What We Offer?

  • 10 Demos and 2 Industry standard project with self-paced solution explainer

  • Code files and Datasets

  • Unique story style learning

  • Self-paced Highly structured Learning references

Demos

  • Null Hypothesis

  • Pandas and Numpy

  • Pandas Operations

  • Python Dictionary

  • Supervised Learning Classification

  • Unsupervised Algorithm

  • Spark MLlib

  • ANN

  • SQL and Reporting tool

  • Model Deployment and Tools

Project-1

Taxi trajectory and predicting final destination

Your work is to make a predictive model that can predict the destination of the given taxi

Supporting Enterprises Around the Globe

Project-2

Detect the online bidding fraud by bots

Create a model to identify whether the bid have been placed by robots, flag those suspicious behavior and remove them

Target Audience

  • Any one who wants to start his journey in Data Science

  • Anyone who wants a great hands-on experience in Data Science

Pre-requisites

  • Basic Python programming knowledge is required [Covered in reference section]

Data Science with Python Demos and Projects

Price INR 999/-

ENROLL NOW

Course Curriculum

  • 2

    Pre-requisite Learning References

    • Lesson Introduction
    • 1. World Changing: Data Science and AI
    • 2. The amount of data we create and how we human analyze it daily
    • 3. Various data science roles and responsibility
    • 4. Real life problem and how data science solve real life problem
    • 5. Various tools to solve datascience Problems
    • 6. How to find datascience job in market
    • 7. Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics
    • 8. How industries are benefiting using data science or AI
  • 3

    Demo-1

    • DEMO - Null Hypothesis
  • 4

    Learning References - Demo-1

    • Lesson Introduction
    • 1. What is Descriptive Statistics?
    • 2. Type of data
    • 3. Categorical Variable and its visualization
    • 4. Numerical variable and frequency distribution
    • 5. Histogram
    • 6. Mean median and Mode
    • 7. Cross table and scatter plots
    • 8. Skewness and its type
    • 9. Standard deviation and coefficient of variance
    • 10. Correlation coefficient
    • 11. Covariance
    • 12. Introduction
    • 13. Distribution
    • 14. Normal distribution
    • 15. Standard Normal distribution
    • 16. Standard error
    • 17. Central limit theorem
    • 18. Null vs Alternate Hypothesis
    • 19. Type I and II error
    • 20. Rejection region of null hypothesis
    • 21. Test for mean and population variance known
    • 22. Test for mean and population variance unknown
    • 23. p-value
    • 24. T-test, z-test,ANOVA, Chi-square
    • 25. Feature selection using hypotheis testing (T-test, z-test,ANOVA, Chi-square)
    • 26. Mutual Information
  • 5

    Lets Refresh Python

    • Lesson Introduction
    • 1. Why we use python for data science
    • 2. Introduction to python programming
    • 3. Introduction to Jupyter notebook and spyder
    • 4. Installing python and jupyter, spyder with anaconda
    • 5. Introduction to google colab and how to use gpu environment
    • 6. Variables, Data types, Numbers, Boolean, String
    • 7. Arithmetic operation
    • 8. Adding comments
    • 9. Indexing elements
    • 10. Structuring code with indentation
    • 11. IF, Else
    • 12. For loop
    • 13. while loop
    • 14. Comparison operator
    • 15. Logical Operators
    • 16. Defining function
    • 17. Parameterized function
    • 18. How to use function inside another function
    • 19. Lambda function
    • 20. Built in function
    • 21. Lists
    • 22. List slicing
    • 23. Tuples
    • 24. Sets
    • 25. Dictionary
    • 26. Using Methods
    • 27. OOPS in python
    • 28. Classes and Objects
    • 29. Module and packages
    • 30. Standard Libraries
  • 6

    Demo-2

    • DEMO - Pandas Operations
    • Demo Resources - Code files and Datasets
  • 7

    Demo-3

    • DEMO - Pandas and Numpy
    • Resource - Code files and Datasets
  • 8

    Demo-4

    • DEMO - Python Dictionary
    • Resource - Code files and Datasets
  • 9

    Learning References - Demo-2,3 and4

    • 1. Introduction to Pandas
    • 2. Data Structures
    • 3. Series & DataFrame
    • 4. Importing excel sheets, csv files, loading data from html
    • 5. Importing and exporting json files
    • 6. Selection of columns
    • 7. Filtering Dataframes
    • 8. Descriptive Analysis with pandas
    • 9. Data Cleaning
    • 10. Handling Missing Values
    • 11. Handling unwanted columns
    • 12. Handling outliers
    • 13. Handling duplicated entries
    • 14. Finding unique values
    • 15. Creating new categorical features from continuous variable
    • 16. Groupby operations
    • 17. Groupby statistical Analysis
    • 18. Apply method
    • 19. Introduction to Data Visualization
    • 20. Python Libraries
    • 21. Data Visualization Best practices
    • 22. Matplotlib Features
    • 23. Line Properties Plot with (x, y)
    • 24. Controlling Line Patterns and Colors
    • 25. Set Axis, Labels, and Legend Properties
    • 26. Alpha and Annotation
    • 27. Multiple Plots
    • 28. Subplots
    • 29. Scatterplots
    • 30. Pie Charts
    • 31. Barplots
    • 32. Types of Plots and Seaborn
    • 33. Boxplots
    • 34. Distribution Plots
    • 35. Heatmaps
    • 36. Swarmplots and countplots
    • 37. Pointplots
  • 10

    Demo-5

    • DEMO - Supervised Learning Classification
    • Resource - Code files and Datasets
  • 11

    Demo-6

    • DEMO - Unsupervised Algorithm
    • Resource - Code files and Datasets
  • 12

    Learning References - Demo-5 and Demo-6

    • Lesson Introduction FREE PREVIEW
    • 1. Supervised and unsupervised Models
    • 2. Linear Regression
    • 3. Multiple Linear Regression
    • 4. Logistic Regression
    • 5. Clustering Analysis
    • 6. K-Means
    • 7. Reinforcement Learning
    • 8. Ensemble Learning
    • 9. Bagging
    • 10. Boosting
    • 11. Stacking
    • 12. Matrix
    • 13. Traspose of matrix
    • 14. Dot Product of matrices
    • 15. Linear Algebra
    • 16. Scalars and Vectors
    • 17. Tensors
    • 18. Regularization(Lasso and Ridge)
    • 19. Performance Analysis for Classification problem
    • 20. ROC Curve
    • 21. Model Specification
    • 22. Confusion Matrix
    • 23. Accuracy, Recall, Precision and F1 Score
    • 24. How to handle overfitting and underfitting
    • 25. Grid Serach and random Search
  • 13

    Demo-7

    • DEMO - Spark-MLlib
    • Resource - Code files and Datasets
  • 14

    Learning References - Demo-7

    • Lesson Introduction
    • 1. Overview of PCA
    • 2. LDA
    • 3. Spark ML - Lib overview and cluster utilization
    • 4. Data Warehouse overview ETL and ELT
    • 5. Bias-Variance tradeoff
    • 6. K-fold cross validation
    • 7. Data cleaning and normalization
    • 8. Detecting outliers
  • 15

    Demo-8

    • DEMO - ANN
    • Resource - Code files and Datasets
  • 16

    Learning References - Demo-8

    • Lesson Introduction
    • 1. Deep Learning Prerequisites
    • 2. The history of deep learning and AI
    • 3. Ethics of deep learning and AI
    • 4. Deep learning framework and open source projects
    • 5. Neurons and Deep learning generalization of human brain
    • 6. Basic Neural network layers and design
    • 7. Different activation functions and there use
    • 8. Introduction to tensorfow
    • 9. Basic overview of usage of tensorflow programming
    • 10. Overview of Tensorboard
    • 11. Introduction to keras
    • 12. How keras and tensorflow work together
    • 13. Introduction to CNN
    • 14. Use Cases of CNN
    • 15. Basic structure to understand CNN
    • 16. Covolutional Layer
    • 17. Maxpooling layer
    • 18. Fully connected layer
    • 19. Simple project to understand CNN working
    • 20. Introduction to RNN
    • 21. Use Cases of RNN
    • 22. Simple project to understand RNN working
    • 23. Introduction to NLP
    • 24. Introduction to nltk library
    • 25. Text cleaning- cleaning punctuation, stop words, special characters
    • 26. Stemming
    • 27. Lemmatization
    • 28. Count Vectorisation
    • 29. TF IDF
    • 30. Training your model with text data
    • 31. Optimization method
    • 32. Different Loss function
    • 33. Different Metrics
  • 17

    Demo-9

    • DEMO - SQL and Reporting Tools
    • Resource - Code files and Datasets
  • 18

    Learning References - Demo-9

    • Lesson Introduction
    • 1. General overview of SQL functionality
    • 2. Real world data problem solved by SQL
    • 3. Overview of Tableau
    • 4. Business use cases of Tableau
  • 19

    Demo-10

    • DEMO - Model Deployment and Tools - A quick overview to fill all the connecting dots in Data Science
    • Resource - Code files and Datasets
  • 20

    Learning References - Demo-10

    • Lesson Introduction
    • 1. Connecting each tools and knowledge to support business
    • 2. Debunking common misconceptions in data science
    • 3. Deployment of AI Model
  • 21

    Project-1

  • 22

    Project-2

FAQ

  • After signing up for the course, after how much time would I get access to the Learning Content?

    As soon as you signed-up, you will have full access to the complete self-paced content.

  • How my doubts will be resolved?

    There is a discussion forum attached to each course in your LMS. You can post your questions and our expert(s) will answer the queries.

  • For how long do I have access to the course material?

    The training course content is available to you for lifetime.

Our Students Love Us.

Vishal Agnihotri

This is my first experience to online learning from learnkarts. The course was very engaging and the support provided was awesome. Overall, it is was a great learning experience and it helped me to get job in Python.

Yana Sri

The instructor of the training explained all the doubts patiently. It is very easy to learn from anywhere without any problem. Online forum support is excellent.

Ankit Vohra

The project was very good. Highly recommend this for anyone looking to learn Python.

Coming soon!

Add your email to the mailing list to get the latest updates.

Related Courses