Project1
Breast Cancer Detection
Supporting Enterprises Around the Globe
Project2
Stock Market Prediction
Target Audience

Any one who wants to deepdive into Machine Learning

Anyone who wants a great handson experience in Deep Learning
Prerequisites

Basic Python programming knowledge is required
Deep Learning Fundamentals  Demos and Projects
Price INR 999/
Course Curriculum

2
Demo1
 Title  Simple Linear Regression Deep Learning
 Demo Resources

3
Demo2
 Title  Understanding Optimizers Execution
 Demo Resources

4
Demo3
 Title  Understanding Optimizers Part1

5
Demo4
 Title  Understanding Optimizers Part2

6
Demo5
 Title  OR Gates Using Keras
 Resource  Code files and Datasets

7
Demo6
 Title  Keras basics

8
Demo7
 Title  CNN Intuition
 Resource  Code files and Datasets

9
Demo8
 Title  RNN Intuition
 Resource  Code files and Datasets

10
Demo9
 Title  RNN 2
 Resource  Code files and Datasets

13
Learning References  Lesson1 : Introduction to DataScience in Nutshell
 Lesson1 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  Lesson2 : Introduction to Statistics
 Lesson2 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. pvalue
 24. Ttest, ztest,ANOVA, Chisquare
 25. Feature selection using hypotheis testing (Ttest, ztest,ANOVA, Chisquare)
 26. Mutual Information

15
Learning Reference  Lesson3 : Introduction to Python
 Lesson3 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  Lesson4 : Statistical Methods in Data Science with Python
 Lesson4 Introduction
 1. Supervised and unsupervised Models
 2. Linear Regression
 3. Multiple Linear Regression
 4. Logistic Regression
 5. Clustering Analysis
 6. KMeans
 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  Lesson5 : Data Mining with Python
 Lesson5 Introduction
 1. Overview of PCA
 2. LDA
 3. Spark ML  Lib overview and cluster utilization
 4. Data Warehouse overview ETL and ELT
 5. BiasVariance tradeoff
 6. Kfold cross validation
 7. Data cleaning and normalization
 8. Detecting outliers
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FAQ

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