Data Science, Python, AI and ML

Course Description

Day Topics To Be Covered Hours Medium

Training duration : 50 Hours

1 Introduction to Problem Solving

2 Introduction to Problem Solving

3 Approach to Problem Solving

4 Approach to Problem Solving

5 Aids to Business Problem Solving

6 Introduction to Python Programming

7 Introduction to Python Programming

8 Data Cleaning in Python

9 Data Cleaning in Python

10 Introduction to Kaggle

11 Python for Data Analysis - Numpy, Pandas

12 Python for Data Analysis - Numpy, Pandas

13 Python for Data Visualisation - Matplotlib, Seaborn

14 Python for Data Visualisation - Matplotlib, Seaborn

15 Python for Data Visualisation - Matplotlib, Seaborn

16 Database Design and Data Warehouse

17 Database Design and Data Warehouse

18 Data Modelling

19 Data Modelling

20 SQL Querying

21 SQL Querying

22 Joins and Set Operations

23 Joins and Set Operations

24 Business problem-solving using Data Modelling

25 Business problem-solving using Data Modelling

26 Introduction to Data Analysis

27 Creating and Formatting Charts

28 Creating a Pivot Table

29 Analysing Data in a Pivot Table

30 Getting Started with Tableau

31 Charts, Plots, Hierarchies

32 Grouping and Tree-Maps

33 Dashboard Creation

34 Dashboard Creation

35 Storytelling

36 Inferential Statistics

37 Hypothesis Testing

38 Data Sourcing

39 Data Cleaning

40 Univariate Analysis

Executed Through Online-Platform

ARTIFICIAL INTELLIGENCE

Pre-Program Preparatory Program

Introduction To Python

Introduction To SQL

Data Visualition

Exploratory Data Analysis and Statistics

41 Bivariate Analysis

42 Derived Metrics

43 Maths Essentials for ML

44 Linear Regression

45 Logistic Regression

46 Decision Trees

47 Clustering

48 Data Storytelling and Effective Communication

49 Business Problem Solving

50 Business Problem Solving

51 Support Vector Machines

52 Support Vector Machines

53 Maximal Margin Classifier

54 Maximal Margin Classifier

55 Soft Margin Classifier

56 Soft Margin Classifier

57 Kernels

58 Kernels

59 Decision Trees and Random Forests

60 Decision Trees and Random Forests

61 Algorithms for Decision Tree Construction

62 Algorithms for Decision Tree Construction

63 Introduction of NLP

64 Introduction of NLP

65 Industry Applications of NLP

66 Industry Applications of NLP

67 Regular Expressions

68 Regular Expressions

69 Basic Lexical Processing

70 Basic Lexical Processing

71 Basic Lexical Processing

72 Basic Lexical Processing

73 Text Encoding

74 Text Encoding

75 Tokenization

76 Tokenization

77 Stemming and Lemmatization

78 Stemming and Lemmatization

79 Structure of Neural Networks

80 Structure of Neural Networks

81 Backpropagation in Neural Networks

82 Backpropagation in Neural Networks

83 Hyperparameter Tuning in Neural Networks

84 Hyperparameter Tuning in Neural Networks

85 Introduction to Convolutional Neural Networks

86 Introduction to Convolutional Neural Networks

Deep Learning

Machine Learning

Artificial Intelligence

Natural Language Processing Fundamental

87 Introduction to Convolutional Neural Networks

88 Building CNNs with Python and Keras

89 Building CNNs with Python and Keras

90 Building CNNs with Python and Keras

91 Introduction To Capstone

92 News Recommender System

93 News Recommender System

94 News Recommender System

95 Credit Card Fraud Detection

96 Credit Card Fraud Detection

97 Eye for Blind - (Image Captioning)

98 Eye for Blind - (Image Captioning)

99 Sentiment analysis based Product Recommender system

100 Sentiment analysis based Product Recommender system

Project 1: Implement a Multiclass Classification Model on Skin cancer

dataset using a custom model in TensorFlow.

Total Learning Hours

Project2: Capstone Project | Workshop

In this Lead Scoring project, the student has to apply the

skills taught to build a Machine Learning model to predict

whether a lead would be successfully converted or not.