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Build powerful Machine Learning models using Python with hands-on practical examples in just a week.
Machine learning is one of the most sought-after skills in the market. But have you ever wondered where to start or found the course not so easy to follow. With this hands-on and practical machine learning course, you can learn and start applying machine learning in less than a week without having to be an expert mathematician.
In this course, you will be introduced to a new machine learning aspect in each section followed by a practical assignment as a homework to help you in efficiently implement the learnings in a practical manner. With the systematic and fast-paced approach to this course, learn machine learning using Python in the most practical and structured way to develop machine learning projects in Python in a week.
This course is structured to unlock the potential of Python machine learning in the shortest amount of time. If you are looking to upgrade your machine learning skills using Python in the quickest possible time, then this course is for you!
This is a fast-paced course offering practical and actionable guidance with step-by-step instruction and assignments. This course will enable you to develop your own ML models and methods to use them efficiently in the quickest possible way.
What You Will Learn
- Master the most important algorithms in machine learning
- Make predictions based on data
- Get an intuitive understanding of how machine learning works
- Get an intuitive understanding of where to use which machine learning approach
- How to use pre-written libraries in python to work with powerful algorithms
- Learn advanced machine learning techniques like Neural Networks
Table of Contents
01 The Course Overview
02 Setting Up Your Machine Learning Environment
03 Exploring Types of Machine Learning
04 Using Scikit-learn for Machine Learning
05 Assignment – Train Your First Pre-built Machine Learning Model
06 Supervised Learning Algorithm
07 Architecture of a Machine Learning System
08 Machine Learning Model and Its Components
09 Linear Regression
10 Predicting Weight Using Linear Regression
11 Assignment – Predicting Energy Output of a Power Plant
12 Review of Predicting Energy Output of a Power Plant
13 Logistic Regression
14 Classifying Images Using Logistic Regression
15 Support Vector Machines
16 Kernels in a SVM
17 Classifying Images Using Support Vector Machines
18 Assignment – Start Image Classifying Using Support Vector Machines
19 Review of Classifying Images Using Support Vector Machines
20 Model Evaluation
21 Better Measures than Accuracy
22 Understanding the Results
23 Improving the Models
24 Assignment – Getting Better Test Sample Results by Measuring Model Performance
25 Review of Getting Better Test Sample Results by Measuring Model Performance
26 Unsupervised Learning
27 Clustering
28 K-means Clustering
29 Determining the Number of Clusters
30 Assignment – Write Your Own Clustering Implementation for Customer Segmentation
31 Review of Clustering Customers Together
32 Why Neural Network
33 Parts of a Neural Network
34 Working of a Neural Network
35 Improving the Network
36 Assignment – Build a Sentiment Analyzer Based on Social Network Using ANN
37 Review of Building a Sentiment Analyser ANN
38 Decision Trees
39 Working of a Decision Tree
40 Techniques to Further Improve a Model
41 Random Forest as an Improved Machine Learning Approach
42 Weekend Task – Solving Titanic Problem Using Random Forest
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