Practical Data Science using Python

Practical Data Science using Python, available at $19.99, has an average rating of 4.73, with 119 lectures, based on 32 reviews, and has 413 subscribers.

You will learn about Data Science Core Concepts in Detail Data Science Use Cases, Life Cycle and Methodologies Exploratory Data Analysis (EDA) Statistical Techniques Detailed coverage of Python for Data Science and Machine Learning Regression Algorithm – Linear Regression Classification Problems and Classification Algorithms Unsupervised Learning using K-Means Clustering Dimensionality Reduction Techniques (PCA) Feature Engineering Techniques Model Optimization using Hyperparameter Tuning Model Optimization using Grid-Search Cross Validation Introduction to Deep Neural Networks This course is ideal for individuals who are Aspiring Data Science Professionals or Aspiring Machine Learning Engineers It is particularly useful for Aspiring Data Science Professionals or Aspiring Machine Learning Engineers.

Enroll now: Practical Data Science using Python

Summary

Title: Practical Data Science using Python

Price: $19.99

Average Rating: 4.73

Number of Lectures: 119

Number of Published Lectures: 119

Number of Curriculum Items: 119

Number of Published Curriculum Objects: 119

Original Price: ₹799

Quality Status: approved

Status: Live

What You Will Learn

Data Science Core Concepts in Detail
Data Science Use Cases, Life Cycle and Methodologies
Exploratory Data Analysis (EDA)
Statistical Techniques
Detailed coverage of Python for Data Science and Machine Learning
Regression Algorithm – Linear Regression
Classification Problems and Classification Algorithms
Unsupervised Learning using K-Means Clustering
Dimensionality Reduction Techniques (PCA)
Feature Engineering Techniques
Model Optimization using Hyperparameter Tuning
Model Optimization using Grid-Search Cross Validation
Introduction to Deep Neural Networks

Who Should Attend

Aspiring Data Science Professionals
Aspiring Machine Learning Engineers

Target Audiences

Aspiring Data Science Professionals
Aspiring Machine Learning Engineers

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 hands-on, 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 worked-out 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 – 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
Cross-Validation Technique
Learning SVM through an Image Classification project
Understanding Decision Trees
Understanding Ensemble Techniques using Random Forest
Dimensionality Reduction using PCA
K-Means Clustering with Customer Segmentation
Introduction to Deep Learning
Bonus Module: Time Series Prediction using ARIMA

Course Curriculum

Chapter 1: Introduction to Data Science

Lecture 1: Course Introduction

Lecture 2: Data Science Introduction and Use Cases

Lecture 3: Data Science Roles and Lifecycle

Lecture 4: Data Science Stages and Technologies

Lecture 5: Data Science Technologies and Analytics

Lecture 6: ML-Data and CRISP-DM

Chapter 2: Statistical Techniques

Lecture 1: Statistics and Experiments

Lecture 2: Types of Data and Descriptive Statistics

Lecture 3: Random Variables and Normal Distribution

Lecture 4: Histograms and Normal Approximation

Lecture 5: Central Limit Theorem

Lecture 6: Probability Theory

Lecture 7: Binomial Theory – Expected Value and Standard Error

Lecture 8: Hypothesis Testing

Chapter 3: Python for Data Science

Lecture 1: Introduction to Python

Lecture 2: Starting with Python with Jupyter Notebook

Lecture 3: Python Variables and Conditions

Lecture 4: Python Iterations 1

Lecture 5: Python Iterations 2

Lecture 6: Python Lists

Lecture 7: Python Tuples

Lecture 8: Python Dictionaries 1

Lecture 9: Python Dictionaries 2

Lecture 10: Python Sets 1

Lecture 11: Python Sets 2

Lecture 12: Numpy Arrays 1

Lecture 13: Numpy Arrays 2

Lecture 14: Numpy Arrays 3

Lecture 15: Pandas Series 1

Lecture 16: Pandas Series 2

Lecture 17: Pandas Series 3

Lecture 18: Pandas Series 4

Lecture 19: Pandas DataFrame 1

Lecture 20: Pandas DataFrame 2

Lecture 21: Pandas DataFrame 3

Lecture 22: Pandas DataFrame 4

Lecture 23: Pandas DataFrame 5

Lecture 24: Pandas DataFrame 6

Lecture 25: Python User Defined Functions

Lecture 26: Python Lambda Functions

Lecture 27: Python Lambda Functions and Date-Time Operations

Lecture 28: Python String Operations

Chapter 4: Exploratory Data Analysis (EDA)

Lecture 1: Introduction to EDA

Lecture 2: EDA Tools and Processes

Lecture 3: EDA Project – 1

Lecture 4: EDA Project – 2

Lecture 5: EDA Project – 3

Lecture 6: EDA Project – 4

Lecture 7: EDA Project – 5

Lecture 8: EDA Project – 6

Lecture 9: EDA Project – 7

Chapter 5: Machine Learning

Lecture 1: Introduction to Machine Learning

Lecture 2: Machine Learning Terminology

Lecture 3: History of Machine Learning

Lecture 4: Machine Learning Use Cases and Types

Lecture 5: Role of Data in Machine Learning

Lecture 6: Challenges in Machine Learning

Lecture 7: Machine Learning Life Cycle and Pipelines

Lecture 8: Regression Problems

Lecture 9: Regression Models and Perforance Metrics

Lecture 10: Classification Problems and Performance Metrics

Lecture 11: Optmizing Classificaton Metrics

Lecture 12: Bias and Variance

Chapter 6: Linear Regression

Lecture 1: Linear Regression Introduction

Lecture 2: Linear Regression – Training and Cost Function

Lecture 3: Linear Regression – Cost Functions and Gradient Descent

Lecture 4: Linear Regression – Practical Approach

Lecture 5: Linear Regression – Feature Scaling and Cost Functions

Lecture 6: Linear Regression OLS Assumptions and Testing

Lecture 7: Linear Regression Car Price Prediction

Lecture 8: Linear Regression Data Preparation and Analysis 1

Lecture 9: Linear Regression Data Preparation and Analysis 2

Lecture 10: Linear Regression Data Preparation and Analysis 3

Lecture 11: Linear Regression Model Building

Lecture 12: Linear Regression Model Evaluation and Optmization

Lecture 13: Linear Regression Model Optimization

Chapter 7: Logistic Regression

Lecture 1: Logistic Regression Introduction

Lecture 2: Logistic Regression – Logit Model

Lecture 3: Logistic Regression – Telecom Churn Case Study

Lecture 4: Logistic Regression – Data Analysis and Feature Engineering

Lecture 5: Logistic Regression – Build the Logistic Model

Lecture 6: Logistic Regression – Model Evaluation – AUC-ROC

Lecture 7: Logistic Regression – Model Optimization

Lecture 8: Logistic Regression – Model Optimization

Chapter 8: Unsupervised Learning – K-Mean Clustering

Lecture 1: Unsupervised Learning – K-Mean Clustering

Lecture 2: K-Means Clustering Computation

Lecture 3: K-Means Clustering Optimization

Lecture 4: K-Means – Data Preparation and Modelling

Lecture 5: K-Means – Model Optimization

Chapter 9: Naive Bayes Probability Model

Lecture 1: Naive Bayes Probability Model – Introduction

Lecture 2: Naive Bayes Probability Computation

Instructors

Manas Dasgupta
Startup Founder, Data Science Expert

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3 stars: 1 votes
4 stars: 5 votes
5 stars: 26 votes

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