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Join the certified courses from Prutor.AI, IIT Kanpur this Summer, and get the opportunity to utilize your summer vacation by learning new technology at the comfort of your home.
This online live training program is designed in such a way that it makes learning by building projects super easy for you.
Certificate You Will Get
For applying for a job AI with Data Science and Python certificate is considered one of the most needed certifications.
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Training Highlights
Learn from home
Stay safe indoors
Certificate of Training
From Prutor.ai
Online Live Classes
Learn anywhere
Faculty Support
Throughout the training
3 months of training
Duration
Doubt clearing
During the live session
Syllabus
The comprehensive syllabus of AI with Data Science and Python
Project Work
You can build
Beginner friendly
No prior knowledge required
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Join any time and start learning
Price: @Rs. 30,000 + 18% GST
Enroll NowHow will you get your certificate?
IIT Kanpur was the first institute in India to start a Computer Science Department.
IIT Kanpur has a rich base of alumni in this space who have made a remarkable impact around the world (Dr. Arvind Krishnan, CEO IBM, Dr. Rajeev Motwani, Google mentor, Dr. Narayan Murthy, Founder Infosys, Mr. Amit Agarwal, CTO Amazon to name few)
While education is impacted, some of the institutions continue to do well as they have realized the importance of practice. There is no substitution to practice. While other jobs are impacted due to COVID 19 coding jobs are still in demand.
To solve the problem of teaching introductory programming to Engineering students, Dr. Amey Karkare, Computer Science Department at IIT Kanpur has developed software - Prutor, that is being used at IIT Bombay, IIT Goa, IIT Kanpur, IISC Bangalore, IISER Bhopal, and EICT Academy IIT Kanpur to teach programming to more than 40,000+ students.
We believe in the practical and industry-based approach of teaching at Prutor.ai
Certificate
Why Learn AI with Data Science and Python?
Learn from Experts at IIT Kanpur
Live Classes over Zoom
Python is simple & easy to learn
The End of Menial Tasks
Increased Sales Opportunities
How will your training work?
1
Attend Online Classes
2
Learn with Hands-on practical
3
Do the project work
4
Give the final quiz
5
Get a certificate in AI with Data Science and Python
AI with Data Science and Python (Mixed Language)
Online Live Training with Faculty Support
Key Features
- Lectures in Mixed Language
- Slides in English
- Training: 6 weeks of learning
- Price: @Rs. 30,000 + 18% GST
COURSE CURRICULUM - Month 1 (Python)
- Introduction
- The Programming Cycle for Python
- Getting started
- Variables and simple data types
- Elements of Python
- Type Conversion
- Expressions
- Assignment Statement
- Arithmetic Operators
- Operator Precedence
- Boolean Expression
- Introducing list
- Working with lists
- For Loop
- Nested Loops
- Nested Loops
- Tuples
- Unpacking Sequence
- Lists
- Mutable Sequences
- List Comprehension
- Sets
- If statements
- Conditional
- Conditionals (Continued)
- Expression Evaluation
- Float Representation
- Dictionaries
- User input and loops
- Break and Continue
- Function
- Parts of A Function
- Execution of A Function
- Keyword and Default Arguments
- Scope Rules
- String
- Indexing and Slicing of Strings
- More Slicing
- Higher Order Functions
- Sieve of Eratosthenes
- Abstract Data Types
- Classe
- Modules
- Importing Modules
- Classes
- Special Methods
- Class Example
- Inheritanc
- Inheritance and OOPS
- Files and Exceptions
- File I/O
- Exceptions
- Testing your code
- Assertion
- Iterators
- Recursion
- Simple Search
- Estimating Search Time
- Binary Search
- Estimating Binary Search Time
- Recursive Fibonacci
- Tower Of Hanoi
- Sorting
- Selection Sort
- Merge List
- Merge Sor
- Higher-Order Sort
COURSE CURRICULUM - Month 2 (Data Science)
A) Data Analysis and Visualization
1: Numpy: Dealing with N-dim array
- Overview
- Creating ndims arrays
- Why do we need arrays?
- Numeric operations using NumPy
- Indexing and slicing
- Some Mathematical functions
- Generate Random array
2: Pandas: Data analysis and Manipulation
- Pandas Overview
- Data Structures
- Series
- DataFrame
- Series and DataFrame operations
- Missing Data
- Categorical Data
- Working on DateTime data
- Read data from the different file format
- Merging and Grouping Data
- Many other data operations using Pandas
3: Matplotlib / Seaborn : Data visualization
- Overview
- Scatter plot, line plot, bar plot
- Histogram
- Xlabel, Ylabel, Xticks, Yticks, title
- Marker style,type, size
- Figure and Subplot
- Saving a Figure
- HeatMap,BoxPlot
4: Text analysis using NLTK
- What is NLP?
- NLP libraries
- NLP Applications
- Cleaning text data
- Tokenization
- Removal Stop words
- Stemming and Lemmatization
- part-of-speech(POS) tagging
B) Predictive Modeling using scikit-learn
1: scikit-learn
- Regression
- Introduction
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Evaluate Performance of a linear regression model
- Overfitting and underfitting
- Regularization
- Logistic Regression
- Logistic Regression theory
- Implementing Logistic regression with scikit-learn
- Logistic Regression Parameters
- MNIST digit dataset with Logistic Regression
- Predictive modeling on adult income dataset
- Theory Naive Bayes Algorithm
- Features Extraction
- Countvectorizer
- TF-IDF
- Email Spam filtering
- Sentiment analysis
- The theory behind the decision tree
- Implementing a decision tree with scikit-learn
- Decision tree parameters
- Combining multiple decision trees via Random forest
- How random forest works..?
- Cross-validation via K-Fold
- Tuning hyperparameters via grid search
- Confusion matrix
- Recall and Precision
- ROC and AUC
- K-means Clustering
- Elbow method
- Principal components analysis(PCA)
- PCA step by step
- Implementing PCA with scikit-learn
COURSE CURRICULUM - Month 3 (AIML)
- Introduction To Machine Learning
- History and Evolution
- Artificial Intelligence Evolution
- Find out where Machine Learning is applied in Technology and Science.
- Supervised Learning
- Unsupervised Learning
- Data Analysis Packages
- NumPy
- SciPy
- Matplotlib
- Pandas
- Slkearn
- Regression
- Classification
- Generalization, Overfitting, and Underfitting
- Classification
- Understand how continuous supervised learning is different from discrete learning.
- Code a Linear Regression in Python with scikit-learn.
- Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions.
- k-Nearest Neighbor
- Linear models
- Naive Bayes Classifiers
- Decision trees
- Support Vector Machines
- Challenges in unsupervised learning
- Preprocessing and Scaling
- Applying data transformations
- Scaling training and test data the same way
- Principal Component Analysis (PCA)
- A revolution in Artificial Intelligence
- Limitations of Machine Learning
- What is Deep Learning?
- Advantage of Deep Learning over Machine learning
- How Deep Learning Works?
- Activation Functions
- Training a Perceptron
- TensorFlow code-basics
- Tensorflow data types
- Tensorflow methods
- Introduction to Neural Networks
- Neural Network Architecture
- Linear Regression example revisited
- The Neuron
- Neural Network Layers
- The MNIST Dataset
- Coding MNIST NN
- Understand the limitations of a Single Perceptron
- Deepening the network
- Convolutional Neural Networks
- ConvNet Architecture
- Overfitting and Regularization
- Max Pooling and ReLU activations
- Dropout
- Strides and Zero Padding
- Coding Deep ConvNets demo
- Visualizing NN using Tensorflow
- Tensorboard
- How to compose Models in Keras
- Sequential Composition
- Functional Composition
- Predefined Neural Network Layers
Price: @Rs. 30,000 + 18% GST
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Price: @Rs. 30,000 + 18% GST
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