Essentials of Python for Machine Learning and AI

Essentials of Python for Machine Learning and Artificial Intelligence

This course with expert Pramod Gupta provides an introduction to the Python programming language essential for data manipulation, statistical analysis, and modeling techniques required for machine learning and artificial intelligence

$24.95 / MONTH SITE SUBSCRIPTION (W/ 10 DAY FREE TRIAL) $239.95 / YEAR SITE SUBSCRIPTION (W/ 10 DAY FREE TRIAL)

Course curriculum

1. Module 1 - Introduction to Python, Libraries and Installation
2. Module 1 - SLIDES
3. Segment - 01 - Introduction
4. Segment - 02 - A bit about artificial intelligence and machine learning
5. Segment - 03 - Software choices for AI and ML
6. Segment - 04 - Data Mining process
7. Segment - 05 - What is data and data analysis?
8. Segment - 06 - Data quality and its measure
9. Segment - 07 - Data analysis
10. Segment - 08 - Installing Python, Running Juypter Notebook
11. Segment - 09 - Data analytics philosophy and teaching
1. Module 2 - Data Structures in Python, Lists and Tuples and Various Operations
2. Segment - 10 - Python Basics
3. Segment - 11 - Strings
4. Segment - 12 - Other Functions and Help
5. Segment - 13 - Data Structures and Lists
6. Segment - 14 - List Comprehension
7. Segment - 15 - Tuple Data Structure
1. Module 3 - Data Structures in Python, Dictionaries and Sets and Various Operations
2. Segment - 16 - Dictionary and Data Structures
3. Segment - 17 - Sets
4. Segment - 18 - Set Comprehension and Control Flow
5. Segment - 19 - NumPy and-SciPy
1. Module 4 - Introduction to NumPy, 1D Array and 2D Arrays
2. Segment - 20 - NumPy in Depth
3. Segment - 21 - Indexing
1. Module 5 - Deep Dive into NumPy and Various Operations with Arrays
2. Segment - 22 - Array Operations and Mathematics
3. Segment - 23 - Sorting Arrays
4. Segment - 24 - Broadcasting
5. Segment - 25 - Dot Matrices
6. Segment - 26 - logical-operations
7. Segment - 27 - Saving NumPy to CSV Files
8. Segment - 28 - Introduction to Pandas
1. Module 6 - Introduction to Pandas, Pandas Series and Various Operations on Series
2. Segment - 29 - Pandas
3. Segment - 30 - Pandas Series
4. Segment - 31 - operations-on-series
5. Segment - 32 - Arithmetic Operations
6. Segment - 33 - Sorting
7. Segment - 34 - Data Cleaning
1. Module 7 - Intro to Pandas DataFrame and Various Operations with DataFrames
2. Segment - 35 - Dataframes
3. Segment - 36 - Further Dataframe Operations
4. Segment - 37 - Still More Dataframe Operations
1. Module 8 - Data Cleaning and Transformation with Pandas
2. Segment - 38 - Data Cleaning and Transformation
3. Segment - 39 - Manor Tasks in Data Transformation
4. Segment - 40 - DataFrames and Transforming Data
5. Segment - 41 - Dealing with Missing Data
6. Segment - 42 - Discretization and Binning
7. Segment - 43 - Detecting and Filtering Outliers
1. Module 9 - Data Visualization with Matplotlib/Seaborn
2. Segment - 44 - Data Visualization Foundations
3. Segment - 45 - Basic Data Visualization Principles
4. Segment - 46 - Selecting the Right Chart Type and Tools
5. Segment - 47 - Types and Uses of Graphs
6. Segment - 48 - Matplotlib
1. Module 10 - Introduction to Machine Learning and Scikit-Learn
2. Segment - 49 - The Basics of Machine Learning
3. Segment - 50 - Steps to Applying Machine Learning
4. Segment - 51 - Machine Learning Paradigms
5. Segment - 52 - Why Machine Learning and Why Now
6. Segment - 53 - Measuring Model Performance
7. Segment - 54 - Scikit-Learn Introduction
1. Labs - Overview
2. Instructions: Using Jupyter Notebooks
3. Instructions: Virtual labs (For Colaboratory)
4. Labs: Module 1

About this course

Free
69 lessons
13.5 hours of video content

Module 1

Module 2

Module 3

Module 4

Module 5

Module 6

Module 7

Module 8

Module 9

Module 10

Labs

About this course