Practical Data Science using Python

Description

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

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