on-demand course

Python Machine Learning in 7 Days

with Arish Ali

June 2018

Intermediate

2h 22m

English

Packt Publishing

Closed Captioning available in English

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Course outline

The Course Overview
1m 51s
Setting Up Your Machine Learning Environment
4m 9s
Exploring Types of Machine Learning
3m 50s
Using Scikit-learn for Machine Learning
6m 25s
Assignment – Train Your First Pre-built Machine Learning Model
1m 26s
Supervised Learning Algorithm
2m 24s
Architecture of a Machine Learning System
3m 40s
Machine Learning Model and Its Components
4m 14s
Linear Regression
2m 4s
Predicting Weight Using Linear Regression
6m 47s
Assignment – Predicting Energy Output of a Power Plant
1m 58s
Review of Predicting Energy Output of a Power Plant
7m 11s
Logistic Regression
5m 51s
Classifying Images Using Logistic Regression
5m 16s
Support Vector Machines
1m 56s
Kernels in a SVM
1m 20s
Classifying Images Using Support Vector Machines
3m 1s
Assignment – Start Image Classifying Using Support Vector Machines
3m 25s
Review of Classifying Images Using Support Vector Machines
5m 17s
Model Evaluation
3m 6s
Better Measures than Accuracy
5m 13s
Understanding the Results
2m 44s
Improving the Models
2m 57s
Assignment – Getting Better Test Sample Results by Measuring Model Performance
1m 51s
Review of Getting Better Test Sample Results by Measuring Model Performance
6m 41s
Unsupervised Learning
2m 3s
Clustering
2m 49s
K-means Clustering
2m 27s
Determining the Number of Clusters
1m 31s
Assignment – Write Your Own Clustering Implementation for Customer Segmentation
2m 7s
Review of Clustering Customers Together
5m 25s
Why Neural Network
3m 15s
Parts of a Neural Network
3m 31s
Working of a Neural Network
2m 33s
Improving the Network
1m 49s
Assignment – Build a Sentiment Analyzer Based on Social Network Using ANN
3m 21s
Review of Building a Sentiment Analyser ANN
5m 24s
Decision Trees
3m 4s
Working of a Decision Tree
3m 8s
Techniques to Further Improve a Model
1m 48s
Random Forest as an Improved Machine Learning Approach
1m 44s
Weekend Task – Solving Titanic Problem Using Random Forest
2m 6s

Overview

In this 2 hr course, you will gain practical knowledge of machine learning using Python, focusing on understanding and implementing key algorithms to solve problems. The course progressively builds from foundational concepts to advanced techniques like neural networks.

What I will be able to do after this course

Understand and implement key machine learning algorithms.
Develop predictive models for data-driven solutions.
Grasp the intuition behind different machine learning methods.
Learn effective usage of Python libraries for machine learning tasks.
Apply advanced concepts like neural networks to real-world problems.

Course Instructor(s)

Arish Ali is a machine learning expert with a strong foundation in Python programming and practical solutions development. With years of teaching experience, Arish brings a methodical and student-friendly approach to this course. His emphasis on hands-on learning ensures impactful skill acquisition.

Who is it for?

This course is ideal for individuals familiar with Python who wish to quickly dive into machine learning. Learners aiming to upskill for creating data-driven models and solutions will benefit. Students and professionals keen on leveraging machine learning in their projects are the target audience. Enroll if you're eager to practically implement machine learning insights.