“Learn Data Science Deep Learning, Machine Learning NLP & R” is a rapidly growing and in-demand 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 decision-making. 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 non-technical 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 Scikit-Learn, 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, k-Nearest 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: k-Nearest 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 two-dimensional 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 Multi-Indices | ||
| Index setting and resetting | ||
| Data Aggregations on Multi-Indices | ||
| 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 | ||
| High-Performance Pandas eval() and query() | ||
| pandas.eval() for Efficient Operations | ||
| DataFrame.eval() for Column-Wise 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 Color-bars | ||
| 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 | ||
| Three-Dimensional 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 Built-in 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 | ||
| Fine-Grained 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 k-Nearest Neighbors | ||
| Making Predictions | ||
| 15. | Supervised Learning with Python (02 hours 34 minutes) | Supervised Learning |
| load_breast_cancer function from scikit-learn | ||
| Dataset Analysis | ||
| Analyzing KNeighborsClassifier | ||
| Evaluation of Test Performance | ||
| k-Neighbors 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 | ||
| Floating-Point 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 | ||
| Run-Time 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 if-else 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 | ||
| Floating-point 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 | ||
| Run-Time 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 if-else 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 | ||
| Vapnik-Chervonenkis (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 | ||
| k-Means Clustering | ||
| Expectation-Maximization 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 | ||
| k-Nearest 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 | ||
| Two-Class 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 k-Means | ||
| Adaptive Resonance Theory | ||
| Self-Organizing Maps | ||
| Radial Basis Functions | ||
| Incorporating Rule-Based 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 | ||
| Error-Correcting Output Codes | ||
| Bagging | ||
| Boosting | ||
| Mixture of Experts Revisited | ||
| Stacked Generalization | ||
| Fine-Tuning 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 hands-on
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 e-learning company that wants to teach people complex ideas from scratch
- They provide real-time projects using real-world data, which aids in skill development through on-site, 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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