Practical Machine Learning using Python

About 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. And in the last section, you will learn how you will create a FAST API for your ML model, just as you need to for production deployment of your model, and invoke the FAST API using a Streamlit UI.

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

Deplying your ML model using FAST API and invoke using a Streamlit UI