Data Science, AI, Machine Learning with Python [Free Machine Learning Course] - TechCracked

Data Science, AI, Machine Learning with Python

Complete Python Course: Data Science, Artificial Intelligence, and Machine Learning from basics to advanced

This course includes:

  • 50 hours on-demand video
  • Access on mobile and TV
  • Full lifetime access
  • Certificate of completion

What you'll 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


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

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:

  • Data Collection: Gathering data from various sources.
  • Data Cleaning: Preparing data for analysis by handling missing values, outliers, etc.
  • Data Exploration: Analyzing data to understand its structure and characteristics.
  • Data Analysis: Applying statistical and machine learning techniques to extract insights.
  • 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:

  • Expert Systems: Computer programs that emulate the decision-making ability of a human expert
  • Natural Language Processing (NLP): Understanding and generating human language.
  • Robotics: Designing and programming robots to perform tasks.
  • 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:

  • Supervised Learning: Algorithms are trained on labeled data.
  • Unsupervised Learning: Algorithms find patterns in unlabeled data.
  • 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:

  • Ease of Learning and Use: Python's syntax is clear and readable, making it accessible for beginners and efficient for experienced developers.
  • Extensive Libraries and Frameworks: Python has a rich ecosystem of libraries that simplify various tasks in data science, AI, and ML.
  • Community and Support: A large and active community contributes to a wealth of resources, tutorials, and forums for problem-solving.
  • 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

Also See : Python programming with MySQL database: for Data Science

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