Do You Want To Know How Machine Learning Algorithms Are Being Implemented In Python?

In this course, you'll learn about machine learning and how to utilize python for building reliable and efficient machine learning models to find solutions for real-life problems. We will be covering aspects like preparing data sets to train the machine learning models and setting up a python environment on your desktops and laptops. Also, you'll learn how to utilize these libraries to evaluate and fine-tune your machine learning models.

This beginner program will help anyone who wants to quickly start working on machine learning solutions.  This program will teach the concepts using real-world problems.

Let's Have A Look At The Major Topics We'll Be Covering In This Course!

• Introduction to Machine Learning with Python

• Data Preparation

• Evaluation and tuning of Classification Models

• Supervised Learning  - Regression and Classification

   

In this course, we'll take you through the topics of supervised learning and unsupervised learning. Also, you'll learn about the different algorithms like regression, naive Bayes, decision trees, logistic regression, random forest, KNN, and Support Vector Machines (SVM).

You'll be learning how to implement the following steps to successfully build machine learning models using Python

• Installing the Python and libraries

• Loading the dataset

• Summarizing the dataset

• Visualizing the dataset

• Evaluating some algorithms

• Making some predictions