What We Offer?

  • 9 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

  • Simple Linear Regression

  • Understanding Optimizers Execution

  • Understanding Optimizers Part-1

  • Understanding Optimizers Part-2

  • OR Gates Using Keras

  • Keras basics

  • CNN Intuition

  • RNN Intuition

  • RNN

Project-1

Breast Cancer Detection

You as a Deep learning engineer have to create a solution using deep learning framework by studying the cancer pattern in its image and try to predict whether the pattern in cells formation is a sign of cancer development .

Supporting Enterprises Around the Globe

Project-2

Stock Market Prediction

Your task is to create a rigorous model that can predict upcoming stock values by learning its history.

Target Audience

  • Any one who wants to deep-dive into Machine Learning

  • Anyone who wants a great hands-on experience in Deep Learning

Pre-requisites

  • Basic Python programming knowledge is required

Deep Learning Fundamentals - Demos and Projects

Price INR 999/-

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Course Curriculum

  • 1

    Course Introduction

  • 2

    Demo-1

    • Title - Simple Linear Regression Deep Learning
    • Demo Resources
  • 3

    Demo-2

    • Title - Understanding Optimizers Execution
    • Demo Resources
  • 4

    Demo-3

    • Title - Understanding Optimizers Part-1
  • 5

    Demo-4

    • Title - Understanding Optimizers Part-2
  • 6

    Demo-5

    • Title - OR Gates Using Keras
    • Resource - Code files and Datasets
  • 7

    Demo-6

    • Title - Keras basics
  • 8

    Demo-7

    • Title - CNN Intuition
    • Resource - Code files and Datasets
  • 9

    Demo-8

    • Title - RNN Intuition
    • Resource - Code files and Datasets
  • 10

    Demo-9

    • Title - RNN 2
    • Resource - Code files and Datasets
  • 11

    Project-1

  • 12

    Project-2

  • 13

    Learning References - Lesson-1 : Introduction to DataScience in Nutshell

    • Lesson-1 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
  • 14

    Learning Reference - Lesson-2 : Introduction to Statistics

    • Lesson-2 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
  • 15

    Learning Reference - Lesson-3 : Introduction to Python

    • Lesson-3 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
    • 31. Introduction to Pandas
    • 32. Data Structures
    • 33. Series & DataFrame
    • 34. Importing excel sheets, csv files, loading data from html
    • 35. Importing and exporting json files
    • 36. Selection of columns
    • 37. Filtering Dataframes
    • 38. Descriptive Analysis with pandas
    • 39. Data Cleaning
    • 40. Handling Missing Values
    • 41. Handling unwanted columns
    • 42. Handling outliers
    • 43. Handling duplicated entries
    • 44. Finding unique values
    • 45. Creating new categorical features from continuous variable
    • 46. Groupby operations
    • 47. Groupby statistical Analysis
    • 48. Apply method
    • 49. Introduction to Data Visualization
    • 50. Python Libraries
    • 51. Data Visualization Best practices
    • 52. Matplotlib Features
    • 53. Line Properties Plot with (x, y)
    • 54. Controlling Line Patterns and Colors
    • 55. Set Axis, Labels, and Legend Properties
    • 56. Alpha and Annotation
    • 57. Multiple Plots
    • 58. Subplots
    • 59. Scatterplots
    • 60. Pie Charts
    • 61. Barplots
    • 62. Types of Plots and Seaborn
    • 63. Boxplots
    • 64. Distribution Plots
    • 65. Heatmaps
    • 66. Swarmplots and countplots
    • 67. Pointplots
  • 16

    Learning Reference - Lesson-4 : Statistical Methods in Data Science with Python

    • Lesson-4 Introduction
    • 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
  • 17

    Learning Reference - Lesson-5 : Data Mining with Python

    • Lesson-5 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

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.

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