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.
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.
Python Developer, ML Engineer, Data Scientist, Data Analyst etc
Freshers, Students doing B.E. / BTech, BSc, MSc, MTech, BCA, MCA, BCom, Development Enthusiast, Working Professionals
Should know fundamentals of Computer Science
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.
Career Mentoring Session | Live Doubt Session | Hands-on Training Sessions
Professor Sameer Velankar has proficiency in the Computer Science field for over 25 years & taught more than 40000 students. He has mastered subjects like Data Structures,Algorithms, Automata theory, Database management. He has also been teaching Computer organization and architecture with Java programming. His excellence in subjects like Data Science,Python programming, Web programming, and Machine Learning has helped various students to achieve their learning goals. He focuses on interactive and communicative learning. He quotes by saying "Teacher does one percentage of teaching but gives out 99 percent of motivation and inspiration to all students." Currently he runs his own Consultancy where he has undertaken Government/Private Sector IT Projects. He has set an idle example of a thorough Professional teacher, through his innovative & effective way of training to students.
Professor Rahul Shelke believes that teaching means to give and take as it is a life long learning process where the teacher learns new strategies, ideas, methodologies, and philosophy each passing day. With over five years of teaching experience, he has attained his M.tech degree in Computer Engineering. This makes python, data science, machine learning, python flask framework, python Django the framework, cloud computing concepts, AWS concepts and automation using python, few of his primary subjects of teaching. Currently, he is working for Praemineo, Inc with a duty role of designing and developing software systems, algorithms for improving efficiency of the system. Few of his achievements include reviewing a technical book of two Python Flask frameworks published by Packt Publishing. He is also an alumni of Veermata Jijabai Technological Institute (VJTI), Matunga. His highly analytical skill makes him understand his students in an effortless way, and guide them accordingly in the right direction.
Learn through real-life industry projects.
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 the next 10 minutes.
Classify the given genetic mutations/variations on the evidence of text based clinical literature.
Using Housing Data Set this project will predict what will be the prize of a house depending on Location & Other factors within the City.
The Project will capture images of human faces from the camera and it will predict emotions like Happy, Sad, Angry and so on. This Project is very important for security systems.
Software emulator for self-driving cars. Understanding the challenges in building self driving cars and improving efficiency by reducing risks and also improving throughput (such as saving fuel)
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.
A desktop notifier app runs on your system and it will be used to send you notifications after every specific interval of time.
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.
Another interesting project is to make a nice interface through which you can download youtube videos in different formats and video quality.
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.
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.
Get Virtual Work experience programs in big Tech companies and showcase your talent
Strong hand-holding with dedicated support to help you master.
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.
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!
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!
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