Practical Data Science using Python
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Are you aspiring to become a Data Scientist or Machine Learning Engineer? if yes, then this course is for you.
In this course, you will learn about core concepts of Data Science, Exploratory Data Analysis, Statistical Methods, role of Data, Python Language, challenges of Bias, Variance and Overfitting, choosing the right Performance Metrics, Model Evaluation Techniques, Model Optmization using Hyperparameter Tuning and Grid Search Cross Validation techniques, etc.
You will learn how to perform detailed Data Analysis using Pythin, Statistical Techniques, Exploratory Data Analysis, using various Predictive Modelling Techniques such as a range of Classification Algorithms, Regression Models and Clustering Models. You will learn the scenarios and use cases of deploying Predictive models.
This course covers Python for Data Science and Machine Learning in great detail and is absolutely essential for the beginner in Python.
Most of this course is handson, through completely worked out projects and examples taking you through the Exploratory Data Analysis, Model development, Model Optimization and Model Evaluation techniques.
This course covers the use of Numpy and Pandas Libraries extensively for teaching Exploratory Data Analysis. In addition, it also covers Marplotlib and Seaborn Libraries for creating Visualizations.
There is also an introductory lesson included on Deep Neural Networks with a workedout example on Image Classification using TensorFlow and Keras.
Course Sections:

Introduction to Data Science

Use Cases and Methodologies

Role of Data in Data Science

Statistical Methods

Exploratory Data Analysis (EDA)

Understanding the process of Training or Learning

Understanding Validation and Testing

Python Language in Detail

Setting up your DS/ML Development Environment

Python internal Data Structures

Python Language Elements

Pandas Data Structure â€“ Series and DataFrames

Exploratory Data Analysis (EDA)

Learning Linear Regression Model using the House Price Prediction case study

Learning Logistic Model using the Credit Card Fraud Detection case study

Evaluating your model performance

Fine Tuning your model

Hyperparameter Tuning for Optimising our Models

CrossValidation Technique

Learning SVM through an Image Classification project

Understanding Decision Trees

Understanding Ensemble Techniques using Random Forest

Dimensionality Reduction using PCA

KMeans Clustering with Customer Segmentation

Introduction to Deep Learning

Bonus Module: Time Series Prediction using ARIMA

15Introduction to Python
This attachment contains the code and data for the entire course.

16Starting with Python with Jupyter Notebook

17Python Variables and Conditions

18Python Iterations 1

19Python Iterations 2

20Python Lists

21Python Tuples

22Python Dictionaries 1

23Python Dictionaries 2

24Python Sets 1

25Python Sets 2

26Numpy Arrays 1

27Numpy Arrays 2

28Numpy Arrays 3

29Pandas Series 1

30Pandas Series 2

31Pandas Series 3

32Pandas Series 4

33Pandas DataFrame 1

34Pandas DataFrame 2

35Pandas DataFrame 3

36Pandas DataFrame 4

37Pandas DataFrame 5

38Pandas DataFrame 6

39Python User Defined Functions

40Python Lambda Functions

41Python Lambda Functions and DateTime Operations

42Python String Operations

52Introduction to Machine Learning

53Machine Learning Terminology

54History of Machine Learning

55Machine Learning Use Cases and Types

56Role of Data in Machine Learning

57Challenges in Machine Learning

58Machine Learning Life Cycle and Pipelines

59Regression Problems

60Regression Models and Perforance Metrics

61Classification Problems and Performance Metrics

62Optmizing Classificaton Metrics

63Bias and Variance

64Linear Regression Introduction

65Linear Regression  Training and Cost Function

66Linear Regression  Cost Functions and Gradient Descent

67Linear Regression  Practical Approach

68Linear Regression  Feature Scaling and Cost Functions

69Linear Regression OLS Assumptions and Testing

70Linear Regression Car Price Prediction

71Linear Regression Data Preparation and Analysis 1

72Linear Regression Data Preparation and Analysis 2

73Linear Regression Data Preparation and Analysis 3

74Linear Regression Model Building

75Linear Regression Model Evaluation and Optmization

76Linear Regression Model Optimization

77Logistic Regression Introduction

78Logistic Regression  Logit Model

79Logistic Regression  Telecom Churn Case Study

80Logistic Regression  Data Analysis and Feature Engineering

81Logistic Regression  Build the Logistic Model

82Logistic Regression  Model Evaluation  AUCROC

83Logistic Regression  Model Optimization

84Logistic Regression  Model Optimization