Program Overview

Learn Patterns from Data. Handling Structured & Unstructured Data Sets. It's easy to learn and use Python. This Data Science Course will help you clear the basic fundamentals & introduce Data Structure, Control flow, Functions, and Advance Concepts. This Online Program will teach simple to advanced skills to work on Complex Data. Watch live project demonstrations, and watch ‘Expert Talks’ to achieve operational excellence in projects and meet your organization's business objectives.

150+ Hours of Learning

100+ Practical Assignments & Projects

Dedicated Student Success Mentor

Career Mentoring Session

Virtual Work Experience & Internship Opportunities

Placement Support

Top skills you will learn?

Understanding Mathematical Models will help in capturing information from data. This course will help students in understanding fundamental concepts about supervised & unsupervised learning Algorithms. Developing Skills to use Python Libraries such as Numpy, Keras, Sklearn, Matplotlib & many such libraries.

Career Opportunities

Python Developer, ML Engineer, Data Scientist, Data Analyst etc

Who can join the program?

Freshers, Students doing B.E. / BTech, BSc, MSc, MTech, BCA, MCA, BCom, Development Enthusiast, Working Professionals

Minimum Eligibility

Should know fundamentals of Computer Science

Success Journey With Ekeeda Pro

Ekeeda Pro Roadmap

Course Curriculum

Best-in-class content by leading faculty and industry leaders in the form of videos,
cases and projects, assignments and live sessions

1. Introduction to Python, Python and anaconda Setup

2. Basic data types of Python

3. Input and Output functions of Python

4. Operators in Python

5. Math Operators

6. Comparison Operators

7. Logical operators

8. Control statement if else , for loop, while loop

9. List, Tuple, Set and dictionary data type

10. Functions in Python

11. Defining simple function

12. Understanding scope of variables

13. Variable number of arguments

14. Positional arguments

15. Recursive functions

16. Lambda function, Map, Filter, List comprehension

17. Back to Functions

18. Nested functions (Inner functions)

19. Understanding locals() and globals()

20. Creating and using Modules

21. Understanding packages

22. Classes and Objects

23. Operator Overloading

24. Type conversion

25. Containership and Inheritance

26. Iterators and Generators

27. Exception Handling

28. File Input/Output

Industry oriented Case Studies

1. Email Slicer : The email slicer is a handy program to get the username and domain name from an email address. You can customize and send a message to the user with this information.

2. Desktop Notifier App in Python: A desktop notifier app runs on your system and it will be used to send you notifications after every specific interval of time.

Industry oriented Case Studies

3. Python Website blocker : Block the unwanted sites. This project will have a simple interface which allows user to block unwanted sites

4. Currency converter :Convert currency using latest conversion rates. The currency value and conversion rate will be auto searched by our application

5. Web Crawler : Get the required data in the form text , images , videos and other formats from various web sites.

6. Music Player in Python : Design a dedicated music player which can play audio files of various formats. The application will have various controls such as fast forward, pause, stop etc

7. Random Password generator : This application will help user to generate random password. Such a password will be very difficult to crack.

8. Convert Text to Speech in Python : Convert your text into voice with Python and Google APIs. Text to speech project takes words as input on digital devices and converts them into audio or speech with a button click or finger touch.

9. YouTube Video downloader : Another interesting project is to make a nice interface through which you can download youtube videos in different formats and video quality.

10. Language translator in Python : Instantly translate texts, words, paragraphs from one language to another. The objective of this project is to translate text content from one language to any other language in real-time with a button click.

29. Numpy Introduction

30. Numerical operations using Numpy

31. MatPlotLib Introduction

32. Plotting various types of graphs such as scatter plot, line plot, histogram etc

33. Pandas Introduction

34. Handling datasets and processing data

35. Understanding Space and time complexity

36. Finding largest and smallest element in a list

37. Searching a value using linear search and binary seach

38. Merging two sorted lists

39. Introduction to Iris dataset

40. 2D scatter plots

41. 3D scatter plots

42. Pair Plots

43. Histograms and Probability density function (PDF)

44. Univariate Analysis using PDF

45. Mean, Median, Variance and standard deviation

46. Cumulative distribution function (CDF)

47. Percentiles and Quantiles

48. Box Plots with whiskers

49. Violin Plots

50. Introduction to Vectors

51. 1D, 2D and nD vectors

52. Row vector and Column vector

53. Dot product of two vectors

54. Projection of vector

55. Unit vector

56. Equation of a line, 2D plane and HyperPlane

57. Distance of a point from hyperplane

58. Equation of circle, sphere and Hypersphere

59. Equation of ellipse, ellipsoid and hyperellipsoid

60. Square, Rectangle, Cube and HyperCube

61. Probability and Statistics

62. Population and Sample

63. Gaussian Normal distribution and PDF

64. Gaussian Normal distribution and CDF

65. Symmetric distribution and skewness

66. Standard normal variate and standardization

67. Kernel density estimation

68. Sampling distribution and Central limit theorem

69. Q-Q plot

70. Various distributions and their use

71. Chebyshev's inequality

72. Discrete and Uniform distribution

73. Probability and Statistics contd.

74. Bernoulli and Binomial distribution

75. Log Normal distribution

76. Power law distribution

77. Box Cox Transform

78. Application of non Gaussian distributions

79. Co-Variance

80. Pearson Correlation Coefficient

81. Spearman rank correlation coefficient

82. Correlation Vs Causation

83. Use of correlations

84. Probability and Statistics contd..

85. Introduction of confidence Interval

86. Computation of confidence interval

87. Hypothesis Testing

88. Resampling and Permutation Test

89. K-S test for similarity of two distributions

90. Proportional Sampling

91. Basics of Data Sets

92. Introduction to dimensionality reduction

93. Row and Column Vector

94. Representation of Data Set

95. Representing Data Set as a matrix

96. Data Preprocessing

97. Mean of data matrix

98. Column standardization

99. Covariance of data matrix

100. MNIST Data set

101. PCA (Principle Component Analysis) for dimensionality reduction

102. Limitations of PCA

103. t-SNE for dimensionality reduction

104. Preprocessing DataSet

105. Data Cleaning

106. Convert text to vector

107. Bag of words

108. Stemming

109. tf-IDF

110. Word2Vec

111. Classification in Machine learning

112. K-Nearest Neighbour

113. Time and space complexity of K-Nearest neighbour

114. Factors affecting classification algorithms

115. Balanced Vs Imbalanced Datasets

116. Impact of outliers

117. Space and Run time complexity

118. K distance

119. Multiclass classification

120. Time and space complexity of K-Nearest neighbour

121. Feature Importance

122. Handling categorical and numerical features

123. Handling missing values

124. Curse of dimensionality

125. Bias-Variance tradeoff

126. Accuracy measure of classification algorithm

127. Accuracy

128. Confusion matrix

129. ROC and AUC curve

130. Log-Loss

131. R-squared coefficient of determination

132. Median absolute deviation (MAD)

133. Naive Bayes algorithm for classification

134. Logistic Regression

135. Linear Regression

136. Gradient descent algorithm

137. Support Vector Machine

138. Decision tree algorithm for classification

139. Ensembles

140. Random Forest

141. Gradient boosting

142. XGboot and ADAboost

143. Feature Engineering

144. Moving window for time series

145. Fourier decomposition

146. Image histogram

147. Relational data

148. Graph data

149. Feature binning

150. Feature slicing

151. Clustering algorithms

152. K means algorithm

153. K medoid algorithm

154. Agglomerative clustering

155. Density based clustering (DBSCAN)

Hands-on Project

1. Project Title: Taxi Demand in your City

The end user of this application is a Taxi driver. Taxi driver will be informed about the expected number of pickups from a given region in next 10 minutes

2. Project title : Cancer diagnosis

Classify the given genetic mutations/variations on the evidence of text based clinical literature

Projects for Self study

1. Iris flower classification : The iris flowers have different species and you can distinguish them based on the length of petals and sepals.This is a basic project for machine learning beginners to predict the species of a new iris flower.

2. Loan Prediction using Machine Learning : The idea behind this ML project is to build a model that will classify how much loan the user can take.

3. Housing Prices Prediction Project :The dataset has house prices of the Boston residual areas. The expense of the house varies according to various factors like crime rate, number of rooms, etc. fun project to build as you will be.

4.Titanic Survival Project : This will be a predicting whether someone would have survived if they were in the titanic ship or not. Learn how to distinguish fake news from a real one.

156. Neural network and Deep Learning

155. History of neural network and comparison with biological neuron

157. Multilayer perceptron

158. Training a single layer model

159. Training MLP model

160. Back Propagation

161. Activation function

162. Vanishing gradient problem

163. Deep layer perceptron

164. Drop Outs

165. Regularization

166. RELU

167. OptimizerHill-descent 2D

168. OptimerHill-descent 3D

169. SGD

170. Adam optimizer algorithm

171. Softmax for multiclass classification

172. Tensor Flow and Keras

173. GPU vs CPU

174. Google collaboratory

175. Convolutional Networks

176. Understanding Visual cortex

177. Edge detection in images

178. Padding and strides

179. Convolutional layer

180. Max Pooling

181. ImageNet data sets

182. AlexNet

183. VGGNet

184. Mini Project : Cats Vs Dogs

185. Given a image of an animal identify whether it is image of Dog OR Cat OR None

186. Recurrent neural Networks

187. Training RNN model by backpropogation

188. Types of RNN

189. LSTM

190. Deep RNN

191. Bidirectional RNN

Projects for self study

1. MNIST Digit Classification Machine Learning Project : The MNIST digit classification python project enables machines to recognize handwritten digits.This project could be very useful for computer vision.

2. Emojify : Create your own emoji with Python The objective of this machine learning project is to classify human facial expressions and map them to emojis.

3. Cartoonify Image with Machine Learning : Transform images into its cartoon. Yes, the objective of this machine learning project is to CARTOONIFY the images.

Thus, you will build a python application that will transform an image into its cartoon using machine learning libraries.

Instructors & Mentors

Career Mentoring Session | Live Doubt Session | Hands-on Training Sessions

Industry Projects and Case Studies

Learn through real-life industry projects.

Get Hands-on coding practice

Develop projects and applications

Get mentored by industry experts

Python Certificate

Course Completion Certificate

You will be awarded a Course Completion Certificate only if you pass with a minimum grade of 60% and a Certificate of Excellence if you secure 90% and above.

Jobs & Virtual Internships

Get Virtual Work experience programs in big Tech companies and showcase your talent

Career Support

Strong hand-holding with dedicated support to help you master.

Ekeeda Internship


Join Internship program with companies to gain complete insights into Python - the perfect programming language. Get guidance from industry experts, and top graders on live projects and case studies.

Ekeeda Community Access

Community Access

Community that connects you with the best Pythoniasts across the globe! All your doubts will be cleared live with industry experts. Aim to grow your knowledge and skills with the Python Community now!

Ekeeda Placement

Placement Support

Dedicated mentorship and intensive career support for your career growth. Prepare for interviews & interact with industry experts at career events. Help you find the perfect career opportunity!