Data Science, AI, and Machine Learning with Python

Data Science, AI, and Machine Learning with Python, available at $54.99, has an average rating of 4.16, with 55 lectures, 2 quizzes, based on 19 reviews, and has 3029 subscribers.

You will learn about Learn the basics of Data Science, Artificial Intelligence, and Machine Learning Understand and implement the Python Environment Setup Get introduced to Python Programming for AI, DS and ML Learn Data Importing Understand Exploratory Data Analysis & Descriptive Statistics Master Probability Theory & Inferential Statistics Learn how to do Data Visualization using Python Take a deep-dive into implementation of Data Cleaning, Data Manipulation & Pre-processing using Python programming Understand Predictive Modeling & Machine Learning This course is ideal for individuals who are Data Scientists and Machine Learning Engineers or Beginners & newbies aspiring for a career in Data Science and Machine Learning or Anyone Interested in Data Science and AI or Software Developers and Engineers or Data Analysts and Business Analysts or Researchers and Academics or IT and Data Professionals or Managers and Executives or Entrepreneurs and Startups It is particularly useful for Data Scientists and Machine Learning Engineers or Beginners & newbies aspiring for a career in Data Science and Machine Learning or Anyone Interested in Data Science and AI or Software Developers and Engineers or Data Analysts and Business Analysts or Researchers and Academics or IT and Data Professionals or Managers and Executives or Entrepreneurs and Startups.

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Summary

Title: Data Science, AI, and Machine Learning with Python

Price: $54.99

Average Rating: 4.16

Number of Lectures: 55

Number of Quizzes: 2

Number of Published Lectures: 55

Number of Published Quizzes: 2

Number of Curriculum Items: 57

Number of Published Curriculum Objects: 57

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the basics of Data Science, Artificial Intelligence, and Machine Learning

Understand and implement the Python Environment Setup

Get introduced to Python Programming for AI, DS and ML

Learn Data Importing

Understand Exploratory Data Analysis & Descriptive Statistics

Master Probability Theory & Inferential Statistics

Learn how to do Data Visualization using Python

Take a deep-dive into implementation of Data Cleaning, Data Manipulation & Pre-processing using Python programming

Understand Predictive Modeling & Machine Learning

Who Should Attend

Data Scientists and Machine Learning Engineers

Beginners & newbies aspiring for a career in Data Science and Machine Learning

Anyone Interested in Data Science and AI

Software Developers and Engineers

Data Analysts and Business Analysts

Researchers and Academics

IT and Data Professionals

Managers and Executives

Entrepreneurs and Startups

Target Audiences

Data Scientists and Machine Learning Engineers

Beginners & newbies aspiring for a career in Data Science and Machine Learning

Anyone Interested in Data Science and AI

Software Developers and Engineers

Data Analysts and Business Analysts

Researchers and Academics

IT and Data Professionals

Managers and Executives

Entrepreneurs and Startups

A warm welcome to the Data Science, Artificial Intelligence, and Machine Learning with Python course by Uplatz.

Pythonis a high-level, interpreted programming language that is widely used for various applications, ranging from web development to data analysis, artificial intelligence, automation, and more. It was created by Guido van Rossum and first released in 1991. Python emphasizes readability and simplicity, making it an excellent choice for both beginners and experienced developers.

Data Science

Data Science is an interdisciplinary field focused on extracting knowledge and insights from structured and unstructured data. It involves various techniques from statistics, computer science, and information theory to analyze and interpret complex data.

Key Components:

1. Data Collection:Gathering data from various sources.

2. Data Cleaning:Preparing data for analysis by handling missing values, outliers, etc.

3. Data Exploration:Analyzing data to understand its structure and characteristics.

4. Data Analysis:Applying statistical and machine learning techniques to extract insights.

5. Data Visualization:Presenting data in a visual context to make the analysis results understandable.

Python in Data Science

Python is widely used in Data Science because of its simplicity and the availability of powerful libraries:

Pandas: For data manipulation and analysis.

NumPy: For numerical computations.

Matplotlib and Seaborn: For data visualization.

SciPy: For advanced statistical operations.

Jupyter Notebooks: For interactive data analysis and sharing code and results.

Artificial Intelligence (AI)

Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart.” It includes anything from a computer program playing a game of chess to voice recognition systems like Siri and Alexa.

Key Components:

1. Expert Systems: Computer programs that emulate the decision-making ability of a human expert.

2. Natural Language Processing (NLP): Understanding and generating human language.

3. Robotics: Designing and programming robots to perform tasks.

4. Computer Vision: Interpreting and understanding visual information from the world.

Python in AI

Python is preferred in AI for its ease of use and the extensive support it provides through various libraries:

TensorFlow and PyTorch: For deep learning and neural networks.

OpenCV: For computer vision tasks.

NLTK and spaCy: For natural language processing.

Scikit-learn: For general machine learning tasks.

Keras: For simplifying the creation of neural networks.

Machine Learning (ML)

Machine Learning is a subset of AI that involves the development of algorithms that allow computers to learn from and make predictions or decisions based on data. It can be divided into supervised learning, unsupervised learning, and reinforcement learning.

Key Components:

1. Supervised Learning: Algorithms are trained on labeled data.

2. Unsupervised Learning: Algorithms find patterns in unlabeled data.

3. Reinforcement Learning: Algorithms learn by interacting with an environment to maximize some notion of cumulative reward.

Python in Machine Learning

Python is highly utilized in ML due to its powerful libraries and community support:

Scikit-learn: For implementing basic machine learning algorithms.

TensorFlow and PyTorch: For building and training complex neural networks.

Keras: For simplifying neural network creation.

XGBoost: For gradient boosting framework.

LightGBM: For gradient boosting framework optimized for speed and performance.

Python serves as a unifying language across these domains due to:

1. Ease of Learning and Use: Python’s syntax is clear and readable, making it accessible for beginners and efficient for experienced developers.

2. Extensive Libraries and Frameworks: Python has a rich ecosystem of libraries that simplify various tasks in data science, AI, and ML.

3. Community and Support: A large and active community contributes to a wealth of resources, tutorials, and forums for problem-solving.

4. Integration Capabilities: Python can easily integrate with other languages and technologies, making it versatile for various applications.

Artificial Intelligence, Data Science, and Machine Learning with Python – Course Curriculum

1. Overview of Artificial Intelligence, and Python Environment Setup
Essential concepts of Artificial Intelligence, data science, Python with Anaconda environment setup

2. Introduction to Python Programming for AI, DS and ML
Basic concepts of python programming

3. Data Importing
Effective ways of handling various file types and importing techniques

4. Exploratory Data Analysis & Descriptive Statistics
Understanding patterns, summarizing data

5. Probability Theory & Inferential Statistics
Core concepts of mastering statistical thinking and probability theory

6. Data Visualization
Presentation of data using charts, graphs, and interactive visualizations

7. Data Cleaning, Data Manipulation & Pre-processing
Garbage in – Garbage out (Wrangling/Munging): Making the data ready to use in statistical models

8. Predictive Modeling & Machine Learning

Set of algorithms that use data to learn, generalize, and predict

9. End to End Capstone Project

1. Overview of Data Science and Python Environment Setup

Overview of Data Science

Introduction to Data Science

Components of Data Science

Verticals influenced by Data Science

Data Science Use cases and Business Applications

Lifecycle of Data Science Project

Python Environment Setup

Introduction to Anaconda Distribution

Installation of Anaconda for Python

Anaconda Navigator and Jupyter Notebook

Markdown Introduction and Scripting

Spyder IDE Introduction and Features

2. Introduction to Python Programming

Variables, Identifiers, and Operators

Variable Types

Statements, Assignments, and Expressions

Arithmetic Operators and Precedence

Relational Operators

Logical Operators

Membership Operators

Iterables / Containers

Strings

Lists

Tuples

Sets

Dictionaries

Conditionals and Loops

if else

While Loop

For Loop

Continue, Break and Pass

Nested Loops

List comprehensions

Functions

Built-in Functions

User-defined function

Namespaces and Scope

Recursive Functions

Nested function

Default and flexible arguments

Lambda function

Anonymous function

3. Data Importing

Flat-files data

Excel data

Databases (MySQL, SQLiteetc)

Statistical software data (SAS, SPSS, Stataetc)

web-based data (HTML, XML, JSONetc)

Cloud hosted data (Google Sheets)

social media networks (Facebook Twitter Google sheets APIs)

4. Data Cleaning, Data Manipulation & Pre-processing

Handling errors, missing values, and outliers

Irrelevant and inconsistent data

Reshape data (adding, filtering, and merging)

Rename columns and data type conversion

Feature selection and feature scaling

useful Python packages

Numpy

Pandas

Scipy

5. Exploratory Data Analysis & Descriptive Statistics

Types of Variables & Scales of Measurement

Qualitative/Categorical

Nominal

Ordinal

Quantitative/Numerical

Discrete

Continuous

Interval

Ratio

Measures of Central Tendency

Mean, median, mode,

Measures of Variability & Shape

Standard deviation, variance, and Range, IQR

Skewness & Kurtosis

Univariate data analysis

Bivariate data analysis

Multivariate Data analysis

6. Probability Theory & Inferential Statistics

Probability & Probability Distributions

Introduction to probability

Relative Frequency and Cumulative Frequency

Frequencies of cross-tabulation or Contingency Tables

Probabilities of 2 or more Events

Conditional Probability

Independent and Dependent Events

Mutually Exclusive Events

Bayes’ Theorem

binomial distribution

uniform distribution

chi-squared distribution

F distribution

Poisson distribution

Student’s t distribution

normal distribution

Sampling, Parameter Estimation & Statistical Tests

Sampling Distribution

Central Limit Theorem

Confidence Interval

Hypothesis Testing

z-test, t-test, chi-squared test, ANOVA

Z scores & P-Values

Correlation & Covariance

7. Data Visualization

Plotting Charts and Graphics

Scatterplots

Bar Plots / Stacked bar chart

Pie Charts

Box Plots

Histograms

Line Graphs

ggplot2, lattice packages

Matplotlib & Seaborn packages

Interactive Data Visualization

Plot ly

8. Statistical Modeling & Machine Learning

Regression

Simple Linear Regression

Multiple Linear Regression

Polynomial regression

Classification

Logistic Regression

K-Nearest Neighbors (KNN)

Support Vector Machines

Decision Trees, Random Forest

Naive Bayes Classifier

Clustering

K-Means Clustering

Hierarchical clustering

DBSCAN clustering

Association Rule Mining

Apriori

Market Basket Analysis

Dimensionality Reduction

Principal Component Analysis (PCA)

Linear Discriminant Analysis (LDA)

Ensemble Methods

Bagging

Boosting

9. End to End Capstone Project

Career Path and Job Titles after learning Python

Learning Python can open doors to various career opportunities, especially if you delve deeper into fields like data science, artificial intelligence (AI), and machine learning (ML). Following is a general career path and some job titles you might target on learning Python:

1. Entry-Level Roles

Python Developer:Focuses on writing Python code for web applications, software, or backend systems. Common frameworks used include Django and Flask.

Junior Data Analyst:Involves analyzing datasets, creating visualizations, and generating reports using Python libraries like pandas, Matplotlib, and Seaborn.

Junior Data Scientist:Assists in data collection, cleaning, and applying basic statistical methods. Typically uses Python for data analysis and modeling.

Automation Engineer:Uses Python to automate repetitive tasks, write scripts, and manage processes in various environments.

2. Mid-Level Roles

Data Analyst:Uses Python extensively for data manipulation, visualization, and statistical analysis. Analyzes large datasets to extract actionable insights.

Data Scientist:Applies advanced statistical methods, machine learning models, and data-driven strategies to solve complex business problems. Uses Python for data modeling, feature engineering, and predictive analysis.

Machine Learning Engineer:Focuses on designing, building, and deploying ML models. Works with Python libraries like TensorFlow, PyTorch, and Scikit-learn.

AI Engineer:Develops AI solutions, such as neural networks and natural language processing systems, using Python-based frameworks. Involves deep learning and AI research.

Backend Developer:Builds and maintains server-side logic and integrates front-end components using Python. Ensures high performance and responsiveness of applications.

3. Senior-Level Roles

Senior Data Scientist:Leads data science projects, mentors junior scientists, and designs end-to-end data science solutions. Often involved in strategic decision-making.

Senior Machine Learning Engineer:Oversees the design, implementation, and scaling of ML models. Works on optimizing models for production environments.

AI Architect: Designs and oversees AI systems and architecture. Involves extensive knowledge of AI frameworks and integrating AI into business processes.

Data Engineering Lead:Manages the data infrastructure, including data pipelines, ETL processes, and big data technologies. Ensures that data is clean, accessible, and usable.

Chief Data Officer (CDO):Executive-level position responsible for data governance, strategy, and utilization within an organization.

Course Curriculum

Chapter 1: Installation & Environment Setup, Introduction?to?Spyder?IDE

Lecture 1: Part 1 – Installation & Environment Setup, Introduction?to?Spyder?IDE

Lecture 2: Part 2 – Installation & Environment Setup, Introduction?to?Spyder?IDE

Chapter 2: Variables, Data Types, Data Structures, Methods in Python

Lecture 1: Part 1 – Variables, Data Types, Data Structures, Methods in Python

Lecture 2: Part 2 – Variables, Data Types, Data Structures, Methods in Python

Lecture 3: Part 3 – Variables, Data Types, Data Structures, Methods in Python

Lecture 4: Part 4 – Variables, Data Types, Data Structures, Methods in Python

Chapter 3: Data Structures in Python

Lecture 1: Part 1 – Data Structures in Python

Lecture 2: Part 2 – Data Structures in Python

Chapter 4: Conditional Control Statements, Loops, Comprehensions in Python

Lecture 1: Part 1 – Conditional Control Statements, Loops, Comprehensions in Python

Lecture 2: Part 2 – Conditional Control Statements, Loops, Comprehensions in Python

Lecture 3: Part 3 – Conditional Control Statements, Loops, Comprehensions in Python

Chapter 5: Functions, Maps, Filters, Reduce, Lambda Expressions in Python

Lecture 1: Part 1 – Functions, Maps, Filters, Reduce, Lambda Expressions in Python

Lecture 2: Part 2 – Functions, Maps, Filters, Reduce, Lambda Expressions in Python

Lecture 3: Part 3 – Functions, Maps, Filters, Reduce, Lambda Expressions in Python

Chapter 6: Modules and Packages in Python

Lecture 1: Part 1 – Modules and Packages in Python

Lecture 2: Part 2 – Modules and Packages in Python

Lecture 3: Part 3 – Modules and Packages in Python

Chapter 7: NumPy and Arrays

Lecture 1: Part 1 – NumPy and Arrays

Lecture 2: Part 2 – NumPy and Arrays

Lecture 3: Part 3 – NumPy and Arrays

Chapter 8: Pandas Series and Data Frames

Lecture 1: Part 1 – Pandas Series and Data Frames

Lecture 2: Part 2 – Pandas Series and Data Frames

Lecture 3: Part 3 – Pandas Series and Data Frames

Chapter 9: SQL Data to Python

Lecture 1: Part 1 – SQL Data to Python

Lecture 2: Part 2 – SQL Data to Python

Lecture 3: Part 3 – SQL Data to Python

Lecture 4: Part 4 – SQL Data to Python

Chapter 10: Data Cleaning & Pre-Processing for Data Science and Machine Learning

Lecture 1: Part 1 – Data Cleaning & Pre-Processing for Data Science and Machine Learning

Lecture 2: Part 2 – Data Cleaning & Pre-Processing for Data Science and Machine Learning

Lecture 3: Part 3 – Data Cleaning & Pre-Processing for Data Science and Machine Learning

Lecture 4: Part 4 – Data Cleaning & Pre-Processing for Data Science and Machine Learning

Lecture 5: Part 5 – Data Cleaning & Pre-Processing for Data Science and Machine Learning

Chapter 11: Data Visualizations in Python – Matplotlib and Seaborn

Lecture 1: Part 1 – Data Visualizations in Python – Matplotlib and Seaborn

Lecture 2: Part 2 – Data Visualizations in Python – Matplotlib and Seaborn

Lecture 3: Part 3 – Data Visualizations in Python – Matplotlib and Seaborn

Lecture 4: Part 4 – Data Visualizations in Python – Matplotlib and Seaborn

Chapter 12: Statistics for Machine Learning

Lecture 1: Part 1 – Statistics for Machine Learning

Lecture 2: Part 2 – Statistics for Machine Learning

Lecture 3: Part 3 – Statistics for Machine Learning

Lecture 4: Part 4 – Statistics for Machine Learning

Lecture 5: Part 5 – Statistics for Machine Learning

Chapter 13: Machine Learning Introduction

Lecture 1: Machine Learning Introduction

Chapter 14: Implementation of Machine Learning Algorithms in Python

Lecture 1: Machine Learning – Supervised Regression

Lecture 2: Machine Learning – Supervised Classification

Lecture 3: Part 1 – Machine Learning – Unsupervised Clustering

Lecture 4: Part 2 – Machine Learning – Unsupervised Clustering

Lecture 5: Part 1 – Machine Learning – Unsupervised Association Rule Mining

Lecture 6: Part 2 – Machine Learning – Unsupervised Association Rule Mining

Chapter 15: Self-paced Practice Materials and Assessments

Lecture 1: Self-paced Practice Materials and Assessments

Chapter 16: Case Studies

Lecture 1: Case Studies – Data Cleaning & Preprocessing – Melbourne Housing

Lecture 2: Part 1 – Case Studies – Data Analysis on Netflix

Lecture 3: Part 2 – Case Studies – Data Analysis on Netflix

Lecture 4: Part 1 – Case Studies – DC & EDA – Heart Failure Analysis

Lecture 5: Part 2 – Case Studies – DC & EDA – Heart Failure Analysis

Chapter 17: End-to-end Capstone Project

Lecture 1: End-to-end Capstone Project

Chapter 18: End of Course Quizzes

Instructors

Uplatz Training
Fastest growing global Technology & Cloud Training Provider

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4 stars: 9 votes

5 stars: 6 votes

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