Artificial Intelligence, Data Science, and Machine Learning with Python

Uplatz offers this comprehensive course on Artificial Intelligence, Data Science and Machine Learning with Python. It is a self-paced course consisting of video lectures. You will be awarded Course Completion Certificate at the end of the course.

Artificial Intelligence (AI): AI refers to the simulation of human intelligence in machines that are designed to think and act like humans. This includes activities like learning, reasoning, problem-solving, perception, and language understanding. AI can be categorized into narrow AI (designed for specific tasks) and general AI (capable of performing any intellectual task that a human can do).

Data Science: Data Science is an interdisciplinary field focused on extracting insights and knowledge from data. It combines statistical analysis, machine learning, data processing, and domain expertise to analyze and interpret complex data sets. Data scientists use various tools and techniques to process large volumes of data to uncover patterns, trends, and actionable insights.

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. Unlike traditional programming, where rules are explicitly programmed, ML models are trained on data to identify patterns and make decisions with minimal human intervention.

Python: Python is a high-level, interpreted programming language known for its simplicity and readability. It has become the preferred language for AI, Data Science, and ML due to its extensive libraries and frameworks, such as NumPy, pandas, matplotlib, scikit-learn, TensorFlow, and Keras, which facilitate the development and deployment of data-driven applications.

How DS, AI, ML with Python works:

1. Data Collection and Preprocessing

   • Data Collection: Gather data from various sources such as databases, APIs, sensors, and web scraping.
   • Data Cleaning: Remove noise and inconsistencies in the data to ensure quality.
   • Data Transformation: Convert raw data into a format suitable for analysis, such as normalizing numerical values or encoding categorical variables.

2. Exploratory Data Analysis (EDA)

   • Descriptive Statistics: Summarize the main characteristics of the data, such as mean, median, variance, and standard deviation.
   • Visualization: Use plots and charts (e.g., histograms, scatter plots, box plots) to understand the distribution and relationships within the data.

3. Feature Engineering

   • Feature Selection: Identify the most relevant variables that contribute to the predictive power of the model.
   • Feature Creation: Generate new features by combining existing ones or using domain knowledge.

4. Model Development

   • Algorithm Selection: Choose appropriate machine learning algorithms based on the problem (e.g., regression, classification, clustering).
   • Model Training: Split the data into training and testing sets, and train the model using the training data.
   • Model Evaluation: Assess the model’s performance using metrics such as accuracy, precision, recall, F1 score, and AUC-ROC curve.

5. Model Optimization

   • Hyperparameter Tuning: Adjust the algorithm's parameters to improve model performance.
   • Cross-Validation: Use techniques like k-fold cross-validation to ensure the model generalizes well to unseen data.

6. Deployment and Monitoring

   • Deployment: Integrate the trained model into a production environment where it can make real-time predictions.
   • Monitoring: Continuously monitor the model's performance and update it as needed to maintain accuracy over time.

7. Application of AI Techniques

   • Natural Language Processing (NLP): Develop applications like chatbots, sentiment analysis, and language translation.
   • Computer Vision: Implement image and video analysis tasks, such as object detection, facial recognition, and automated inspection.
   • Recommendation Systems: Build systems that suggest products, content, or actions based on user behavior and preferences.

Tools and Libraries

• NumPy: Fundamental package for numerical computations.
• pandas: Data manipulation and analysis library.
• matplotlib and seaborn: Libraries for data visualization.
• scikit-learn: Machine learning library for data mining and data analysis.
• TensorFlow and Keras: Libraries for deep learning and neural network models.
• NLTK and spaCy: Libraries for natural language processing.

By mastering AI, Data Science, and Machine Learning with Python, individuals and organizations can harness the power of data to make informed decisions, automate processes, and create innovative solutions to complex problems.

Reed courses certificate of completion

Digital certificate - Included

Uplatz Certificate of Completion

Digital certificate - Included

• AI, DS, ML with Python - all lectures 50:37:16

  • 1: Part 1 - Installation & Environment Setup, Introduction to Spyder IDE Preview 38:13
  • 2: Part 2 - Installation & Environment Setup, Introduction to Spyder IDE 39:43
  • 3: Part 1 - Variables, Data Types, Data Structures, Methods in Python 38:54
  • 4: Part 2 - Variables, Data Types, Data Structures, Methods in Python 49:41
  • 5: Part 3 - Variables, Data Types, Data Structures, Methods in Python 51:49
  • 6: Part 4 - Variables, Data Types, Data Structures, Methods in Python 48:50
  • 7: Part 1 - Data Structures in Python 58:03
  • 8: Part 2 - Data Structures in Python 1:10:20
  • 9: Part 1 - Conditional Control Statements, Loops, Comprehensions in Python 44:35
  • 10: Part 2 - Conditional Control Statements, Loops, Comprehensions in Python 1:04:46
  • 11: Part 3 - Conditional Control Statements, Loops, Comprehensions in Python 53:58
  • 12: Part 1 - Functions, Maps, Filters, Reduce, Lambda Expressions in Python 1:02:41
  • 13: Part 2 - Functions, Maps, Filters, Reduce, Lambda Expressions in Python 57:28
  • 14: Part 3 - Functions, Maps, Filters, Reduce, Lambda Expressions in Python 48:24
  • 15: Part 1 - Modules and Packages in Python 53:47
  • 16: Part 2 - Modules and Packages in Python Preview 50:25
  • 17: Part 3 - Modules and Packages in Python 59:42
  • 18: Part 1 - NumPy and Arrays 1:01:23
  • 19: Part 2 - NumPy and Arrays 43:26
  • 20: Part 3 - NumPy and Arrays 49:05
  • 21: Part 1 - Pandas Series and Data Frames 34:46
  • 22: Part 2 - Pandas Series and Data Frames 50:11
  • 23: Part 3 - Pandas Series and Data Frames 55:55
  • 24: Part 1 - SQL Data to Python 07:36
  • 25: Part 2 - SQL Data to Python 46:12
  • 26: Part 3 - SQL Data to Python 38:16
  • 27: Part 4 - SQL Data to Python 49:08
  • 28: Part 1 - Data Cleaning & Pre-Processing for Data Science and Machine Learning 57:40
  • 29: Part 2 - Data Cleaning & Pre-Processing for Data Science and Machine Learning 1:00:22
  • 30: Part 3 - Data Cleaning & Pre-Processing for Data Science and Machine Learning 58:41
  • 31: Part 4 - Data Cleaning & Pre-Processing for Data Science and Machine Learning 57:59
  • 32: Part 5 - Data Cleaning & Pre-Processing for Data Science and Machine Learning 37:12
  • 33: Part 1 - Data Visualizations in Python - Matplotlib and Seaborn 1:13:09
  • 34: Part 2 - Data Visualizations in Python - Matplotlib and Seaborn 1:23:24
  • 35: Part 3 - Data Visualizations in Python - Matplotlib and Seaborn 1:05:27
  • 36: Part 4 - Data Visualizations in Python - Matplotlib and Seaborn 56:51
  • 37: Part 1 - Statistics for Machine Learning 1:00:26
  • 38: Part 2 - Statistics for Machine Learning 27:08
  • 39: Part 3 - Statistics for Machine Learning 1:09:07
  • 40: Part 4 - Statistics for Machine Learning 1:00:30
  • 41: Part 5 - Statistics for Machine Learning 55:48
  • 42: Machine Learning Introduction 1:48:49
  • 43: ML - Supervised Regression 1:19:44
  • 44: ML - Supervised Classification 46:08
  • 45: Part 1 - ML - Unsupervised Clustering 1:12:50
  • 46: Part 2 - ML - Unsupervised Clustering 30:15
  • 47: Part 1 - ML - Unsupervised Association Rule Mining 1:06:36
  • 48: Part 2 - ML - Unsupervised Association Rule Mining 28:44
  • 49: Self-paced Practice Materials and Assessments 43:33
  • 50: Case Studies - Data Cleaning & Preprocessing - Melbourne Housing 53:07
  • 51: Part 1 - Case Studies - Data Analysis on Netflix 54:48
  • 52: Part 2 - Case Studies - Data Analysis on Netflix 1:31:48
  • 53: Part 1 - Case Studies - DC & EDA - Heart Failure Analysis 50:16
  • 54: Part 2 - Case Studies - DC & EDA - Heart Failure Analysis 39:39
  • 55: End-to-end Capstone Project 1:59:58

This comprehensive course is designed to provide you with a solid foundation in Artificial Intelligence (AI), Data Science, and Machine Learning (ML) using Python, one of the most powerful and versatile programming languages in the tech industry today.

• Artificial Intelligence: Dive into the world of AI and understand how machines can mimic human intelligence. Explore the fundamental concepts of AI, including neural networks, natural language processing, and robotics. Learn how AI is transforming industries and solving complex problems.

• Data Science: Master the skills required to extract meaningful insights from vast amounts of data. Learn data manipulation, visualization, and statistical analysis techniques. Understand the lifecycle of data science projects, from data collection and cleaning to model building and evaluation.

• Machine Learning: Gain expertise in machine learning, a subset of AI focused on building models that can learn from data. Study various supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Implement these algorithms using Python libraries such as scikit-learn, TensorFlow, and Keras.

• Python Programming: Develop proficiency in Python, the preferred language for data science and AI. Learn to write clean, efficient, and reusable code. Understand the use of libraries and frameworks that facilitate AI and data science projects.

Course Curriculum

Artificial Intelligence, Data Science, and Machine Learning with Python

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

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

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

Exploratory Data Analysis & Descriptive Statistics
Understanding patterns, summarizing data

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

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

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

Predictive Modeling & Machine Learning

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

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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, SQLite...etc)

• Statistical software data (SAS, SPSS, Stata...etc)

• web-based data (HTML, XML, JSON...etc)

• 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

Everyone

Passion & determination to achieve your goals!

• Data Scientist
• Machine Learning Engineer
• AI Engineer
• Data Analyst
• Big Data Engineer
• Research Scientist (AI)
• Data Engineer
• Deep Learning Engineer
• NLP Engineer
• Computer Vision Engineer
• Quantitative Analyst
• Predictive Analytics Developer
• Robotics Engineer
• AI Product Manager
• AI Consultant
• Business Analyst
• Data Architect
• Recommendation System Engineer
• Statistical Programmer
• Operations Research Analyst

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