“Learn Data Science Deep Learning, Machine Learning NLP & R” is a rapidly growing and indemand field that offers promising career opportunities for qualified professionals. To be successful in this field, it is important to go beyond the traditional skills of data analysis, mining, and programming. Data scientists are now in high demand across a variety of industries and are essential assets for companies due to their ability to create sophisticated algorithms that organize and analyze large amounts of data in order to provide answers to complex questions and guide strategic decisionmaking. Data scientists possess a broad range of skills, including the ability to work with big data, programming skills, and a strong quantitative background in statistics and linear algebra. They also have strong leadership and communication skills, which allow them to effectively communicate technical results to nontechnical stakeholders.
According to Glassdoor, being a data scientist was named the best job in America for three consecutive years in 2018. As the need for data science professionals continues to grow, there is a shortage of skilled professionals to fill these roles. This has led to an increasing demand for data scientists in both large and small companies across a variety of industries. Currently, udemy is offering the Learn Data Science Deep Learning, Machine Learning NLP & R course for up to 80 % off i.e. INR 449 (INR 2,299).
Who Can opt for this Course?
 Individuals want to study machine learning
 Anyone interested in learning the Python language
 People interested in learning R
 Individuals seeking to learn data science
 Individuals want to learn deep learning
 Individuals with an interest in artificial intelligence
 Individuals interested in learning natural language processing
Course Highlights
Key Highlights  Details 

Registration Link  Apply Now! 
Price  INR 449 ( 
Duration  70 Hours 
Rating  4.5/5 
Student Enrollment  6,270 students 
Instructor  Cinnamon TechX https://www.linkedin.com/in/cinnamontechx 
Topics Covered 

Course Level  N.A 
Total Student Reviews  1,093 
Learning Outcomes
 Machine learning is based on statistics and mathematics
 Deep Learning is supported by statistics and mathematics
 Artificial intelligence is based on statistics and mathematics
 Python from Scratch: A Programming Language
 Using Python and its libraries
 Study ScikitLearn, Matplotlib, Pandas, and Numpy
 Find out about Natural Language Processing
 Study the R language
 Become familiar with tokenization in NLP
 Learn how to use R libraries and packages on various data sets
 Learn how to implement Python libraries on various data sets
 Models and Algorithms for Machine Learning
 Models and Algorithms for Deep Learning
 Discover data science
 Naive Bayes, kNearest Neighbors, etc
 Learning that is supervised and unsupervised
 Clustering
 various theorems
Course Content
S.No.  Module (Duration)  Topics 

1.  Course Outline (01 minute)  Course Outline 
2.  Installing Software (11 minutes)  How to Download Anaconda 
VERY IMPORTANT NOTE FOR STUDENTS!!  
How to Download R Studio  
3.  Data Science with Python (04 hours 21 minutes)  Introduction to NumPy 
Understanding Data Types in Python  
Fixed Type Arrays in Python  
Creating Arrays from Scratch  
The Basics of NumPy Arrays  
Array Slicing – Accessing Subarrays  
Reshaping of Arrays  
Splitting of Arrays  
Splitting of Arrays – 2  
Exploring NumPy’s UFuncs  
Exponents and Logarithms  
Advanced UFunc Functions  
Aggregations  
Example US Presidents  
Computation on Arrays – Broadcasting  
Broadcasting Example  
Broadcasting in Practice  
Comparisons, Masks and Boolean Logic  
Boolean Operators  
Boolean Arrays as Masks  
Fancy Indexing  
Combined Indexing  
Modifying values with Fancy Indexing  
Example Binning Data  
Sorting Arrays  
Fast Sorting in NumPy  
Example: kNearest Neighbors  
Structured Data – NumPy’s Structured Arrays  
More Advanced Component Types  
4.  Data Manipulation with Pandas (06 hours 44 minutes)  Introduction 
Installing and Using Pandas  
Introducing Pandas Objects  
Constructing Series Objects  
The Pandas DataFrame Object  
DataFrame as a specialized Dictionary  
The Pandas Index Object  
Data Indexing and Selection  
Data Selection in DataFrame  
DataFrame as twodimensional array  
Operating on Data in Pandas  
UFuncs Index Alignment  
Index alignment in DataFrame  
Ufuncs Operations Between DataFrame and Series  
Handling Missing Data  
Missing Data in Pandas  
NaN and None in Pandas  
Operating on Null Values  
Hierarchical Indexing  
The better way Pandas MultiIndex  
Methods of MultiIndex Creation  
MultiIndex for columns  
Indexing and Slicing a MultiIndex  
Rearranging MultiIndices  
Index setting and resetting  
Data Aggregations on MultiIndices  
Combining Datasets Concat and Append  
Prediction Evaluation  
Linear Regressor Training  
Duplicate indices  
Catching the repeats as an error  
Combining Datasets Merge and Join  
Specification of the Merge Key  
Specifying Set Arithmetic for Joins  
Example US States Data  
Example US States Data – 2  
Aggregation and Grouping  
GroupBy Split, Apply, Combine  
Iteration over groups  
Aggregate, filter, transform, apply  
Transformation  
Pivot Tables  
Pivot Table Syntax  
Example Birthrate Data  
Vectorized String Operations  
Methods using regular expressions  
Working with Time Series  
Dates and times in Pandas Best of both worlds  
Pandas Time Series Data Structures  
Example Visualizing Seattle Bicycle Counts  
Example Visualizing Seattle Bicycle Counts – 1  
HighPerformance Pandas eval() and query()  
pandas.eval() for Efficient Operations  
DataFrame.eval() for ColumnWise Operations  
5.  Data Visualization with Matplotlib (05 hours 12 minutes)  Visualization with Matplotlib 
Plotting from an IPython Shell  
Two Interfaces for the Price of one  
Simple Line Plots  
Adjusting the Plot – Axes Limits  
Simple Scatter Plots  
Scatter Plots with plt.scatter  
Basic Errorbars  
Density and Contour Plots  
Histograms, Binnings, and Density  
plt.hexbin – Hexagonal binnings  
Customizing Plot Legends  
Legend for Size of Points  
Multiple Legends  
Customizing Colorbars  
Color limits and extensions  
Example Handwritten Digits  
Multiple Subplots  
plt.subplots The Whole Grid in One Go  
Text and Annotation  
Arrows and Annotation  
Major and Minor Ticks  
Reducing or Increasing the Number of Ticks  
Customizing Matplotlib Configurations and Stylesheets  
Changing the Defaults rcParams  
Stylesheets  
ThreeDimensional Plotting in Matplotlib  
Wireframes and Surface Plots  
Example Visualizing a Möbius strip  
Geographic Data with Basemap  
Map Projections  
Seaborn Versus Matplotlib  
Pair plots  
Bar Plots  
6.  Machine Learning with Python (01 hour 19 minutes)  Machine Learning 
Data Wrangling  
Creating a DataFrame  
Describing the Data  
Selecting Rows based on Conditionals  
Renaming Columns  
Finding the Minimum, Maximum, Sum, Average and Count  
Handling Missing Values  
Deleting a Row & Dropping Duplicate Rows  
Grouping Values by Variables  
Looping over a column  
Concatenating DataFrames  
Merging DataFrames  
7.  Handling Categorical Data (39 minutes)  Encoding Nominal Categorical Data 
Encoding Ordinal Categorical Features  
Encoding Dictionaries of Features  
Imputing Missing Class Values  
Handling Imbalanced Classes  
8.  Handling Missing Values (17 minutes)  Rescaling a Feature 
Normalizing Observations  
9.  Handling Numerical Data (27 minutes)  Transforming Features 
Detecting Outliers  
Handling Outliers  
Discretizating Features  
Grouping Observations using Clustering  
10.  Loading Data (13 minutes)  Loading Data 
11.  Vectors, Matrices and Arrays (46 minutes)  Vectors, Matrices and Arrays 
One or more elements in a matrix  
Finding Minimum and Maximum Values in an Array and Matrix + Vector  
Flattening a Matrix  
Calculating the trace of a Matrix  
Calculating the Dot Products  
12.  Deep Learning (04 hours 24 minutes)  Introduction 
MNIST and Softmax Regression  
Softmax Regression  
Softmax Regression Code Explanation  
Computation Graphs  
Graphs, Sessions, Fetches  
Constructing and Managing Graph & Fetches  
Flowing Tensors  
Data Types, Casting, Tensor Arrays and Shapes  
Matrix Multiplication  
Names  
Variables, Placeholders, and Simple Optimization  
Placeholders  
Optimization  
The Gradient Descent Optimizer  
Gradient Descent in TensorFlow  
Example Linear Regression  
Convolutional Neural Networks  
Convolution, Pooling, Dropout  
The Model  
MNIST Data Set  
Working with TensorfBoard  
Introduction to Recurrent Neural Networks  
MNIST Images as Sequences  
Input and Label Placeholders  
The RNN Step  
Applying the RNN step with tfscan  
Sequential Outputs  
TensorFlow Builtin RNN Functions  
RNN for Text Sequences  
Text Sequences  
contrib.learn  
13.  Natural Language Processing (02 hours 08 minutes)  Computing with Language Texts and Words 
Searching Texts  
Counting Vocabulary  
Lexical_Diversity  
Lexical diversity of various genres in the Brown Corpus  
Lists  
Indexing Lists  
Variables  
Strings  
Computing with Language Simple Statistics  
Frequency Distributions  
FineGrained Selection of Words  
Collocations and Bigrams  
Counting other Things  
Making Decisions and Taking Control  
Operating on Every Element  
Nested Code Blocks  
Looping with Conditionals  
Accessing Text Corposa  
Web and Chat Texts  
Brown Corpus  
Brown Corpus – 2  
Reuters Corpus  
Inaugural Address Corpus  
Conditional Frequency Distribution  
Plotting and Tabulating Distributions  
Generating Random Texts with Bigram  
Function  
14.  Machine Learning with Python  Introductory  (51 minutes)  Essential Libraries and Tools 
A First Application Classifying Iris Species  
Measuring Success Training and Testing Data  
First Things Fast – Look at your Data  
Building your first model kNearest Neighbors  
Making Predictions  
15.  Supervised Learning with Python (02 hours 34 minutes)  Supervised Learning 
load_breast_cancer function from scikitlearn  
Dataset Analysis  
Analyzing KNeighborsClassifier  
Evaluation of Test Performance  
kNeighbors Regression  
Analyzing KNeighborsRegressor  
Linear Models  
Linear regression (aka ordinary least squares)  
Ridge regression  
Boston Housing dataset and evaluated LinearRegression  
Lasso  
Linear Models for Classification  
Decision boundaries of a linear SVM  
Coefficients Learned by the models with the three different settings  
Linear Models for Multiclass Classification  
Predictions for all regions of the 2D  
Strengths, weaknesses, and parameter  
Decision Trees  
16.   R Language  (04 hours 34 minutes)  Using R as a Calculator 
Assignments  
Vector of Values  
Indexing of Vectors  
Vectorized Expressions  
Comments  
Functions  
Writing your own Functions  
Vectorized Expressions and Functions  
Control Structures  
Fucnctor  
Factors  
Factors – 2  
Data Frames  
Dealing with Missing Values  
Installing Packages  
Data Pipelines  
Writing Pipelines of Functional Calls  
Writing Functions that work with the Pipelines  
The Magical “.” Argument  
Defining Functions Using  
Anonymous Functions  
Data Manipulation  
Quickly Reviewing the Data  
Breast Cancer Dataset  
Breast Cancer Dataset – 2  
Breast Cancer Dataset – 3  
Boston Housing Dataset  
The readr Package  
Manipulating Data with dplyr  
Some Useful dplyr Functions  
Select()  
Mutate()  
Transmute()  
Group_by()  
Tidying Data with tidyr  
Visualizing Data  
Longley Data  
Longley Data and Geom_point  
Grammer of Graphics and ggplo2  
qplot  
Using Geometries  
Making Graphs through ggplot  
Using ggplot  
Facets  
Facet_grid  
Scaling  
Using Iris Data Set  
Themes and Other Graphical Transformation  
Iris Dataset  
Figures with Multiple Plots  
Working with Large Dataset  
Running out of Memory  
17.   Python X  (01 hour 48 minutes)  Integer Values 
Variables and Assignment  
Identifiers  
FloatingPoint Types  
Control Codes within Strings  
User Input  
The eval Function  
Controlling the Print Function  
18.  Expressions and Arithmetic (01 hour 45 minutes)  Expressions 
Operator Precedence and Associativity  
Comments  
Errors  
Syntax Errors  
RunTime Errors  
Logic Errors  
Arithmetic Examples  
More Arithmetic Operators  
Algorithms  
19.  Conditional Execution (02 hours 02 minutes)  Boolean Expressions 
Boolean Expressions 2.0  
The Simple if Statement  
The ifelse Statement  
Compound Boolean Expressions  
Nested Conditionals  
Multiway Decision Statements  
Conditional Expressions  
Errors in Conditional Statements  
What is an Exception?  
20.  Iteration (02 hours 08 minutes)  The while statement 
Definite Loops vs Indefinite Loops  
The for statement  
Nested Loops  
Abnormal Loop Termination  
The break statement  
The continue statement  
Infinite Loops  
Iteration Examples  
Computing Square root  
Drawing a Tree  
Printing Prime Number  
Insisting on the Proper Input  
21.  Using Functions (01 hour 16 minutes)  Introduction to Using Functions 
Standard Mathematical Functions  
time Functions  
Random Numbers  
Importing Issues  
22.  Writing Functions (01 hour 51 minutes)  Function Basics 
Using Functions  
Main Function  
Parameter Passing  
Function examples  
Better Organized Prime Generator  
Command Interpreter  
Restricted Input  
Better Die Rolling Simulator  
Tree Drawing Functions  
Floating Point Equality  
Custom Functions vs Standard Functions  
23.  More on Functions (01 hour 06 minutes)  Global Variables 
Default Parameters  
Recursion  
Making Functions Reusable  
Documenting Functions and Modules  
Functions as Data  
24.  Lists (01 hour 49 minutes)  Lists 
Using Lists  
List Assignment and Equivalence  
List Bounds  
Slicing  
Lists and Functions  
Prime Generation with a List  
25.  List Processing (02 hours 01 minutes)  Sorting 
Flexible Sorting  
Search  
Linear Search  
Binary Search  
List Permutations  
Randomly Permuting a List  
Reversing a List  
26.  Objects (24 minutes)  Using Objects 
String Objects  
27.   Python DIY  (24 minutes)  The Background of Software Development 
Software  
Development Tools  
Learning Programming with Python  
Writing a Python Program  
A Longer Python program  
28.  Values and Variables (01 hour 00 minutes)  Values and Variables 
Integer Values  
Variables and Assignment  
Identifiers  
Floatingpoint Types  
Control Codes within Strings  
User Input  
The Eval Function  
Controlling the Print Function  
29.  Expressions and Arithmetic (50 minutes)  Expressions and Arithmetic 
Expressions  
Operator Precedence and Associativity  
Comments  
Errors  
Syntax Errors  
RunTime Errors  
Logic Errors  
Arithmetic Examples  
More Arithmetic Operators  
Algorithms  
30.  Conditional Execution (49 minutes)  Conditional Execution 
Boolean Expressions  
Boolean Expressions 2.0  
The Simple if Statement  
The ifelse Statement  
Compound Boolean Expressions  
Nested Conditionals  
Multiway Decision Statements  
Conditional Expressions  
Errors in Conditional Statements  
31.  Iteration (58 minutes)  Iteration 
The while statement  
Definite Loops vs Indefinite Loops  
The for statement  
Nested Loops  
Abnormal Loop Termination  
The break statement  
The continue statement  
Infinite Loops  
Iteration Examples  
Computing Square root  
Drawing a Tree  
Printing Prime Number  
32.  Using Functions (50 minutes)  Using Functions 
Introduction to Using Functions  
Standard Mathematical Functions  
time Functions  
Random Numbers  
Introduction to Using Functions  
Importing Issues  
33.  Writing Functions (53 minutes)  Writing Functions 
Function Basics  
Using Functions  
Main Function  
Parameter Passing  
Function examples  
Better Organized Prime Generator  
Command Interpreter  
Restricted Input  
Better Die Rolling Simulator  
Tree Drawing Functions  
Floating Point Equality  
Custom Functions vs Standard Functions  
34.  More on Functions (24 minutes)  More on Functions 
Global Variables  
Default Parameters  
Making Functions Reusable  
Documenting Functions and Modules  
Functions as Data  
35.  Lists (46 minutes)  Lists 
Using Lists  
List Assignment and Equivalence  
List Bounds  
Slicing  
Lists and Functions  
Prime Generation with a List  
36.  List Processing (38 minutes)  List Processing 
Sorting  
Flexible Sorting  
Search  
Linear Search  
Binary Search  
List Permutations  
Randomly Permuting a List  
Reversing a List  
37.  Objects (17 minutes)  Objects 
Using Objects  
String Objects  
List Objects  
38.  Custom Types (35 minutes)  Custom Types 
Geometric Points  
Methods  
Custom Type Examples  
Stopwatch  
Automated Testing  
Class Inheritance  
39.  Mathematics and Statistics for Machine Learning (35 minutes)  Introduction 
What is Machine Learning?  
Examples of Machine Learning Applications  
Learning Association  
Classification  
Regression  
Unsupervised Learning  
Reinforcement Learning  
40.  Supervised Learning (01 hour 06 minutes)  Supervised Learning 
Learning a Class from Examples  
VapnikChervonenkis (VC) Dimension  
Probably Approximately Correct (PAC) Learning  
Noise  
Learning Multiple Classes  
Regression  
Model Selection and Generalization  
Dimensions of a Supervised Machine Learning Algorithm  
41.  Bayesian Decision Theory (34 minutes)  Bayesian Decision Theory 
Introduction  
Classification  
Losses and Risks  
Discriminant Functions  
Utility Theory  
Association Rules  
42.  Parametric Methods (01 hour 04 minutes)  Parametric Methods 
Introduction  
Maximum Likelihood Estimation  
Bernoulli Density  
Multinomial Density  
Gaussian (Normal) Density  
Evaluating an Estimator  
The Bayes’ Estimator  
Parametric Classification  
Regression  
Tuning Model Complexity  
Model Selection Procedures  
43.  Dimensionality Reduction (01 hour 10 minutes)  Dimensionality Reduction 
Introduction  
Subset Selection  
Principal Components Analysis  
Factor Analysis  
Multidimensional Scaling  
Linear Discriminant Analysis  
Locally Linear Embedding  
44.  Clustering (42 minutes)  Clustering 
Introduction  
Mixture Densities  
kMeans Clustering  
ExpectationMaximization Algorithm  
Mixtures of Latent Variable Models  
Supervised Learning after Clustering  
Hierarchical Clustering  
Choosing the Number of Clusters  
45.  Nonparametric Methods (29 minutes)  Nonparametric Methods 
Introduction  
Nonparametric Density Estimation  
Histogram Estimator  
Kernel Estimator  
kNearest Neighbor Estimator  
Generalization to Multivariate Data  
Nonparametric Classification  
Condensed Nearest Neighbor  
Nonparametric Regression  
Running Mean Smoother  
Kernel Smoother  
Running Line Smoother  
How to Choose the Smoothing Parameter  
46.  Decision Trees (41 minutes)  Decision Trees 
Introduction  
Univariate Trees  
Classification Trees  
Regression Trees  
Pruning  
Rule Extraction from Trees  
Learning Rules from Data  
Multivariate Trees  
47.  Linear Discrimination (40 minutes)  Linear Discrimination 
Introduction  
Generalizing the Linear Model  
Geometry of the Linear Discriminant  
Two Classes  
Multiple Classes  
Pairwise Separation  
Parametric Discrimination Revisited  
Gradient Descent  
Logistic Discrimination  
Two Classes  
Multiple Classes  
Discrimination by Regression  
48.  Multilayer Perceptrons (01 hour 24 minutes)  Multilayer Perceptrons 
Introduction  
Understanding the Brain  
Neural Networks as a Paradigm for Parallel Processing  
The Perceptron  
Training a Perceptron  
Learning Boolean Functions  
Multilayer Perceptrons  
MLP as a Universal Approximator  
Backpropagation Algorithm  
Nonlinear Regression  
TwoClass Discrimination  
Multiclass Discrimination  
Multiple Hidden Layers  
Training Procedures  
Improving Convergence  
Over training  
Structuring the Network  
Hints  
Tuning the Network Size  
Bayesian View of Learning  
Dimensionality Reduction  
Learning Time  
Time Delay Neural Networks  
Recurrent Networks  
49.  Local Models (50 minutes)  Local Models 
Introduction  
Competitive Learning  
Online kMeans  
Adaptive Resonance Theory  
SelfOrganizing Maps  
Radial Basis Functions  
Incorporating RuleBased Knowledge  
Normalized Basis Functions  
Competitive Basis Functions  
Learning Vector Quantization  
Cooperative Experts  
Competitive Experts  
50.  Hidden Markov Models (43 minutes)  Hidden Markov Models 
Introduction  
Discrete Markov Processes  
Hidden Markov Models  
Three Basic Problems of HMMs  
Evaluation Problem  
Finding the State Sequence  
Learning Model Parameters  
Continuous Observations  
The HMM with Input  
Model Selection in HMM  
51.  Combining Multiple Learners (55 minutes)  Combining Multiple Learners 
Rationale  
Generating Diverse Learners  
Model Combination Schemes  
Voting  
ErrorCorrecting Output Codes  
Bagging  
Boosting  
Mixture of Experts Revisited  
Stacked Generalization  
FineTuning an Ensemble  
Cascading 
Resources Required
 Just a desire to learn
Featured Review
Venkata Sai Bhanu Shouri Kanduri (5/5): Perfect syllabus for getting insights into data science and machine learning
Pros
 Vikas Singh (5/5) : Excellent course for everyone to increase their knowledge of data science
 Siddiqui Rahil (5/5) : If you are looking for a course on data science ml and dl then this is the best course
 Suresh Krishna (5/5) : Best content to learn data science…In this course, they will teach you the basics.
 Raghuraj Muni (5/5) : It’s really awesome course they teach you all that to require to go handson
Cons
 Abhishek Kumar (1/5): You will not get anything from this course other than a certificate.
Negative Review
Qm (1/5): A badly executed course. That you will regret purchasing. i )cut and paste of notes, that are just simply read out verbatim….. why was this? There are much better courses on this site, this is not worth 70 hrs of anyone’s time. The preview vids are misleading, you assume this is an organised learning experience, sadly it is not. Sadly I was unable to review/return this within the return period.
About the Author
The instructor of this course is Cinnamon TechX who is providing breakthrough learning. With 4.3 Instructor Rating and 1,836 Reviews on Udemy, he/she offers 2 Courses and has taught 13,690 Students so far.
 Cinnamon TechX is a startup elearning company that wants to teach people complex ideas from scratch
 They provide realtime projects using realworld data, which aids in skill development through onsite, individualized training
 More online courses are being developed, and books are also being published
Comparison Table
Parameters  Learn Data Science Deep Learning, Machine Learning NLP & R  Mathematical Foundations of Machine Learning  Mathematics & Statistics of Machine Learning & Data Science 

Offers  INR 455 (  INR 455 (  INR 455 ( 
Duration  70.5 hours  16.5 hours  11 hours 
Rating  4.5 /5  4.6 /5  4.1 /5 
Student Enrollments  6,270  102,492  8,540 
Instructors  Cinnamon TechX  Dr Jon Krohn  Cinnamon TechX 
Register Here  Apply Now!  Apply Now!  Apply Now! 
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