## Learn Machine Learning Online At Your Own Pace. Start Today and Become an Expert in Days

MlMinds course enables you to master the concepts of Data Science.

#### Skills Covered in Machine Learning Course

● Probability and Statistics

● Visualization techniques

● Machine Learning

#### Jobs Facts on ML or Data Science

“AI is the new electricity!” Electricity transformed countless industries; AI will now do the same. **– Andrew Ng**

Jobs on AI is not limited to one industry. Most of the industries have been extensively using it.

- Banking and Financial Industries
- Healthcare
- E-commerce
- Gaming
- Manufacturing, and many more.

#### Basics of Business Analytics

Business analytics is the practice of iterative, methodical exploration of an organization’s data, with an emphasis on statistical analysis. Business analytics is used by companies committed to data-driven decision-making. It is about using your data to derive information, insights, knowledge, and recommendations. Businesses use business analytics to improve effectiveness and efficiency of their solutions.

In this module, I will talk about how analytics has progressed from simple descriptive analytics to being predictive and prescriptive. I will also talk about multiple examples to understand this better and discuss various industry use cases. I will also introduce multiple components of big data analysis including data mining, machine learning, web mining, natural language processing, social network analysis, and visualization in this module. Lastly, I will provide some tips for learners of data science to succeed in learning and applying data science successfully for their projects.

##### Types of analytics

- Descriptive analytics, predictive analytics, prescriptive analysis

##### Introduction to components of big data analytics

- Brief Introduction about Components of Big Data Analysis
- Introduction to Hadoop and Big Data
- Infrastructure
- Introduction to Data Mining
- Introduction to Machine Learning
- Introduction to Nature Language Processing
- Introduction to Information Retrieval
- Introduction to Web Mining
- Introduction to Social Network Analytics
- Introduction to IOT
- Introduction to Visualization

##### Practical Data Science

1.Lots of open positions available in the industry

2.High paying jobs

3.DataScience makes the product better.

4.Reduces of lot of human work and can create impact on the society

#### Python for data science

Python is an interpreted high-level programming language for general-purpose programming. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace. Python is open source, has awesome community support, is easy to learn, good for quick scripting as well as coding for actual deployments, good for web coding too.

In this module, I will start with basics of the Python language. We will do both theory as well as hands-on exercises intermixed. I will use Jupyter notebooks while doing hands-on. I will also discuss in detail topics like control flow, input output, data structures, functions, regular expressions and object orientation in Python. Closer to data science, I will discuss about popular Python libraries like NumPy, Pandas, SciPy, Matplotlib, Scikit-Learn and NLTK.

##### Basics of the Python language

- Why Python
- Python Installation
- Python 2.7 Vs 3.x
- Introduction to Essential Python Libraries
- Introduction to iPython and Jupyter Notebooks
- Python Language Basics- Indentation, Comments, Function Calls, Variables and Argument Passing
- Python Language Basics-Types, Duck-Typing, Import
- Python Language Basics-Binary operators, Comparisons, Mutable
- Python Language Basics-Standard Data types in Python
- Python Language Basics-Command Line Arguments

##### Python Language Basics-Control Flow

- Loops: for, while
- Conditional Execution

##### Input/Output in Python

- Input, output, Eval, Print
- repr, str, zfill
- File IO
- JSON I/O with Python Dictionary
- JSON I/O with Generic objects
- JSON I/O Serialization and Deserialization
- JSON I/O File
- Introduction to Pickle
- cPickle
- Pickle and Multi-Processing

##### Python Data Structures and Sequences

- Tuples
- List
- Sorting, Searching, Slicing
- Built-In Functions-Enumerate, Sort, Zip, Reversed
- Dictionary
- Sets
- Lists, Sets and Dict Comprehensions

##### Functions

- Introduction to Functions and Variable Length Argument
- Namespace, Scope, Local Funtions, Local vs Global Variables
- Returning multiple vales, Pass by Reference
- Functions are objects
- Recursive functions, Anonymous(Lambda) Functions
- Currying, Generators
- Itertools Module
- Errors and Exception Handling

- Introduction to Functions and Variable Length Argument
- Namespace, Scope, Local Funtions, Local vs Global Variables
- Returning multiple vales, Pass by Reference
- Functions are objects
- Recursive functions, Anonymous(Lambda) Functions
- Currying, Generators
- Itertools Module
- Errors and Exception Handling

##### Object Orientation in Python

- Python Modules and Packages
- object oriented Nature of Python
- Class Inheritance, overriding, overloading, Data Hiding

##### Regular expressions in Python

- Searching for patterns, matching groups
- Regular expression flags
- split, findall, finditer
- Repetition syntax
- Character sets, Exclusion, Character Ranges, Escape Codes
- Substitution
- Greedy vs non-greedy matching
- Backreferences and anchors
- Capturing parts of pattern match
- split and zero-width assertions
- Look-arounds

##### NumPy

- Introduction to Numpy and ndarrays
- Datatypes of ndarrays
- Arithmetic operations, Indexing, Slicing
- Boolean and fancy indexing
- Basic ndarray operations
- Array-oriented programming with arrays
- Conditional, Statistical and Boolean operation
- Sorting and set operation
- File IO with NumPy
- Linear Algebra for Numpy
- Reshaping, Concatenating and Splitting Arrays
- Broadcasting

##### Pandas

- Series Data Structures
- DataFrame
- Index objects
- Reindexing
- Dropping entries from an axis
- Indexing, Selection and Filtering
- Arithmetic and Data Alignment
- Operations between DataFrame and Series
- Function Application and Mapping
- Sorting and Ranking
- Axis indexes with duplicate labels
- Computing Descriptive Statistics
- pct_change(), Correlation and Covariance, Unique values, Value counts and membership

##### Visualization

- Introduction to Matpotlib
- Colours, Markers and line styles
- Customization of Matplotlib
- Plotting with Pandas
- Barplots, Histograms plots, Density Plots
- Introduction to Seaborn, Style Management
- Controlling figure aesthetics
- Colour Palettes
- Plotting univariate Distribution
- Plotting bivariate Distribution
- Visualizing pairwise relationship in pairplots
- Plotting with Categorical Data
- Visualizing Linear Relationships
- Plotting on Data-aware grids
- Other Python Visualization tools

##### SciPy

- Linear Algebra in SciPy
- Sparse Matrices in SciPy
- Constants, Cluster and FFT Packages
- Integration using SciPy
- Interpolation in SciPy
- SciPy I/O, SciPy ndimage
- Optimization and root finding
- SciPy.Stats

##### Scikit learn

- Introduction to SciKit Learn and Machine Learning
- Sample Dataset in SciKit Learn
- Train Test using SciKit Learn
- Classification IRIS using Decision Trees
- Holdout Validation, K-fold cross Validation
- Cross Validation using SciKit Learn
- K-means Clustering in SciKit Learn

##### Basic Text Mining using Python

- Introduction to Nature Language Processing tool kit
- Tokenization, Lower casing and removing stop words, Lemmatization, Stemming
- ngrams, Sentence tokenization, Part of speech tagging
- Chunking, Named Entity Recognition
- Introduction to WordNet, and word sense disambiguation

##### Mini Projects

- Word ladders game Read more
- Data Analysis and Prediction using the Loan Prediction Dataset Read more

#### R for data science

While Python has been used by many programmers even before they were introduced to data science, R has its main focus on statistics, data analysis, and graphical models. R is meant mainly for data science. Just like Python, R has also has very good community support. Python is good for beginners, R is good for experienced data scientists. R provides the most comprehensive statistical analysis packages.

In this module, I will again talk about both theory as well as hands-on about various aspects of R. I will use the R Studio for hands-on. I will discuss basic programming aspects of R as well as visualization using R. Then, I will talk about how to use R for exploratory data analysis, for data wrangling, and for building models on labeled data. Overall, I will cover whatever you need to do good data science using R.

##### Introduction to R

- R Vs Python
- Basics of R
- Data Exploration in R
- Customizations for ggplot in R
- Common Problems, Facets, Geoms
- Statistical Transformation
- Position Adjustments
- Coordinate Systems

##### RStudio Basics

- Introduction to R Studio
- RStudio Editor
- Keyboard shortcuts
- RStudio Diagnostics

##### Data Transformation with dplyr

- Introduction to dplyr
- dplyr-filter
- dplyr-arrange, select
- dplyr-mutate
- dplyr-summarize
- dplyr-Grouping and Ungrouping

##### Exploratory Data Analysis

- Introduction to Exploratory Data Analysis
- Variation
- Covariation

##### Tibbles

- Introduction to Data Wrangling and Tibbles
- Tibbles Vs Data Frames

##### Data Import with readr

- Introduction to Readr and Read csv
- Parsing Vector
- Parsing a file using Readr
- Writing to files

##### Tidy data with tidyr

- Introduction to tidy data
- Spreading and Gathering
- Separating and Unite
- Missing Values

##### Relational Data with dplyr

- Relational Data in Keys
- Mutating joins in dplyr
- Filtering joins and Set operations

##### Strings with stringr

- Introduction to Strings and Combining Strings
- Regular Expressions

##### Factors with forcats

- Creating Factors using forcats
- Visualization and reordering of categorical variables

##### Dates and times with lubridate

- Creating Date/Time objects
- Date/Time Components
- Time Spans

##### Pipes with magrittr

- Details about Pipe operator
- Tools in magrittr

##### Functions

- Functions in R
- Conditional execution and function arguments
- Variable Arguments in R
- Return values in R

##### Vectors

- Basics of vector in R
- Basics of Atomic vectors
- Coercion, Test functions and Recyling rules
- Naming and subset
- Lists
- Augmented vectors

##### Iterations with purrr

- For loop and variations
- Passing functions as an arguments
- Map Functions
- Dealing with failure
- Advanced purrr
- other patterns of for loop

##### Model basics with modelr

- Introduction to modeling
- Building your first simple model in R
- Visualizing models in R
- Modeling with categorical variables
- Modeling with mix of categorical variables

##### Projects

Data Analysis using R: Why Are Low-Quality Diamonds More Expensive?

#### Probability and Statistics

Probability and statistics helps in understanding whether data is meaningful, including inference, testing, and other methods for analyzing patterns in data and using them to predict, understand, and improve results.

We live in an uncertain and complex world, yet we continually have to make decisions in the present with uncertain future outcomes. To study, or not to study? To invest, or not to invest? To marry, or not to marry? This is what is captured mathematically using the notion of probability. Statistics, on the other hand, helps us analyze data sets, and correctly interpret results to make solid evidence-based decisions.

In this module, I will discuss some very fundamental terms/concepts related to probability and statistics that often come across any literature related to Machine Learning and AI. Key topics include quantifying uncertainty with probability, descriptive statistics, point and interval estimation of means, central limit theorem, and the basics of hypothesis testing.

##### Basics of Probability

- Introduction to Probability
- Events, Sample space, Simple Probability, Join Probability
- Mutually Exclusive events collectively exhaustive events marginal probability
- Addition Rule
- Conditional Probability
- Multiplication Rule
- Bayes theorem
- Counting rules caution advanced stuff

##### Probability Distributions

- What are probability distributions
- Poisson Probability Distribution
- Normal Probability Distribution
- Binomial Probability Distribution

##### CLT and Confidence Intervals

- Central Limit Theorem
- CLT Example
- CLT Using R-code
- Confidence Intervals of Mean
- Confidence Intervals of Mean Examples
- Confidence interval of mean in details
- Confidence interval for the mean with population deviation unknow
- Confidence interval using Python
- What do confidence intervals actually mean
- Confidence intervals for pop mean with unknown pop std dev using Python

##### Hypothesis Testing

- what is hypothesis testing? Null and alternative hypothesis
- Hypothesis testing for pop mean type1 and type2 errors
- 1-tailed hypothesis testing (known sigma)
- 2-tailed hypothesis testing (known sigma)
- Hypothesis testing (unknown sigma)
- 2-sample tests
- Independent 2-sample t-tests
- Paired 2-sample t-tests
- Chi-squared tests of independence

##### Measures of Central Tendency and Deviation

- Descriptive Vs Inferential statistics
- Central Tendency (mean, median, mode)
- Measures of dispresion (Range, IQR, std dev, variance)
- Five Number summary and skew
- Graphic displays of basic statistical descriptions
- Correlation Analysis

#### Machine Learning

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.

Machine Learning is a first-class ticket to the most exciting careers in data science. As data sources proliferate along with the computing power to process them, automated predictions have become much more accurate and dependable. Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions.

In this module, broadly I will talk about supervised as well as unsupervised learning. We will talk about multiple types of classifiers like Naïve Bayes, KNN, decision trees, SVMs, artificial neural networks, logistic regression, and ensemble learning. Further, we will also talk about linear regression analysis, sequence labeling using HMMs. As part of unsupervised learning, I will discuss clustering as well as dimensionality reduction. Finally, we will also discuss briefly semi-supervised learning, multi-task learning, architecting ML solutions, and a few ML case studies.

##### Introduction to Machine Learning

- Introduction to machine learning
- Supervised, semisupervised, unsupervised machine learning
- Types of data sets
- Data() in R
- Introduction to classification

##### Decision Trees

- Introduction to Decision tree
- Hunt’s algorithm for learning a decision tree
- Details of tree induction
- GINI index computation
- ID3, Entropy and information gain
- ID3 Example
- C4.5
- Pruning
- Metrics for performance Evaluation
- Iris Decision Tree Example

##### K Nearest Neighbors (KNN)

- Introduction to KNN algorithm
- Decision boundary KNN Vs Decision tree
- What is the best K
- KNN Problems
- Feature selection using KNNs
- Wilson Editing
- KNN Imputation
- Speeding up KNN using KMeans
- Coding up KNN from scratch in Python
- KNN using sklearn
- Digits classification using KNN in Python

##### Naïve Bayes

- Examples of few text classification problems
- Classification for text using bag of words
- Naïve Bayes for text classification
- Multinomial Naïve Bayes
- Multinomial Naïve Bayes Example
- Naïve Bayes for Hand-written digit recognition
- Naïve Bayes for weather data
- Numeric stability issue with Naïve bayes
- Gaussian Naïve Bayes from scratch in Python
- Naïve Bayes using sklearn
- Multinomial Naïve Bayes

##### SVMs

- Linear Classifiers
- Margin of SVM’s
- SVM optimization
- SVM for Data which is not linear separable
- Learning non-linear patterns
- Kernel Trick
- SVM Parameter Tuning
- Handling class imbalance in SVM’s
- SVM’s pros and cons and summary
- Linear SVM using Python
- SVM with RBF kernel with Python
- Learning SVM with noise data in Python

##### Ensemble Learning

- Introduction to Ensemble learning
- Why Ensemble learning
- Independently constructed ensembles for classification: Majority voting
- Independently constructed ensembles for classification: Bagging
- Independently constructed ensembles for classification: Random forests
- Independently constructed ensembles for classification: Error correcting output codes
- Sequentially constructed ensembles for classification boosting
- Sequentially constructed ensembles for classification boosting example
- Sequentially constructed ensembles for classification stacking
- Introduction to gradient boosted machines (GBM)
- Relations between GBM gradient Descent
- GBM regression with squared loss
- Bagging in Python
- Random forests in Python
- Boosting in Python
- Feature importance using ensemble classifiers
- XGBoost in Python
- Parameter tuning for GBM’s
- Voting classifier using skLearn

##### Artificial Neural Networks

- Motivation for Artificial Neural Network
- Mimicing a single neuron, integration function, Activation Function
- Perceptron Algorithm
- Perceptron Algorithm Example
- Decision Boundary for a single Neuron
- Learning Non-Linear Patterns
- Introduction to Deep Learning
- What can we achieve using a single hidden layers
- MLPs with Sigmoid activation Function
- Layers are transformation into a new space
- Playing at the Tensorflow playground
- Cost function, Loss function, Error Surface
- How to learn Weights
- Stochastic Gradient descent, Minibatch SGD, Momentum
- Choosing a learning Rate
- Updaters
- Back Propagation
- Softmax and Binary/Multi-class cross entropy loss
- Overfitting and Regularization
- Practical Advice on using Neural Networks
- Autonomous Vehicles
- Automated Feature Learning using Neural Networks
- Deep Learning Architectures and Libraries
- Applications of Artificial Neural Networks
- History of Artificial Neural Networks and Revival
- Python Code: Basic Introduction to Tensorflow: Constants, Placeholders and Variables.
- Python Code: Learning the first Tensorflow model: Linear Regression using Tensorflow.
- Python Code: MLP for Hand-written digit recognition with no hidden layer with 10 output neurons
- Python Code: MLP for Hand-written digit recognition with two hidden layers
- Python Code: Fashion Multi-class classification using MLP in Keras

##### Linear Regression

- Introduction to Linear Regression
- Understanding the real meaning of Linear Regression
- 𝑹^𝟐: Coefficient of Determination
- Multiple Linear Regression and Non-linear Regression
- Assumptions for Linear Regression
- Using Residual to Verify the Assumptions for Linear Regression
- Deriving Linear Regression Formulas using Ordinary Least Squares Method
- Multiple Linear Regression
- Underfitting, Overfitting, Bias and Variance
- Ridge Regularization
- Lasso Regularization, Elastic Net Regularization
- Metrics and Practical Considerations for Regression
- Python code: Simple Linear Regression using sklearn
- Python code: Example to code up regression using ordinary least squares method
- Python code: Multiple Linear Regression using Gradient Descent based approach
- Python code: Multiple Linear Regression using sklearn
- Python code: Ridge and Lasso Regression

##### Logistic Regression

- Logistic regression vs Linear Regression
- Can we use Regression Mechanism for Classification?
- Logistic Regression – Deriving the Formula
- Logistic Regression for Multi-class Classification
- Logistic Regression Decision Boundary
- Python Code: Logistic regression on the titanic dataset- Part 1
- Python Code: Logistic regression on the titanic dataset- Part 2
- Python Code: Logistic regression on the titanic dataset- Part 3
- Python Code: Logistic regression on the titanic dataset- Part 4
- Python Code: Visualizing a logistic regression model

##### Feature Selection

- What is feature selection? Why feature selection?
- Feature selection vs feature extraction
- Feature subset selection using Filter based methods
- More Filter based methods for feature selection
- Wrapper Methods and their Comparison with Filter Methods
- Wrapper Methods
- Embedded Methods
- Model based machine learning with regularization
- Regularization using L2
- Regularization using L1
- Python Code: Feature Extraction with Univariate Statistical Tests (Chi-squared for classification)
- Python Code: Recursive Feature Elimination — wrapper
- Python Code: Choosing important features (feature importance)
- Python Code: Feature Selection using Variance Threshold
- What is feature selection? Why feature selection?
- Feature selection vs feature extraction
- Feature subset selection using Filter based methods
- More Filter based methods for feature selection
- Wrapper Methods and their Comparison with Filter Methods
- Wrapper Methods
- Embedded Methods
- Model based machine learning with regularization
- Regularization using L2
- Regularization using L1
- Python Code: Feature Extraction with Univariate Statistical Tests (Chi-squared for classification)
- Python Code: Recursive Feature Elimination — wrapper
- Python Code: Choosing important features (feature importance)
- Python Code: Feature Selection using Variance Threshold

##### Sequence Labeling

- Introduction to Sequence Learning
- Sequence Labeling as Classification
- Probabilistic Sequence Models
- Hidden Markov Model
- Details about HMMs
- Dishonest Casino Example of an HMM
- Three Problems of an HMM
- Decoding Problem of an HMM and the Viterbi Algorithm
- Evaluation Problem of an HMM
- The Forward Algorithm
- The Backward Algorithm and the Posterior Decoding
- The Learning Problem of an HMM, The Baum Welch Algorithm
- Conditional Random Fields (CRFs)
- Why prefer CRFs over HMMs?
- Python code: Creating a simple Gaussian HMM
- Python code: Learning a Gaussian HMM
- Python code: Sampling from HMM
- Python Code: Use CoNLL 2002 data to build a NER system: Understand the dataset
- Python Code: Use CoNLL 2002 data to build a NER system: Define features
- Python Code: Use CoNLL 2002 data to build a NER system: Learn and evaluate the CRF
- Python Code: Use CoNLL 2002 data to build a NER system: Hyper-parameter Optimization
- Python Code: Use CoNLL 2002 data to build a NER system: Feature Importances

##### Clustering

- Applications of Clustering
- Understanding Distance
- Basics of Clustering
- Hierarchical (Agglomerative) clustering Part 1
- Hierarchical (Agglomerative) clustering Part 2
- K-means Algorithm example
- K-means Algorithm details
- Problems with K-means
- Evaluation of cluster quality
- Engineering issues with clustering
- Soft clustering and EM algorithm example
- Clustering summary
- Python code: Kmeans Example
- Python code: Kmeans on digits Example
- Python code: Clustering for color compression
- Mini Batch KMeans
- Python code: Agglomerative Hierarchical Clustering
- Ensemble Methods for Clustering: Problem Definition
- Ensemble Methods for Clustering: Image Segmentation
- Ensemble Methods for Clustering: Broad Approach
- Ensemble Methods for Clustering: Finding Corresponding Clusters
- Ensemble Methods for Clustering: Combining Corresponding Clusters

##### Dimensionality Reduction using PCA and LDA

- Why PCA?
- PCA: A Layman’s Introduction
- Understanding Matrix Transformations and Definition of Eigen Vectors
- How is PCA Computed?
- PCA Examples
- Relationship between PCA, Curve Fitting and Entropy
- Eigenfaces in OpenCV
- Kernel PCA
- Python Code: Compute PCA and show components
- Python Code: PCA as dimensionality reduction
- Python Code: PCA for visualization: Hand-written digits
- Python Code: Eigenfaces
- LDA
- PCA vs LDA
- 2 class LDA
- 2 class LDA: Computing within and Between Class Scatter
- 2 class LDA Full Example
- LDA for C classes
- Limitations of LDA
- Python Code: LDA on Wine dataset
- Python Code: LDA from Scikit Learn on Iris dataset
- Python Code: LDA on Iris dataset from scratch

##### Architecting ML solutions

- Machine Learning Process
- Qualities of a Classifier
- Technical Practical Issues in ML
- Non-Technical Practical Issues in ML

##### ML case studies

- Machine Learning for Healthcare – Part 1
- Machine Learning for Healthcare – Part 2
- Machine Learning for Internet Service Providers
- Machine Learning for People Analytics
- Machine Learning for Retail and Telecom – Part 1
- Machine Learning for Retail and Telecom – Part 2
- Machine Learning for Supply Chain Management
- Machine Learning for Agriculture
- Machine Learning for Education
- Machine Learning for Transportation and self-driving cars
- Machine Learning for Connected Cars
- Machine Learning for Legal Domain – Part 1
- Machine Learning for Legal Domain – Part 2
- Machine Learning for Oil Industry
- Machine Learning for Banking Domain – Part 1
- Machine Learning for Banking Domain – Part 2
- Machine Learning for Insurance
- Machine Learning for Project Management
- Machine Learning for Fashion Industry
- Other use-cases of Machine Learning

##### ML Mini Projects

- Learning various classifiers on Iris dataset
- MLP for hand-written digit recognition
- Logistic regression on the titanic dataset
- Use CoNLL 2002 data to build a NER system

#### Manish Gupta

**Instructor**

He is also an Adjunct Faculty at the International Institute of Information Technology, Hyderabad and a visiting faculty at the Indian School of Business, Hyderabad. He received his Masters in Computer Science from IIT Bombay in 2007 and his Ph.D. from the University of Illinois at Urbana-Champaign in 2013.

#### FEE

**INR 15000**

Length: 100+ Hours

Validity: 1 year (365 days)

#### Visualization

For any good data science story, it is very important to visualize it nicely. Visualizations help us understand data and insights much better.

I cover basics of visualization in R and Python in those respective modules. In this module, I will talk about innovative ways of visualizing complex and large data.

##### Basics of Visualization

- Why data visualizations?
- Guidelines for good plots: Part 1
- Guidelines for good plots: Part 2
- Guidelines for good plots: Part 3
- Maintain integrity when plotting data: Avoid misleading graphs
- Web–based visualization libraries
- Data Analysis/Business Intelligence and Visualization Softwares

##### Plotting Large Data

- Plotting pitfalls with large data
- Python Code: Plotting sample of NYC taxi data using bokeh
- Python Code: Interactive Plotting of NYC taxi data using datashader and bokeh
- Python Code: Plotting US Census data using datashader

##### Visualizing Graph Data

- Graph visualization: Why?
- Graph visualization: Challenges
- Graph visualization: Aesthetics
- Graph visualization: Common Layout Algorithms
- Graph visualization: Large graphs
- Introduction to Gephi

##### Customer Churn Prediction

Customers of a big international bank decided to leave the bank. The bank is investigating a very high rate of a customer leaving the bank. The dataset contains 10000 records, and we use it to investigate and predict which of the customers are more likely to leave the bank soon. The approach here is supervised classification; the classification model to be built on historical data and then used to predict the classes for the current customers to identify the churn. The dataset contains 13 features, and also the label column (Exited or not). The best accuracy was obtained with the Naïve Bayes model (83.29%). Such churn prediction models could be very useful for applications such as churn prediction in Telecom sector to identify the customers who are switching from current network, and also for Churn prediction in subscription services.

##### Student Dropout Prediction

Students’ high dropout rate on MOOC platforms has been heavily criticized, and predicting their likelihood of dropout would be useful for maintaining and encouraging students’ learning activities.

In this competition, you are challenged to build a predictor that can predict the chance that a student will drop out of an enrollment after observing his/her early course activities.

In particular, you have access to the statistics of the student’s course-relevant activities during the first 10 days since its launch, such as working on course assignments, watching course videos, accessing the course wiki, etc.

Further, not many students dropout overall but their performance could suffer. The second part of the project concerns predicting student performance in secondary education (high school).

#### Target Audience

The course content and Teaching Methodology is built to cater to the needs of students at various levels of expertise and varied background skills/competencies.

Learn to Excel. You have to put your time and efforts to learn from this course as we teach from the basics and all that you need to have is a very basic knowledge of Programming and a strong determination to LEARN.

Here is a list of aspirants who would benefit from our course:

- Undergraduate (BS/BTech/BE) students in Engineering, Technology and Science.
- Post Graduate (MS/MTech/ME/MCA) students in Engineering, Technology and Science.
- Working Professionals: Software Engineers, Business Analysts, Product & Program Managers, Enthusiasts involved in building ML Products & Services.

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