Practical Data Science Using Python [Video]
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-
Free ChapterIntroduction to Data Science
-
Statistical Techniques
-
Python for Data Science
- Introduction to Python
- Starting with Python with Jupyter Notebook
- Python Variables and Conditions
- Python Iterations 1
- Python Iterations 2
- Python Lists
- Python Tuples
- Python Dictionaries 1
- Python Dictionaries 2
- Python Sets 1
- Python Sets 2
- NumPy Arrays 1
- NumPy Arrays 2
- NumPy Arrays 3
- Pandas Series 1
- Pandas Series 2
- Pandas Series 3
- Pandas Series 4
- Pandas DataFrame 1
- Pandas DataFrame 2
- Pandas DataFrame 3
- Pandas DataFrame 4
- Pandas DataFrame 5
- Pandas DataFrame 6
- Python User-Defined Functions
- Python Lambda Functions
- Python Lambda Functions and Date-Time Operations
- Python String Operations
-
Exploratory Data Analysis (EDA)
-
Machine Learning
- Introduction to Machine Learning
- Machine Learning Terminology
- History of Machine Learning
- Machine Learning Use Cases and Types
- Role of Data in Machine Learning
- Challenges in Machine Learning
- Machine Learning Lifecycle and Pipelines
- Regression Problems
- Regression Models and Performance Metrics
- Classification Problems and Performance Metrics
- Optimizing Classification Metrics
- Bias and Variance
-
Linear Regression
- Linear Regression Introduction
- Linear Regression - Training and Cost Function
- Linear Regression - Cost Functions and Gradient Descent
- Linear Regression - Practical Approach
- Linear Regression - Feature Scaling and Cost Functions
- Linear Regression OLS Assumptions and Testing
- Linear Regression Car Price Prediction
- Linear Regression Data Preparation and Analysis 1
- Linear Regression Data Preparation and Analysis 2
- Linear Regression Data Preparation and Analysis 3
- Linear Regression Model Building
- Linear Regression Model Evaluation and Optimization
- Linear Regression Model Optimization
-
Logistic Regression
- Logistic Regression Introduction
- Logistic Regression - Logit Model
- Logistic Regression - Telecom Churn Case Study
- Logistic Regression - Data Analysis and Feature Engineering
- Logistic Regression - Build the Logistic Model
- Logistic Regression - Model Evaluation - AUC-ROC
- Logistic Regression - Model Optimization
- Logistic Regression - Model Optimization 2
-
Unsupervised Learning - K-Means Clustering
-
Naive Bayes Probability Model
-
Classification using decision trees
-
Ensemble Methods – Random Forest
-
Advanced Classification Techniques – Support Vector Machine
-
Dimensionality Reduction Using PCA
-
Dimensionality Reduction Using PCA
About this
video
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
- Publication date:
- August 2022
- Publisher
- Packt
- Duration
- 29 hours 46 minutes
- ISBN
- 9781804611814