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"Data Science and Machine Learning Using Python and R" is no longer available

1 Reviews

Course type: Online Instructor led Course

Platform: Zoom

Course ID: 45392

Course type: Online Instructor led Course

Platform: Zoom

1 Reviews

About the Course

What is this course about ?

Equip students to use Python and R for performing the different statistical data analysis and visualization tasks both prior to and after data modelling.

Introduce some of the most important machine learning concepts to students in the most practical way such that the students can apply these concepts for practical data analysis and interpretation.

Students will get a strong background in some of the most important statistical modelling, machine learning,deep learning concepts along with basic exploratory data analysis (grouping visualization) skills.

Students will work with real life data - data from different sources and get acquainted with the modalities of working with actual data.

The syllabus for the entire course is given below :

Python for Data Science:-

  1. Introduction to Python Data Science tool

  2. Introduction to Python Data Science environment

  3. IPython Interpreter

  4. Different types of data used in Statically and Machine Learning analysis

  5. Different types of data used programmatically

  6. Python Data science packages to be used

  7. Numpy Introduction

  8. Create Numpy Arrays

  9. Numpy operations

  10. Matrix Arithmetic and Linear systems

  11. Numpy for basic Vector Arithmetic

  12. Numpy for Basic Matrix Arithmetic

  13. Broadcasting with NumPy

  14. Solve equations with Numpy

  15. NumPy for statistical Operations

  16. Data structures in Python

  17. Read in Data

  18. Read in CSV data using Pandas

  19. Read in excel data using Pandas

  20. Read in JSON data

  21. Read in HTML data

  22. Removing NAs/No Value from our data

  23. Basic Data Handling:Starting with conditional Data selection

  24. Drop Column/Row

  25. Subset and Index data

  26. Basic data grouping based on Qualitative attributes

  27. Cross tabulations

  28. Reshaping

  29. Pivoting

  30. Rank and sort data

  31. Concatenate

  32. Merging and joining Data Frames

  33. What is Data Visualization

  34. Some theoretical concepts behind data visualizations

  35. Histograms â?? Visualize the Distribution of Continuous Numerical Variables

  36. Boxplots - â?? Visualize the Distribution of Continuous Numerical Variables

  37. Scatter Plot-Visualize the Relationship between 2 continuous Variables

  38. Barplot

  39. Pie Chart

  40. Line Chart


  1. Applications of Machine Learning

  2. Why Machine Learning is the future

  3. Installing Python and Anaconda

  4. Installing R and R studio

  5. Data Pre-processing

  6. Regression

  7. Linear Regression in Python

  8. Linear Regression in R

  9. Single Linear Regression

  10. Multiple Linear Regression in Python

  11. Multiple Regression in R

  12. What is the P-Value?

  13. Polynomial Regression

  14. Polynomial Regression in Python

  15. Polynomial Regression in R

  16. R Regression Template

  17. Support Vector Regression (SVR)

  18. SVR in Python

  19. SVR in R

  20. Decision Tree Regression

  21. Decision Tree Regression in Python

  22. Decision Tree Regression in R

  23. Random Forest Regression

  24. Random Forest Regression in Python

  25. Random Forest Regression in R

  26. Evaluating Regression Model Performance

  27. R-Squared intuition

  28. Adjusted R-Squared Intuition

  29. Interpreting Linear Regression Coefficients

  30. Logistic Regression

  31. Logistic Regression in Python

  32. Logistic Regression in R

  33. K-Nearest Neighbours (K-NN)

  34. K-NN in Python

  35. K-NN in R

  36. Support Vector Machine (SVM)

  37. SVM in Python

  38. SVM in R

  39. Kernel SVM

  40. Kernel SVM Intuition

  41. Mapping to a higher dimension

  42. The Kernel Trick

  43. Types of Kernel Functions

  44. Kernel SVM in Python

  45. Kernel SVM in R

  46. Bayes Theorem

  47. Naive Bayes Intuition

  48. Naive Bayes In Python

  49. Naïve Bayes in R

  50. Decision Tree Classification in Python

  51. Decision Tree Classification in R

  52. Random Forest Classification

  53. Random Forest Classification in Python

  54. Random Forest Classification in R

  55. Evaluating Positives and False Negatives

  56. Confusion Matrix

  57. Accuracy Paradox

  58. Cap Curve

  59. Cap Curve Analysis

  60. K-Means Clustering

  61. K-Means Random Initialization Trap

  62. K-Means Selecting the number of Clusters

  63. K-Means Clustering in Python

  64. K-Means Clustering in R

  65. Hierarchical Clustering

  66. Hierarchical Clustering using Dendograms

  67. Hierarchical Clustering in Python

  68. Hierarchical Clustering in R

  69. Apriori

  70. Apriori in Python

  71. Apriori in R

  72. Eclat in R

  73. Upper Confidence Bound

  74. The Multi Armed Bandit problem

  75. Upper Confidence Bound in Python

  76. Upper Confidence Bound in R

  77. Thomson Sampling

  78. Thomson Sampling in Python

  79. Thomson Sampling in R

  80. Natural Language Processing

  81. Natural Language Processing in Python

  82. Natural Language Processing in R

  83. Deep Learning

  84. Activation Neural Networks

  85. The Neuron

  86. The Activation Framework

  87. How do Neural Networks work?

  88. How do Neural Networks learn?

  89. Gradient Descent

  90. Stochastic Gradient Descent

  91. Backpropagation

  92. ANN in Python â?? Installing Theano,Tensorflow and Keras

  93. ANN in Python

  94. ANN in R

  95. Convolutional Neural Networks

  96. What are convolutional neural networks

  97. Convolution operation

  98. ReLU Layer

  99. Pooling

  100. Flattening

  101. Full Connection

  102. Softmax and Cross-Entropy

  103. CNN in Python

  104. CNN in R

  105. Dimensionality Reduction

  106. Principal Component Analysis (PCA)

  107. PCA in Python

  108. PCA in R

  109. Linear Discriminant Analysis (LDA)

  110. LDA in Python

  111. LDA in R

  112. Kernel PCA

  113. Kernel PCA in Python

  114. Kernel PCA in R

  115. Model Selection and Boosting

  116. K-fold Cross Validation in Python

  117. K-fold cross validation in R

  118. Grid Search in Python

  119. Grid Search in R

  120. XG Boost

  121. XG Boost in Python

  122. XG Boost in R

Date and Time

Not decided yet.

About the Trainer

Biswanath Banerjee picture

3 Avg Rating

8 Reviews

15 Students

3 Courses

Biswanath Banerjee

BTech IIT Roorkee

20 Years of Experience

Data Scientist with 20 years of technology experience in leading IT Multinationals like IBM, British Telecom, AT&T in India and abroad.
Conduct Corporate training on Python, Data Science, Machine Learning, Artificial Intelligence and Deep Learning.
Achievement - Recently conducted a 12 weeks (480 hours ) of Data Science , Machine Learning and Deep Learning with Python Corporate training at Leeds in United Kingdom for a well known Software services company in UK.
Also conducted Data Science training at different corporates in India and abroad.
Conducts online and class room training for Engineering graduates and working professional.
My strengths - hands on learning through lots of practical examples tough subjects like Machine Learning, Data Science algorithms, Deep Learning , Artificial Intelligence using Python libraries.
Join my demo class to know me more. Most of my students are well placed in TCS, IBM, ESPN etc as Data Scientists.

Student Feedback


Average Rating





He is the worst teacher I have meet. Also cheater. First of all he does not know python basic coding like simple interest function.


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