Practical Machine Learning using Python

Practical Machine Learning using Python

Are you aspiring to become a Machine Learning Engineer or Data Scientist? if yes, then this course is for you.

In this course, you will learn about core concepts of Machine Learning, use cases, role of Data, 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 build Classification Models using a range of Algorithms, Regression Models and Clustering Models. You will learn the scenarios and use cases of deploying Machine Learning 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 Machine Learning

• Types of Machine Learning Algorithms

• Use cases of Machine Learning

• Role of Data in Machine Learning

• Understanding the process of Training or Learning

• Understanding Validation and Testing

• Introduction to Python

• Setting up your 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

Concepts and Projects based learning for aspiring Machine Learning Professionals

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What you will learn

• Machine Learning Core Concepts in Detail
• Understand use-case scenarios for applying Machine Learning
• Detailed coverage of Python for Data Science and Machine Learning

Rating: 4.2

Level: Intermediate Level

Duration: 28 hours

Instructor: Manas Dasgupta