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AI and Data science

AI and Data science

This introductory course provides a foundational understanding of Artificial Intelligence (AI) and Data Science. Students will learn key concepts, techniques, and tools for data analysis, machine learning, and AI applications. Through practical exercises and real-world examples, students will gain the skills needed to start a career in these rapidly evolving fields.


  • Build Machine learning models with scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)
  • Build & train a neural network with TensorFlow to perform multi-class classification.
  • Build an unsupervised learning model for clustering & anomaly detection.
  • Build recommender systems with a collaborative filtering approach & a content-based deep learning method.
  • Understand how to clean and organize data for analysis, and complete analysis and calculations using python & R.
  • Learn key analytical skills (data cleaning, analysis, & visualization)
  • Learn how to visualize and present data findings in dashboards.



Join this Course
Responsible Mokhtar Ahmed
Last Update 11/01/2023
Completion Time 5 days
Members 6
Beginner
  • Introduction, Variables and String
    • What is programing and its main three concepts
    • What and why Python
    • compiler vs interpreter
    • the concept behind the virtual environment
    • Python, pip, and Jupyter installation
    • Basics of CMD commands
    • Write your first code with python
    • Organize your code with Comments
    • What are Variables
    • Data types
    • variables creation rules
    • String Variables
    • Creating and dealing with quotations
    • Strings Features
    • String slicing and indexing
    • String built-in functions and methods
    • injection of variables into strings
    • Numerical Variables
    • Sequence Variables
    • Boolean Variables
    • Mapping Variables
  • Numerical, Boolean, set, tuple and list
    • Int, float, and complex
    • mathematics operation
    • Comparison Operators
    • Boolean Variables
    • Creating and managing lists
    • Lists Features
    • Lists built-in function
    • List Concatenation and List Replication
    • Adding, removing, and updating list Items
    • Nested Lists
    • enumerate, zip, and sorted
    • Difference between list, set, and tuple
    • sets Features
    • sets built-in functions
    • Sets Join
    • tuples Features
    • adding, removing, and updating tuple items
  • Dictionaries and Functions
    • Dictionaries creating
    • Dictionaries Features
    • Keys and values in dictionaries
    • updating, adding, removing in Dictionaries
    • Dictionaries merge
    • Dictionaries' build-in methods
    • What is the function and why to use it
    • function Creating steps
    • functions arguments
    • local and global variables
    • function single and multiple returns
    • Nested Functions
    • Lambda function
  • control flow
    • hat is control flow and how do applications work
    • Elements of control flow
    • if, elif and else statement
    • while loop statement
    • for statement
    • try and expect statement
    • or "and" and in control flow
  • Object-oriented programming (OOP)
    • what is the difference between normal programming and OOP
    • OOP the main concept
    • encapsulation
    • abstraction
    • inheritance
    • polymorphism
  • Files and OS with python and Modules
    • File handling in Python
    • Read files
    • write in files
    • system commands with python
    • What is Modules and how it works
    • Built-in Modules
    • Renaming and import from Modules
  • SQL Connection
    • Data Types
    • DataBases Types
    • What is a Relational Database
    • Why MYSQL
    • Database data types
    • MYSQL installation
    • MySQL Constraints (rules)
    • Most Important SQL Commands
    • Connection between python and databases
    • Dealing with DataBases SQL within python
    • Relations between tuples and DataBases output and inputs
  • Math Skills For AI
    • Numbers Types
    • Sets
    • Intervals
    • Functions
    • Summations
    • Exponents
    • Logarithms
    • Euler’s Number
    • Limits
    • Derivatives
    • Integrals
    • Introduction to Linear Algebra
    • Scalar, vector and matrix
    • Special Types of Matrices
    • Operations on Matrices
    • Matrix Transpose
    • Matrix Inverse
    • Solving System of Linear Equations
    • Numpy
  • Probabilities & Descriptive statistics
    • Joint
    • Union
    • Conditional
    • Probability Distributions
      Preview
    • Populations, Samples, and Bias
    • Mean, Median and Mode
    • Variance and Standard Deviation
  • Inferential statistics
    • Graphs
    • The Central Limit Theorem
    • Confidence Intervals
    • P-Values
    • Hypothesis Testing
  • Data Processing
    • Missing values
    • Outliers
    • Inconsistent
    • Data conflict
    • Label encoding
    • Entity Identification
    • Redundancy
    • Duplication
  • Supervised Machine learning
    • Linear Regression
    • Basic Linear Regression
    • Residuals and Squared Errors
    • Best Fit Line
    • Gradient Descent
    • Overfitting and Variance
    • Stochastic Gradient Descent
    • The Correlation Coefficient
    • Prediction Intervals
    • Multiple Linear Regression
    • Classiication
    • Logistic Function
    • Multivariable Logistic Regression
    • Confusion Matrices
    • Receiver Operator Characteristics/Area Under Curve
  • Algorithms and Advanced topics
    • Knn
    • Linear Models
    • Naive Bayes Classifiers
    • Decision Trees
    • Support Vector Machines
    • Evaluating
    • Normalization
    • Advanced Topics
      Preview
  • unSupervised Machine learning
    • Kmeans
    • Agglomerative Clustering
    • DBSCAN
    • Evaluating
  • Deep Learning and Neural Networks
    • Simple Neural Network
    • Activation Functions
    • Forward Propagation
    • Backpropagation
    • Weight and Bias Derivatives
    • Stochastic Gradient Descent