Description

This course covers Python for data science and machine learning in detail and is for a beginner in Python. You will also learn about core concepts of data science, exploratory data analysis, statistical methods, role of data, challenges of bias, variance and overfitting, model evaluation techniques, model optimization using hyperparameter tuning, grid search cross-validation techniques, and more.

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 optimization using hyperparameter tuning and grid search cross validation techniques, and more. You will learn how to perform detailed data analysis using Python, statistical techniques, and exploratory data analysis, using various predictive modeling 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 also covers classification using decision trees, which include the Gini index and entropy measures and hyperparameter tuning. It covers the use of NumPy and Pandas libraries extensively for teaching exploratory data analysis. In addition, you will also explore advanced classification techniques and support vector machine predictions. There is also an introductory lesson included on Deep Neural Networks with a worked-out example on image classification using TensorFlow and Keras. By the end of the course, you will learn some basic foundations of data science using Python. All resources and code files are placed here: https://github.com/PacktPublishing/Practical-Data-Science-using-Python

What You Will Learn

Learn all about exploratory data analysis (EDA)
Explore various statistical techniques
Understand Dimensionality Reduction Techniques (PCA)
Learn about feature engineering techniques
Learn about data science use cases, life cycle and methodologies
Learn about Deep Neural Networks

Audience

This course is for Python, machine learning developers, data scientists, data analysts, and business analysts. This course will also be beneficial for aspiring data science professionals and machine learning engineers.

Exposure to programming languages will be useful.

Approach

Most of this course is hands-on; completely worked out projects and examples will take you through exploratory data analysis, model development, model optimization, and model evaluation techniques.

Key Features

Detailed coverage of Python for data science and machine learning * Learn about model optimization using hyperparameter tuning * Learn about unsupervised learning using K-Means clustering

Github Repo

https://github.com/PacktPublishing/Practical-Data-Science-using-Python

About the Author

Manas Dasgupta

Manas Dasgupta holds a master's degree (MSc) from the Liverpool John Moore's University (LJMU), the UK in Artificial Intelligence and Machine Learning (AI/ML). My specialization and research areas are Natural Language Processing (NLP) using Deep Learning Methods such as Siamese Networks, Encoder-Decoder techniques, various Language Embedding methods such as BERT, and areas such as Supervised Learning on Semantic Similarity and so on. His expertise area also encompasses an array of Machine Learning and Data Science / Predictive Analytics areas including various Supervised, Unsupervised, and Clustering methods. He has almost 20 Years of experience in the IT Industry, mostly in the Financial Services domain. Starting as a Developer to being an Architect for several years to a leadership position. His key focus and passion are to increase technical breadth and innovation.

Course Outline

1. Introduction to Data Science

1. Data Science Introduction and Use Cases

This video introduces you to data science and its use cases.

2. Data Science Roles and Lifecycle

This video introduces you to data science roles and lifecycle.

3. Data Science Stages and Technologies

This video explains data science stages and technologies.

4. Data Science Technologies and Analytics

This video explains data science technologies and analytics.

5. ML-Data and CRISP-DM

This video explains ML-Data and CRISP-DM.

2. Statistical Techniques

1. Statistics and Experiments

This video explains statistics and experiments.

2. Types of Data and Descriptive Statistics

This video explains types of data and descriptive statistics.

3. Random Variables and Normal Distribution

This video explains random variables and normal distribution.

4. Histograms and Normal Approximation

This video explains histograms and normal approximation.

5. Central Limit Theorem

This video explains the Central Limit Theorem.

6. Probability Theory

This video explains the Probability Theory.

7. Binomial Theory - Expected Value and Standard Error

This video explains the Binomial Theory - expected value and standard error.

8. Hypothesis Testing

This video explains hypothesis testing.

3. Python for Data Science

1. Introduction to Python

This video introduces you to Python.

2. Starting with Python with Jupyter Notebook

This video explains how to start with Python with Jupyter Notebook.

3. Python Variables and Conditions

This video explains Python variables and conditions.

4. Python Iterations 1

This video explains loop structures in Python.

5. Python Iterations 2

This video explains loop structures with numeric ranges.

6. Python Lists

This video explains Python lists.

7. Python Tuples

This video explains Python tuples.

8. Python Dictionaries 1

This video explains Python dictionaries, which are a collection of key-value pairs.

9. Python Dictionaries 2

This video explains item functions in Python dictionaries.

10. Python Sets 1

This video explains Python sets, which are unordered collection objects.

11. Python Sets 2

This video introduces you to creating a copy of the set.

12. NumPy Arrays 1

This video explains the NumPy library.

13. NumPy Arrays 2

This video explains returning the dot product of two arrays.

14. NumPy Arrays 3

This video explains iterating through an array.

15. Pandas Series 1

This video explains the Pandas series.

16. Pandas Series 2

This video explains the various methods of accessing a series of objects.

17. Pandas Series 3

This video explains between the various methods in Pandas series.

18. Pandas Series 4

This video explains the round method.

19. Pandas DataFrame 1

This video explains Pandas DataFrame.

20. Pandas DataFrame 2

This video explains the describe() DataFrame command.

21. Pandas DataFrame 3

This video explains how to manipulate data within your dataframes.

22. Pandas DataFrame 4

This video explains concatenation in DataFrame.

23. Pandas DataFrame 5

This video explains the left outer join in dataframe.

24. Pandas DataFrame 6

This video explains filling missing values in dataframe.

25. Python User-Defined Functions

This video explains Python user-defined functions.

26. Python Lambda Functions

This video explains Python Lambda functions.

27. Python Lambda Functions and Date-Time Operations

This video explains Python Lambda functions and date-time operations.

28. Python String Operations

This video explains Python string operations.

4. Exploratory Data Analysis (EDA)

1. Introduction to EDA

This video introduces you to Exploratory Data Analysis.

2. EDA Tools and Processes

This video explains EDA tools and processes.

3. EDA Project - 1

This video explains an example of lending club case study.

4. EDA Project - 2

This video explains data distribution.

5. EDA Project - 3

This video explains unordered categorical variables.

6. EDA Project - 4

This video explains the isnull() function.

7. EDA Project - 5

This video explains how to apply binning on funded amount.

8. EDA Project - 6

This video explains the correlation between grade of loan and default ratio.

9. EDA Project - 7

This video explains the influence of dti on the loan status or the default ratio.

5. Machine Learning

1. Introduction to Machine Learning

This video introduces you to machine learning.

2. Machine Learning Terminology

This video explains machine learning terminology.

3. History of Machine Learning

This video explains the history of machine learning.

4. Machine Learning Use Cases and Types

This video explains machine learning use cases and types.

5. Role of Data in Machine Learning

This video explains the role of data in machine learning.

6. Challenges in Machine Learning

This video explains challenges in machine learning.

7. Machine Learning Lifecycle and Pipelines

This video explains machine learning lifecycle and pipelines.

8. Regression Problems

This video explains regression problems.

9. Regression Models and Performance Metrics

This video explains regression models and performance metrics.

10. Classification Problems and Performance Metrics

This video explains classification problems and performance metrics.

11. Optimizing Classification Metrics

This video explains optimizing classification metrics.

12. Bias and Variance

This video explains bias and variance.

6. Linear Regression

1. Linear Regression Introduction

This video introduces you to linear regression.

2. Linear Regression - Training and Cost Function

This video explains linear regression - training and cost function.

3. Linear Regression - Cost Functions and Gradient Descent

This video explains linear regression - cost functions and gradient descent.

4. Linear Regression - Practical Approach

This video explains linear regression - practical approach.

5. Linear Regression - Feature Scaling and Cost Functions

This video explains linear regression - feature scaling and cost functions.

6. Linear Regression OLS Assumptions and Testing

This video explains linear regression OLS assumptions and testing.

7. Linear Regression Car Price Prediction

This video explains linear regression car price prediction.

8. Linear Regression Data Preparation and Analysis 1

This video explains linear regression data preparation and analysis.

9. Linear Regression Data Preparation and Analysis 2

This video explains how to plot the distribution of various values of engine types.

10. Linear Regression Data Preparation and Analysis 3

This video explains the train-rest split and feature scaling.

11. Linear Regression Model Building

This video explains linear regression model building.

12. Linear Regression Model Evaluation and Optimization

This video explains linear regression model evaluation and optimization.

13. Linear Regression Model Optimization

This video explains linear regression model optimization.

7. Logistic Regression

1. Logistic Regression Introduction

This video introduces you to logistic regression.

2. Logistic Regression - Logit Model

This video explains logistic regression - Logit model.

3. Logistic Regression - Telecom Churn Case Study

This video explains logistic regression - Telecom Churn case study.

4. Logistic Regression - Data Analysis and Feature Engineering

This video explains logistic regression - data analysis and feature engineering.

5. Logistic Regression - Build the Logistic Model

This video explains logistic regression - build the logistic model.

6. Logistic Regression - Model Evaluation - AUC-ROC

This video explains logistic regression - model evaluation - AUC-ROC.

7. Logistic Regression - Model Optimization

This video explains logistic regression - model optimization.

8. Logistic Regression - Model Optimization 2

This video demonstrates how to predict the level by taking 0.3 as the optimum threshold.

8. Unsupervised Learning - K-Means Clustering

1. Unsupervised Learning - K-Means Clustering

This video introduces you to K-Means clustering.

2. K-Means Clustering Computation

This video explains K-Means clustering computation.

3. K-Means Clustering Optimization

This video explains K-Means clustering optimization.

4. K-Means - Data Preparation and Modelling

This video explains K-Means - data preparation and modelling.

5. K-Means - Model Optimization

This video explains K-Means - model optimization.

9. Naive Bayes Probability Model

1. Naive Bayes Probability Model - Introduction

This video introduces you to the Naive Bayes probability model.

2. Naive Bayes Probability Computation

This video explains Naive Bayes probability computation.

3. Naive Bayes - Employee Attrition Case Study

This video explains Naive Bayes - employee attrition case study.

4. Naive Bayes - Model Building and Optimization

This video explains Naive Bayes - model building and optimization.

10. Classification using decision trees

1. Decision Tree - Model Concept

This video introduces you to decision tree - model concept.

2. Decision Tree - Learning Steps

This video explains decision tree - learning steps.

3. Decision Tree - Gini Index and Entropy Measures

This video explains decision tree - Gini Index and Entropy Measures.

4. Decision Tree - Hyperparameter Tuning

This video explains decision tree - hyperparameter tuning.

5. Decision Tree - Iris Dataset Case Study

This video explains decision tree - Iris Dataset case study.

6. Decision Tree - Model Optimization using Grid Search Cross Validation

This video explains decision tree - model optimization using grid search cross validation.

11. Ensemble Methods - Random Forest

1. Random Forest - Ensemble Techniques Bagging and Random Forest

This video introduces you to ensemble techniques bagging and random forest.

2. Random Forest Steps Pruning and Optimization

This video explains random forest steps pruning and optimization.

3. Random Forest - Model Building and Hyperparameter Tuning using Grid Search CV

This video explains random forest - model building and hyperparameter tuning using grid search CV.

4. Random Forest - Optimization Continued

This video explains random forest - optimization continued.

12. Advanced Classification Techniques - Support Vector Machine

1. Support Vector Machine Concepts

This video introduces you to Support Vector Machine concepts.

2. Support Vector Machine Metrics and Polynomial SVM

This video explains Support Vector Machine metrics and polynomial SVM.

3. Support Vector Machine Project 1

This video explains the Support Vector Machine project.

4. Support Vector Machine Predictions

This video explains Support Vector Machine predictions.

5. Support Vector Machine - Classifying Polynomial Data

This video explains Support Vector Machine - classifying polynomial data.

13. Dimensionality Reduction Using PCA

1. Principal Component Analysis - Concepts

This video introduces you to Principal Component Analysis - concepts.

2. Principal Component Analysis - Computations 1

This video explains Principal Component Analysis - computations.

3. Principal Component Analysis - Computations 2

This video explains Eigenvalues and Eigenvectors.

4. Principal Component Analysis Practical

This video demonstrates PCA using Python.

14. Introduction to Deep Learning

1. Introduction to Deep Learning

This video introduces you to deep learning.

Course Images

Practical Data Science Using Python.

By Packt

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