Practical Data Science Using Python

Practical Data Science Using Python course is your sure guide if you are aspiring to become a Data Scientist or Machine Learning Engineer.

This course covers Python for Data Science and Machine Learning in great detail and is absolutely essential for the beginner in Python.

Practical Data Science Using Python Course Overview

In this course, you will learn about core concepts of Data Science, Exploratory Data Analysis, Statistical Methods, the 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, etc.

You will learn how to perform detailed Data Analysis using Python, 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.

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 of Image Classification using TensorFlow and Keras.

Course Sections:

• Introduction to Data Science
• Use Cases, Methodologies
• Role of Data in Data Science
• Statistical Methods
• Exploratory Data Analysis
• 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
• Cross Validation
• 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 Project
• Introduction to Deep Learning

Who this course is for:

• Aspiring Data Science Professionals
• Aspiring Machine Learning Engineers