Introduction to Machine Learning with Python Full Course
This Complete Beginners Machine Learning Course - is a carefully designed course for absolute beginners to intermediate level audiences. The course is designed visually with interesting and clear code examples that anybody can take this course even without any prior programming experience. First few modules are designed to enable audiences to understand the foundational topics of Machine Learning (i.e., ML tools, techniques, Maths behind ML). Once students get the grip on ML, then they are taken to the Python and ML world. You can learn the course at your pace and practice the exercises provided at the end of the topics
Each section of the course is linked to the previous one in terms of utilizing what was already learned and each topic is supplied with lots of examples which will help students in their process of learning.
Throughout the course, the code examples are demonstrated using the popular tool Jupyter Notebook.
We recommend you to download the latest version (3.6) of Python from the Anaconda Distribution website covered in this course.
If you have any suggestions on topics that have not been covered, you can leave a comment. We will do our best to cover them in our next ML Course.
00:00:00 - 00:13:47 Introduction to ML
What is Machine Learning?
Machine Learning vs Traditional Programming
Basic workflow of Machine Learning
Applications of Machine Learning
00:13:48 - 00:28:08 Types of ML
Supervised Learning
Unsupervised learning
Reinforcement Learning
Artificial Neural Network (ANN)
00:28:09 - 00:47:50 ML Models and Applications
Supervised Learning
Unsupervised learning
Reinforcement Learning
00:47:51 - 01:02:06 Setting Up Python Environment
Installing Python on Windows
Installing Python on Linux
Installing Python on Mac
Installing Anaconda
01:02:07 - 01:06:22 Data Acquiring (Lab)
Data Acquiring
Data Formats
Importing Datasets from Public Sources
01:06:23 - 02:15:52 Exploratory Data Analysis and Data Cleaning
What is Exploratory Data Analysis (EDA)?
Understanding Data through Visualization
Data Normalization
Handling Missing Values
Handling Outliers
Data Normalization
EDA Lab
02:15:53 - 02:45:14 Model Selection and Training
Model Selection
Training Model
Model Selection and Training Lab
02:45:15 - 02:58:53 Evaluating Model
K-fold
Accuracy and Precision
Evaluating Model Lab
02:58:54 - 03:03:50 Deploying Model
Deploying ML Model
03:03:51 - 03:32:43 Capstone Project
Capstone Project Lab
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