numerous extraordinary applications that are impacting the world with regards to innovation. To make an artificial intelligence framework that is for the most part as keen as people stay a fantasy yet basically we are in this stage where ML permits the PC to beat us in calculations, design acknowledgment as well as oddity discovery.

[moreyouread]

Hands on Machine Learning

Chapter 1-2

- batch vs online learning

- instance vs model learning

- hyperparameter grid search

Chapter 3

- 1-precision (x) vs recall (y) is the ROC curve

- true positive rate = recall = sensitivity and true negative rate is = precision = specificity

- harmonic mean to balance precision and recall averages

Chapter 4

- training data with 3 different types of stochastic gradient descent: batch, mini batch, stochastic (with single sample row)

- cross entropy error is minimized for logistic regression

- softmax for multi class predictions. multi-label vs multi-class predictions where labels are mutually exclusive. Softmax is used when mutually exclusive labels.

- softmax helps the gradient not die, while argmax will make it disappear

Chapter 5 SVM

- svm regression is opposite of few points in the street but actually more

- hard vs soft margin classification (like output is a probability vs 1 or 0?)

- kernel trick makes non-linear classification less computationally complex

- dual problem is a problem with a similar or in this case the same mathematical solution as the primal problem of maximizing the distance between the boundaries

- things to better understand: kernel SVM thru Mercer’s condition, how hinge loss applies to SVM solved with gradient descent

Chapter 6

- trees are prone to overfit and regressions are sensitive to the orientation of the data (can be fixed with PCA)

Chapter 7

- ensemble through bagging or pasting: one with replacement and the other without, leading to OOB error

- extra randomized trees when splits on nodes for the tree is done on a random threshold. It’s called random trees bc of using only a subset of features and data points for each tree

- Adaboost (weighting wrong predictions more) vs. gradient boost (adding predictions on all the error residuals)

- stacking is a separate model used to aggregate multiple models instead of a hard vote

Chapter 9 unsupervised

- Silhouette score, balance intra and inter cluster scores, but can do for each cluster to get you a balance within the clusters

- DBSCAN density clustering, sihoulette score to find the optimal epsilon, working well for dense clusters. Don’t need to specify number of clusters

- Gaussian Mixture Model, also density clustering working well for ellipsoid clusters. Do need to specify cluster number, and covariance type of the types of shapes, which would mess it up. It also helps with anomaly detection because of p values. This can’t use silhouette score bc they’re not spherical shapes because of biases of distances.

- Bayesian GMM, similar to lasso for GMM, to set cluster count for you with priors

- Latent class, which is the cluster label of a latent variable

Chapter 13 CNN computer vision

- CNN uses a square to go over pixels in a square, some with zero padding; this is called “convolving”

- the layers are actual horizontal and vertical filters, that the model uses to multiple against inputted image

- these filters can be trained to eventually become pattern detectors. Patterns could be dog faces or even edges

- a pooling layer doesn’t detect patterns but simply averages things together, simplifying complex images

- QUESTION: how does the pattern eventually detect if yes or no for training if something is a dog for instance?

Chapter 8 Dimensionality Reduction

- PCA: projection onto a hyperplane in a dimension, max with the same number of features. The number of top dimensions you pick is your hyper parameter, with the max being the dimensions you are in. The next line is orthogonal for projection

- Kernel PCA: vector is curved or circular, not just 1 straight line. The additional hyper parameter is the shape of the curved lines used. It’s a mathematical transformation used to make different data points linearly separable in a higher dimension (making lines in a lower dimension look curved) without actually having to go to the higher dimension.

- you can decompress by multiplying by the inverse transformation. Then you see how off you are from the actual image, i.e reconstruction error

- another measurement is explained variance ratio for each dimension n, also chosen with an elbow plot

- manifold learning is twisting, unfolding, etc from a 2D space to 3D space

Chapter 14

- RNN predict time series and NLP

- it is a loop with time, each previous layer feeding into the next

- can be shorted with probabilistic dropout and feeding older t-20 to t-1 outputs, to prevent vanishing gradient

- LTSM cell allows you to recognize what’s an important input vs an unimportant one to forget

- encoder vs decoder for machine translation NLP occurs such that encoders are fed in a series as one output to a series of decoders, each with its own output. https://youtu.be/jCrgzJlxTKg

Chapter 15 autoencoders

a neural function that encodes and decodes, predicting itself (technically unsupervised but is a supervised training neural network with fewer outputs in the middle ie the encoder which simplifies and then the same number of outputs as inputs in the final layer.

GANS used autoencoders to build additional data, and autoencoders are dimensionality reducers.

Questions: how is it reducing dimensionality if the same number of outputs as inputs exist?

It’s helpful for detecting anomalies or even predicting if something is a different class. If the error bar of the output and input is super large, it is likely an anomaly or different class.

https://youtu.be/H1AllrJ-_30

https://youtu.be/yz6dNf7X7SA

Reinforcement learning

Q-learning is a value derived to punish or reward behaviors at each step in reinforcement learning

Reinforcement learning requires doing a lot of steps and getting just 1 success criteria at the end

It can be trained with stochastic gradient descent, boosting the actions with gradient descent that yielded more positive end Q score results

QUESTIONS

- does waiting longer days increase power? Or does it increase only in so far that sample size increases with more days of new users exposed? More days of data even with the same sample size will decrease std.

[futuregenstuff]

MACHINE LEARNING courses in Ameerpet Hyderabad

MACHINE LEARNING courses in Ameerpet Hyderabad

1.Python: (a)Python –Core (b)Python-Advance

2.Data Analysis and Visualization

3.Machine Learning and Natural Language Processing

4.Vilsualization Tools •Tableau •Qlik view

5.Big Data Tools: •Hadoop •Apache Spark •SQL •Scala

 1. Supervised Learning •Regression Techniques •Classification Techniques •Ensemble Methods •DISTANCE BASED MODULES •Support Vector Machines

2. Unsupervised Learning •Principal Components Analysis(PCA) •DBSCAN •K-Means •Hierarchical clustering •Association Rules •Apriori

3. Hyper Parameter Tuning 4. Natural Language Processing

 Lear more- https://www.futuregentechnologies.com/masters-in-machine-learning#1611062056904-75d07b84-223a

[wonbindatascience]

Clustering

K-means

https://scikit-learn.org/stable/modules/clustering.html#k-means

This algorithm requires the number of clusters to be specified.

The K-means algorithm aims to choose centroids(mean values) that minimise the inertia, or within-cluster sum-of-squares criterion:

Note that centroids are not, in general, points from X, although they live in the same space.

Inertia can be recognized as a measure of how internally coherent clusters are.

Inertia suffers from various drawbacks:

nertia makes the assumption that clusters are convex and isotropic, which is not always the case. It responds poorly to elongated clusters, or manifolds with irregular shapes.

Inertia is not a normalized metric: we just know that lower values are better and zero is optimal. But in very high-dimensional spaces, Euclidean distances tend to become inflated (this is an instance of the so-called “curse of dimensionality”). Running a dimensionality reduction algorithm such as Principal component analysis (PCA) prior to k-means clustering can alleviate this problem and speed up the computations.

The algorithm has three steps.

The first step chooses the initial centroids, with the most basic method being to choose k samples from the dataset X.

After initialization, K-means consists of looping between the two other steps. The first step assigns each sample to its nearest centroid.

The second step creates new centroids by taking the mean value of all of the samples assigned to each previous centroid. The difference between the old and the new centroids are computed and the algorithm repeats these last two steps until this value is less than a threshold. In other words, it repeats until the centroids do not move significantly.

Advantages and disadvantages

https://developers.google.com/machine-learning/clustering/algorithm/advantages-disadvantages

Advantages

Relatively simple to implement.

Scales to large data sets.

Guarantees convergence.

Can warm-start the positions of centroids.

Easily adapts to new examples.

Generalizes to clusters of different shapes and sizes, such as elliptical clusters.

Disadvantages

Choosing  manually.

Being dependent on initial values.

Clustering data of varying sizes and density.

Clustering outliers.

Scaling with number of dimensions.

Evaluation

https://towardsdatascience.com/k-means-clustering-algorithm-applications-evaluation-methods-and-drawbacks-aa03e644b48a

Elbow method

Elbow method gives us an idea on what a good k number of clusters would be based on the sum of squared distance (SSE) between data points and their assigned clusters’ centroids.

(The graph below shows that k=2 is not a bad choice.)

DBSCAN

https://scikit-learn.org/stable/modules/clustering.html#dbscan

Clusters found by DBSCAN can be any shape, as opposed to k-means which assumes that clusters are convex shaped.

There are two parameters to the algorithm, min_samples and eps, which define formally what we mean when we say dense. Higher min_samples or lower eps indicate higher density necessary to form a cluster.

While the parameter min_samples primarily controls how tolerant the algorithm is towards noise (on noisy and large data sets it may be desirable to increase this parameter),

the parameter eps is crucial to choose appropriately for the data set and distance function and usually cannot be left at the default value.

[startupjobsasia]

Data Scientist - Hong Kong job at Pulse iD Hong-Kong

Pulse iD is an identity platform that works with innovative banks, telcos & media companies. Our services analyse geolocation data from mobile apps to unlock powerful security, loyalty & identity services.

●      At least one year experience in a relevant position.

●      Excellent understanding of supervised and unsupervised machine learning techniques and algorithms, such as k-NN, DBSCAN, PCA, Naive Bayes, SVM, Random Forests, etc.

●      Experience with common data science toolkits, such as SciPy, NumPy, Pandas, Scikit-learn, R, Weka, TensorFlow, etc. Excellence in at least one of these is highly desirable.

●      Experience with data visualisation tools, such as Matplotlib, Bokeh, Seaborn, GGplot, Plotly, D3.js, etc.

●      Excellent proficiency in SQL.

●      Good applied statistics skills, such as regression modelling, statistical testing, etc.

●      Good scripting and programming skills in Python or Scala.

●      Strong presentation and communication skills, explaining complex analytical concepts to people from other fields

 Nice to have

●      Experience analyzing sensor and smartphone data.

●      Experience building data based products.

●      Worked extensively with Apache Spark.

●      Experience working with Apache Zeppelin.

●      University degree in a relevant field.

StartUp Jobs Asia - Startup Jobs in Singapore , Malaysia , HongKong ,Thailand from http://www.startupjobs.asia/job/33812-data-scientist-hong-kong-big-data-job-at-pulse-id-hong-kong

[freetutorialstack-blog]

Data Science Masterclass With R! 4 Projects+8 Case Studies

Description

What Projects We are Going to Cover In the Course? Project 1- Titanic Case Study which is based on Classification Problem. Project 2 - E-commerce Sale Data Analysis - based on Regression. Project 3 - Customer Segmentation which is based on Unsupervised learning. Final Project - Market Basket Analysis - based on Association rule mining Why Data Science is a MUST HAVE for Now A Days? The Answer Why Data Science is a Must have for Now a days will take a lot of time to explain. Let's have a look into the Company name who are using Data Science and Machine Learning. Then You will get the Idea How it BOOST your Salary if you have Depth Knowledge in Data Science & Machine Learning! What You Will Learn From The Data Science MASTERCLASS Course: Learn what is Data science and how Data Science is helping the modern world! What are the benefits of Data Science , Machine Learning and Artificial Intelligence Able to Solve Data Science Related Problem with the Help of R Programming Why R is a Must Have for Data Science , AI and Machine Learning! Right Guidance of the Path if You want to be a Data Scientist + Data Science Interview Preparation Guide How to switch career in Data Science? R Data Structure - Matrix, Array, Data Frame, Factor, List Work with R’s conditional statements, functions, and loops Systematically explore data in R Data Science Package: Dplyr , GGPlot2 Index, slice, and Subset Data Get your data in and out of R - CSV, Excel, Database, Web, Text Data Data Science - Data Visualization : plot different types of data & draw insights like: Line Chart, Bar Plot, Pie Chart, Histogram, Density Plot, Box Plot, 3D Plot, Mosaic Plot Data Science - Data Manipulation - Apply function, mutate(), filter(), arrange (), summarise(), groupby(), date in R Statistics - A Must have for Data Science Data Science - Hypothesis Testing Business Use Case Understanding Data Pre-processing  Supervised Learning Logistic Regression  K-NN  SVM  Naive Bayes  Decision Tree  Random Forest K-Mean Clustering  Hierarchical Clustering  DBScan Clustering  PCA (Principal Component Analysis) Association Rule Mining  Model Deployment      Read the full article

[eurekakinginc]

"[D] Classifier for tSNE or UMAP results?"- Detail: Recently I worked on a binary classification problem. The input data is a high dimension (>100) series. I tried PCA to lower the input to a much smaller dimension (<10) then applied Gradient Boosting on it and this seems to give good result. However I want to improve the results by replacing the PCA part since the classifier is not necessarily linear.​I tried both tSNE and UMAP and they can bring out clusters even in 2D. However I don't know what to do next:Should I use clustering algorithms like DBSCAN to do the binary classification? How should I do that? One of the issue is that although I can see a cluster of positives, there are also clusters of mixed positives and negatives that I couldn't label;I tried to put UMAP results to Gradient Boosting and to my surprise, it actually give poorer classification than PCA + Gradient Boosting. One issue I believe is that I only tried tSNE and UMAP at 2 or 3 dimensions because the computation time involved. So is there a way (in tSNE or UMAP) to know the intrinsic dimension of a input dataset, like the explained variance or factor loadings in PCA?​I tried to read many articles on how to use tSNE/UMAP properly but it seems most of them focused on visualization and clustering.. Caption by dinoaide. Posted By: www.eurekaking.com

[learning-code-ficusoft]

Common Pitfalls in Machine Learning and How to Avoid Them

Selecting and training algorithms is a key step in building machine learning models. 

Here’s a brief overview of the process: 

Selecting the Right Algorithm The choice of algorithm depends on the type of problem you’re solving (e.g., classification, regression, clustering, etc.), the size and quality of your data, and the computational resources available. 

Common algorithm choices include: 

For Classification: Logistic Regression Decision Trees Random Forests Support Vector Machines (SVM) k-Nearest Neighbors (k-NN) Neural 

Networks For Regression: Linear Regression Decision Trees Random Forests Support Vector Regression (SVR) Neural Networks For Clustering: 

k-Means DBSCAN Hierarchical Clustering For Dimensionality Reduction: Principal Component Analysis (PCA) t-Distributed Stochastic Neighbor Embedding (t-SNE) 

Considerations when selecting an algorithm: 

Size of data: 

Some algorithms scale better with large datasets (e.g., Random Forests, Gradient Boosting). 

Interpretability: 

If understanding the model is important, simpler models (like Logistic Regression or Decision Trees) might be preferred. 

Performance:

 Test different algorithms and use cross-validation to compare performance (accuracy, precision, recall, etc.). 

2. Training the Algorithm After selecting an appropriate algorithm, you need to train it on your dataset. 

Here’s how you can train an algorithm:

 Preprocess the data: 

Clean the data (handle missing values, outliers, etc.). Normalize/scale the features (especially important for algorithms like SVM or k-NN). 

Encode categorical variables if necessary (e.g., using one-hot encoding). 

Split the data:

 Divide the data into training and test sets (typically 80–20 or 70–30 split). 

Train the model: 

Fit the model to the training data using the chosen algorithm and its hyperparameters. Optimize the hyperparameters using techniques like Grid Search or Random Search. 

Evaluate the model: Use the test data to evaluate the model’s performance using metrics like accuracy, precision, recall, F1 score (for classification), mean squared error (for regression), etc. 

Perform cross-validation to get a more reliable performance estimate. 

3. Model Tuning and Hyperparameter Optimization Hyperparameter tuning: Many algorithms come with hyperparameters that affect their performance (e.g., the depth of a decision tree, learning rate for gradient descent). 

You can use methods like: Grid Search: 

Try all possible combinations of hyperparameters within a given range. 

Random Search: 

Randomly sample hyperparameters from a range, which is often more efficient for large search spaces. 

Cross-validation: 

Use k-fold cross-validation to get a better understanding of how the model generalizes to unseen data. 

4. Model Evaluation and Fine-tuning Once you have trained the model, fine-tune it by adjusting hyperparameters or using advanced techniques like regularization to avoid overfitting. 

If the model isn’t performing well, try:

 Selecting different features. 

Trying more advanced models (e.g., ensemble methods like Random Forest or Gradient Boosting).

Gathering more data if possible. 

By iterating through these steps and refining the model based on evaluation, you can build a robust machine learning model for your problem.

WEBSITE: https://www.ficusoft.in/data-science-course-in-chennai/

[craigbrownphd-blog-blog]

Overview and Classification of Machine Learning Problems

  Topic Difficulty Level (High / Low) Questions Refs / Answers 1. Text Mining L Explain :TFIDF,  Stanford NLP, Sentiment Analysis, Topic Modelling   2. Text Mining H Explain Word2Vec. Explain how word vectors are created https://www.tensorflow.org/tutorials/word2vec 3. Text Mining L Explain Distance :  hamming,  cosine or eucleadean.   4. Text Mining H How can I get single vector for sentence / paragraphs / document using word2vec ?  https://radimrehurek.com/gensim/models/doc2vec.html 5. Dimestion Reduction L Suppoese I have TFIDF matrix having dimensions 1000x25000. I want to reduce the the dimensions to 1000x500. What are the ways available ?  PCA , SVD, (max df, min df, max features in TFIDF) 6. Dimestion Reduction H Kernel PCA, tSNE http://scikit-learn.org/stable/modules/decomposition.html#decompositions 7. Supervised Learning H Uncorrelated vs highly corelated features :  How they will affect linear regression vs GBM vs Random Forest GBM and RF are least affected 8. Supervised Learning L If Metioned in Resume ask about : Logistic Regression, RF, Boosted Trees, SVM, NN   9. Supervised Learning L Explain Bagging Vs Boosting   10.  Supervised Learning L Explain how variable importance is computed in RF and GBM   11.  Supervised Learning H What is Out Of bag in bagging   12.  Supervised Learning H What is difference between adaboost and gradient boosted trees   13.  Supervised Learning H What is learning rate ? What will happen if I increase my rate from 0.01 to 0.6  The learning will be unncessarlity fast and the chances are that because of increased learning rate, global minima will be missed and weights will fluctuate. But if learning rate is 0.01, the learning will be slow and the chances are model will get stuck in local minima. Learning rate shoul dbe decided based on CV / parameter tuning 14. Supervised Learning L How would you choose parameters of any model? http://scikit-learn.org/stable/modules/grid_search.html 15. Supervised Learning L Evaluation of Supervised Learning, Log Loss, Accuracy , sensitivity, specificity, AUC-ROC curve, Kappa http://scikit-learn.org/stable/modules/model_evaluation.html 16. Supervised Learning L My data has 1% Lable 1 and 99% lalel 0 , and my model has 99% accuracy? Should I be happy ? Explain Why No. This might just mean that model has predicted all 0s with no intelligence. Look at Confusion Mat, Sensitivity Specificity, Kappa etc. Try oversampling, Outlier Detection , diferent algos like RusBoost etc 17. Supervised Learning H How can I increase the percentage of Minority class representation in this case ? SMOTE, Random Oversampling 18. Unsupervised Learning L Explain Kmeans  http://scikit-learn.org/stable/modules/clustering.html#clustering 19. Unsupervised Learning L How to choose no of clusters in K means https://www.quora.com/How-can-we-choose-a-good-K-for-K-means-clustering 20. Unsupervised Learning H How to evaluate unsupervised learning algorithms http://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation 21. Unsupervised Learning H Which algorithm doesn’t require no of clusters as an input ? Birch , DBSCAN, etc http://scikit-learn.org/stable/modules/clustering.html#overview-of-clustering-methods 22. Unsupervised Learning H Explain AutoEncoder- Decoders   23. Data Preprocessing L Normalising the data : How to normalise Train and Test data http://scikit-learn.org/stable/modules/preprocessing.html#custom-transformers 24. Data Preprocessing L Categorical variables : How to convert categorical variablesin to features 1- when no ordering, 2- when ordering Dummy / one hot Encoding , Thermometer Encoding 25. Unsupervised Learning H How kmeans will be affected in the presence of dummy variables   26. Deep Learning H Deep learning : Explain activation function  : ReLu, Fermi / sigmoid , Tanh ,etc www.deeplearningbook.org 27. Supervised Learning L Explain Cross Validation : Simple,  , If it is time series data can normal cross validation work ? http://scikit-learn.org/stable/modules/cross_validation.html 28. Supervised Learning L Explain : Stratified and LOO CV http://scikit-learn.org/stable/modules/cross_validation.html 29. Supervised Learning H In Ensemble Learning, What is Soft Voting and Hard Voting http://scikit-learn.org/stable/modules/ensemble.html#voting-classifier 30. Supervised Learning L Ensemble Learning: If correlations of prediction between 3 classifiers is >0.95 should I ensemble the outputs? Why if Yes andNO?   31. Optimisation H What is regularisation, is linear regression regularised , if no then how it can be regularised L1, l2 : See Ridge and lasso  32. Supervised Learning L Which algorithms will afected by Random Seed : Logistic regression, SVM, RandomForest,  Neural nets RF and NN 33. Supervised Learning H What is Look Ahead Bias ? How it can be identified ?   34. Supervised Learning H Situation : I have 1000 Samples and 500 Features. I want to select 50 features. I Check the correlation of each of the 500 variable with Y using 100 samples and then use top 50. After doing this step I run  cross validation on 1000 sample. What is the problem here ? This has Look Ahead Bias 35. Optimisation H Explain Gradient Descent. Which one is better Gradient Descent or SGD or ADAM ? http://ruder.io/optimizing-gradient-descent/ 36. Supervised Learning L Which algorithm is faster : GBM Trees or xgBoost ? Why Xgboost : https://arxiv.org/abs/1603.02754 37. Deep Learning H Explain back progapagation  www.deeplearningbook.org 38. Deep Learning H Explain Softmax www.deeplearningbook.org 39. Deep Learning H DL : For Time series which archeture is used : MLP / LSTM / CNN ? Why ? www.deeplearningbook.org 40. Deep Learning H Is it required ot normalise the data in neural nets ? Why ? www.deeplearningbook.org 41. Optimisation L My Model has Very High Variance but Low Bias. Is this overfitting or underfitting ? If ans is Overfitting ( Which is correct) how can I make sure I don’t overfit.   42. Deep Learning H Explain Early Stopping http://www.deeplearningbook.org/contents/regularization.html#pf20 43. Deep Learning H Explain Dropout. Is bagging and dropout similar concepts ? If No , what  is the difference ? http://www.deeplearningbook.org/contents/regularization.html#pf20 https://goo.gl/gWrdWD #DataScience #Cloud

[techinfogo]

Ml & AI

AI is the field of study that gives PCs the ability to learn without being expressly modified. ML is one of the most energizing innovations that one would have ever gone over. As it is obvious from the name, it gives the PC that makes it progressively like people: The capacity to learn. AI is effectively being utilized today, maybe in a lot a greater number of spots than one would anticipate.

Late Articles on Machine Learning !

Presentation

Information and it's Processing

Administered Learning

Solo Learning

Support Learning

Dimensionality Reduction

Normal Language Processing

Neural Networks

ML – Applications

Various

Presentation :

Beginning with Machine Learning

An Introduction to Machine Learning

What is Machine Learning ?

Prologue to Data in Machine Learning

Demystifying Machine Learning

ML – Applications

Best Python libraries for Machine Learning

Computerized reasoning | An Introduction

AI and Artificial Intelligence

Distinction between Machine learning and Artificial Intelligence

Operators in Artificial Intelligence

10 Basic Machine Learning Interview Questions

Information and It's Processing:

Prologue to Data in Machine Learning

Understanding Data Processing

Python | Create Test DataSets utilizing Sklearn

Python | Generate test datasets for Machine learning

Python | Data Preprocessing in Python

Information Cleansing

Highlight Scaling – Part 1

Highlight Scaling – Part 2

Python | Label Encoding of datasets

Python | One Hot Encoding of datasets

Taking care of Imbalanced Data with SMOTE and Near Miss Algorithm in Python

Managed learning :

Beginning with Classification

Essential Concept of Classification

Sorts of Regression Techniques

Order versus Regression

ML | Types of Learning – Supervised Learning

Multiclass order utilizing scikit-learn

Angle Descent :

Angle Descent calculation and its variations

Stochastic Gradient Descent (SGD)

Little Batch Gradient Descent with Python

Advancement systems for Gradient Descent

Prologue to Momentum-based Gradient Optimizer

Straight Regression :

Prologue to Linear Regression

Angle Descent in Linear Regression

Numerical clarification for Linear Regression working

Typical Equation in Linear Regression

Straight Regression (Python Implementation)

Straightforward Linear-Regression utilizing R

Univariate Linear Regression in Python

Various Linear Regression utilizing Python

Various Linear Regression utilizing R

Privately weighted Linear Regression

Python | Linear Regression utilizing sklearn

Straight Regression Using Tensorflow

A Practical way to deal with Simple Linear Regression utilizing R

Straight Regression utilizing PyTorch

Pyspark | Linear relapse utilizing Apache MLlib

ML | Boston Housing Kaggle Challenge with Linear Regression

Python | Implementation of Polynomial Regression

Softmax Regression utilizing TensorFlow

Calculated Regression :

Understanding Logistic Regression

Why Logistic Regression in Classification ?

Calculated Regression utilizing Python

Cost work in Logistic Regression

Strategic Regression utilizing Tensorflow

Gullible Bayes Classifiers

Bolster Vector:

Bolster Vector Machines(SVMs) in Python

SVM Hyperparameter Tuning utilizing GridSearchCV

Bolster Vector Machines(SVMs) in R

Utilizing SVM to perform characterization on a non-direct dataset

Choice Tree:

Choice Tree

Choice Tree Regression utilizing sklearn

Choice Tree Introduction with model

Choice tree usage utilizing Python

Choice Tree in Software Engineering

Irregular Forest:

Irregular Forest Regression in Python

Troupe Classifier

Casting a ballot Classifier utilizing Sklearn

Packing classifier

Unaided learning :

ML | Types of Learning – Unsupervised Learning

Regulated and Unsupervised learning

Bunching in Machine Learning

Various Types of Clustering Algorithm

K implies Clustering – Introduction

Elbow Method for ideal estimation of k in KMeans

ML | K-means++ Algorithm

Examination of test information utilizing K-Means Clustering in Python

Smaller than usual Batch K-implies bunching calculation

Mean-Shift Clustering

DBSCAN – Density based grouping

Actualizing DBSCAN calculation utilizing Sklearn

Fluffy Clustering

Phantom Clustering

OPTICS Clustering

OPTICS Clustering Implementing utilizing Sklearn

Various leveled bunching (Agglomerative and Divisive grouping)

Actualizing Agglomerative Clustering utilizing Sklearn

Gaussian Mixture Model

Support Learning:

Support learning

Support Learning Algorithm : Python Implementation utilizing Q-learning

Prologue to Thompson Sampling

Hereditary Algorithm for Reinforcement Learning

SARSA Reinforcement Learning

Q-Learning in Python

Dimensionality Reduction :

Prologue to Dimensionality Reduction

Prologue to Kernel PCA

Head Component Analysis(PCA)

Head Component Analysis with Python

Autonomous Component Analysis

Highlight Mapping

Additional Tree Classifier for Feature Selection

Chi-Square Test for Feature Selection – Mathematical Explanation

ML | T-conveyed Stochastic Neighbor Embedding (t-SNE) Algorithm

Python | How and where to apply Feature Scaling?

Parameters for Feature Selection

Underfitting and Overfitting in Machine Learning

Common Language Processing :

Prologue to Natural Language Processing

Content Preprocessing in Python | Set – 1

Content Preprocessing in Python | Set 2

Expelling stop words with NLTK in Python

Tokenize content utilizing NLTK in python

How tokenizing content, sentence, words works

Prologue to Stemming

Stemming words with NLTK

Lemmatization with NLTK

Lemmatization with TextBlob

How to get equivalent words/antonyms from NLTK WordNet in Python?

Neural Networks :

Prologue to Artificial Neutral Networks | Set 1

Prologue to Artificial Neural Network | Set 2

Prologue to ANN (Artificial Neural Networks) | Set 3 (Hybrid Systems)

Prologue to ANN | Set 4 (Network Architectures)

Actuation capacities

Executing Artificial Neural Network preparing process in Python

A solitary neuron neural system in Python

Convolutional Neural Networks

Machine Learning

Prologue to Convolution Neural Network

Prologue to Pooling Layer

Prologue to Padding

Kinds of cushioning in convolution layer

Applying Convolutional Neural Network on mnist dataset

Intermittent Neural Networks

Prologue to Recurrent Neural Network

Intermittent Neural Networks Explanation

seq2seq model

Prologue to Long Short Term Memory

Long Short Term Memory Networks Explanation

Gated Recurrent Unit Networks(GAN)

Content Generation utilizing Gated Recurrent Unit Networks

GANs – Generative Adversarial Network

Prologue to Generative Adversarial Network

Generative Adversarial Networks (GANs)

Utilize Cases of Generative Adversarial Networks

Building a Generative Adversarial Network utilizing Keras

Modular Collapse in GANs

Prologue to Deep Q-Learning

Actualizing Deep Q-Learning utilizing Tensorflow

ML – Applications :

Precipitation expectation utilizing Linear relapse

Recognizing transcribed digits utilizing Logistic Regression in PyTorch

Kaggle Breast Cancer Wisconsin Diagnosis utilizing Logistic Regression

Python | Implementation of Movie Recommender System

Bolster Vector Machine to perceive facial highlights in C++

Choice Trees – Fake (Counterfeit) Coin Puzzle (12 Coin Puzzle)

Charge card Fraud Detection

NLP examination of Restaurant audits

Applying Multinomial Naive Bayes to NLP Problems

Picture pressure utilizing K-implies grouping

Profound learning | Image Caption Generation utilizing the Avengers EndGames Characters

How Does Google Use Machine Learning?

How Does NASA Use Machine Learning?

5 Mind-Blowing Ways Facebook Uses Machine Learning

Directed Advertising utilizing Machine Learning

How Machine Learning Is Used by Famous Companies?

Misc :

Example Recognition | Introduction

Compute Efficiency Of Binary Classifier

Calculated Regression v/s Decision Tree Classification

R versus Python in Datascience

Clarification of Fundamental Functions engaged with A3C calculation

Differential Privacy and Deep Learning

Man-made consciousness versus Machine Learning versus Deep Learning

Prologue to Multi-Task Learning(MTL) for Deep Learning

Top 10 Algorithms each Machine Learning Engineer should know

Sky blue Virtual Machine for Machine Learning

30 minutes to AI

What is AutoML in Machine Learning?

Disarray Matrix in Machine Learning

Learn More Here

[rafi1228]

Data Science by IITian -Data Science+R Programming ,Data analysis, Data Visualization, Data Science: Data Pre-processing

What you’ll learn

Learn what is Data Science and how it is helping the modern world!

What are the benefits of Data Science and Machine Learning

Able to Solve Data Science Related Problem with the Help of R Programming

Why R is a Must Have for Data Science , AI and Machine Learning!

Right Guidance of the Path if You want to be a Data Scientist + Data science Interview Preparation Guide

How to switch career in Data Science?

R Data Structure – Matrix, Array, Data Frame, Factor, List

Work with R’s conditional statements, functions, and loops

Systematically Explore data in R

Data Science Package: Dplyr , GGPlot 2

Index, slice, and Subset Data

Get your data in and out of R – CSV, Excel, Database, Web, Text Data

Data Visualization : plot different types of data & draw insights like: Line Chart, Bar Plot, Pie Chart, Histogram, Density Plot, Box Plot, 3D Plot, Mosaic Plot

Data Manipulation – Apply function, mutate(), filter(), arrange (), summarise(), groupby(), date in R

Statistics – A Must have for Data Sciecne

Hypothesis Testing

Have fun with real Life Data Sets

Requirements

No prior knowledge is required to understand for the Data Science & Machine Learning Course

R Software will be used in the course. Installation and use of R will be taught in the course.

All Software and data used in the course are free

Description

Are you planing to build your career in Data Science in This Year?

Do you the the Average Salary of a Data Scientist is $100,000/yr?

Do you know over 10 Million+ New Job will be created for the Data Science Filed in Just Next 3 years??

If you are a Student / a Job Holder/ a Job Seeker then it is the Right time for you to go for Data Science!

Do you Ever Wonder that Data Science is the “Hottest” Job Globally in 2018 – 2019!

>> 30+ Hours Video

>> 4 Capstone Projects

>> 8+ Case Studies

>> 24×7 Support

                >>ENROLL TODAY & GET DATA SCIENCE INTERVIEW PREPARATION COURSE FOR FREE <<

What Projects We are Going to Cover In the Course?

Project 1– Titanic Case Study which is based on Classification Problem.

Project 2 – E-commerce Sale Data Analysis – based on Regression.

Project 3 – Customer Segmentation which is based on Unsupervised learning.

Final Project – Market Basket Analysis – based on Association rule mining

Why Data Science is a MUST HAVE for Now A Days?

The Answer Why Data Science is a Must have for Now a days will take a lot of time to explain. Let’s have a look into the Company name who are using Data Science and Machine Learning. Then You will get the Idea How it BOOST your Salary if you have Depth Knowledge in Data Science & Machine Learning!

What Students Are Saying:

“A great course to kick-start journey in Machine Learning. It gives a clear contextual overview in most areas of Machine Learning . The effort in explaining the intuition of algorithms is especially useful”

– John Doe, Co-Founder, Impressive LLC

I simply love this course and I definitely learned a ton of new concepts.

Nevertheless, I wish there was some real life examples at the end of the course. A few homework problems and solutions would’ve been good enough.

– – Brain Dee, Data Scientist

It was amazing experience. I really liked the course. The way the trainers explained the concepts were too good. The only think which I thought was missing was more of real world datasets and application in the course. Overall it was great experience. The course will really help the beginners to gain knowledge. Cheers to the team

– – Devon Smeeth, Software Developer

Above, we just give you a very few examples why you Should move into Data Science and Test the Hot Demanding Job Market Ever Created!

The Good News is That From this Hands On Data Science and Machine Learning in R course You will Learn All the Knowledge what you need to be a MASTER in Data Science.

Why Data Science is a MUST HAVE for Now A Days?

The Answer Why Data Science is a Must have for Now a days will take a lot of time to explain. Let’s have a look into the Company name who are using Data Science and Machine Learning. Then You will get the Idea How it BOOST your Salary if you have Depth Knowledge in Data Science & Machine Learning!

Here we list a Very Few Companies : –

Google – For Advertise Serving, Advertise Targeting, Self Driving Car, Super Computer, Google Home etc. Google use Data Science + ML + AI to Take Decision

Apple: Apple Use Data Science in different places like: Siri, Face Detection etc

Facebook: Data Science , Machine Learning and AI used in Graph Algorithm for Find a Friend, Photo Tagging, Advertising Targeting, Chat bot, Face Detection etc

NASA: Use Data Science For different Purpose

Microsoft: Amplifying human ingenuity with Data Science

So From the List of the Companies you can Understand all Big Giant to Very Small Startups all are chessing Data Science and Artificial Intelligence and it the Opportunity for You!

Why Choose This Data Science with R Course?

We not only “How” to do it but also Cover “WHY” to do it?

Theory explained by Hands On Example!

30+ Hours Long Data Science Course

100+ Study Materials on Each and Every Topic of Data Science!

Code Templates are Ready to Download! Save a lot of Time

What You Will Learn From The Data Science MASTERCLASS Course:

Learn what is Data science and how Data Science is helping the modern world!

What are the benefits of Data Science , Machine Learning and Artificial Intelligence

Able to Solve Data Science Related Problem with the Help of R Programming

Why R is a Must Have for Data Science , AI and Machine Learning!

Right Guidance of the Path if You want to be a Data Scientist + Data Science Interview Preparation Guide

How to switch career in Data Science?

R Data Structure – Matrix, Array, Data Frame, Factor, List

Work with R’s conditional statements, functions, and loops

Systematically explore data in R

Data Science Package: Dplyr , GGPlot 2

Index, slice, and Subset Data

Get your data in and out of R – CSV, Excel, Database, Web, Text Data

Data Science – Data Visualization : plot different types of data & draw insights like: Line Chart, Bar Plot, Pie Chart, Histogram, Density Plot, Box Plot, 3D Plot, Mosaic Plot

Data Science – Data Manipulation – Apply function, mutate(), filter(), arrange (), summarise(), groupby(), date in R

Statistics – A Must have for Data Science

Data Science – Hypothesis Testing

Business Use Case Understanding

Data Pre-processing 

Supervised Learning

Logistic Regression 

K-NN 

SVM 

Naive Bayes 

Decision Tree 

Random Forest

K-Mean Clustering 

Hierarchical Clustering 

DBScan Clustering 

PCA (Principal Component Analysis)

Association Rule Mining 

Model Deployment 

Who this course is for:

Anyone who is interested in Data Science can take this course.

Aspiring Data Scientists

Anyone who wants to switch his career in Data Science/Analytics/Machine Learning should take this course.

Beginners to any Programming and Interested In the Amazing world of Machine Learning , Artificial Intelligence & Data Science

People interested in Statistics and Data Analysis

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