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Machine Learning & Artificial Intelligence

Courses Information

Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning. At each step, get practical experience by applying your skills to code exercises and projects.

You will learn about training data, and how to use a set of data to discover potentially predictive relationships.

Curriculum
Core Python

Module 1 - Introduction to Python
  • What is Python?
  • Features of Python
  • Setting up Python environment
  • Basic syntax and data types
Module 2 - Variables and Data Types
  • Variables and assignment
  • Basic data types (strings, numbers, floats)
  • lists, tuples and dictionaries
Module 3 - Control Structures
  • Conditional statements (if-else)
  • Loops (for, while)
  • Break and continue statements
Module 4 - Functions
  • Defining and calling functions
  • Function arguments
Module 5 - Modules and Packages
  • Importing modules and packages
  • Creating and publishing packages
  • Working with popular libraries (e.g., NumPy, Pandas)
Module 6 - File Input/Output
  • Reading and writing files
  • Working with CSV and JSON files
  • Reading and writing to databases
Module 7 - Object-Oriented Programming
  • Classes and objects
  • Constructors and destructors
  • Inheritance and polymorphism
  • Encapsulation and abstraction
Module 8 - Error Handling
  • Try-except blocks
  • Raising and catching exceptions


Python For Data Analysis

Module 1: NumPy for Numerical Computing
  • Introduction to NumPy arrays
  • Array operations and indexing
  • Linear algebra operations
  • Random number generation
Module 2 - Pandas for Data Manipulation
  • Introduction to Pandas data structures (Series, DataFrames)
  • Data merging and joining
  • Data grouping and aggregation
  • Data reshaping and pivoting
Module 3 - Data Visualization with Matplotlib and Seaborn
  • Introduction to Matplotlib and Seaborn
  • Plotting different types of data (line plots, scatter plots, bar charts)
  • Customizing plots (labels, titles, legends)
  • Advanced visualization techniques (heatmaps, pairplots)
Module 4 - Data Preprocessing and Cleaning
  • Handling missing data
  • Data normalization and scaling
  • Feature engineering
Statistics

Module 1 - Descriptive Statistics
  • Measures of central tendency (mean, median, mode)
  • Measures of variability (range, variance, standard deviation)
  • Data visualization (histograms, box plots, scatter plots)
Module 2 - Inferential Statistics
  • Hypothesis testing (t-tests, ANOVA, non-parametric tests)
  • Confidence intervals
  • Regression analysis (simple and multiple linear regression)
Module 3 - Probability Theory
  • Basic probability concepts (events, probability distributions)
  • Random variables and probability distributions (Bernoulli, binomial, normal)
  • Bayes' theorem and conditional probability
Module 4 - Descriptive Statistics
  • Measures of central tendency (mean, median, mode)
  • Measures of variability (range, variance, standard deviation)
  • Data visualization (histograms, box plots, scatter plots)
Machine Learning

Module 1 - Introduction to Machine Learning
  • Overview of machine learning
  • Applications of machine learning
  • Types of machine learning (supervised, unsupervised, reinforcement learning)
Module 2 - Supervised Learning
  • Linear regression
  • Logistic regression
  • Decision trees
  • Support vector machines
Module 3 – Unsupervised Learning
  • Clustering (k-means, hierarchical clustering)
  • Dimensionality reduction (PCA, t-SNE)
Module 4 – Data Preprocessing
  • Handling missing data
  • Data normalization and scaling
  • Label encoding and one-hot encoding
  • Feature engineering and selection
Module 5 – Error Types and Model Interpretation
  • Clustering (k-means, hierarchical clustering)Types of errors (Type I and Type II errors)
  • Confusion matrix
  • Model interpretation techniques
Module 6 – Machine Learning in Practice
  • Working with real-world datasets
  • Handling outliers and anomalies
  • Hyperparameter tuning
Module 7 – Project Development and Deployment
  • Building and deploying machine learning models
  • Working with real-world datasets
  • Implementing machine learning solutions for various applications
Artificial Intelligence

Module 1 – Introduction to Artificial Intelligence
  • Overview of AI
  • History of AI
Module 2 – TensorFlow
  • Overview of TensorFlow
  • Basic TensorFlow concepts (tensors, graphs, sessions)
  • Installing and setting up TensorFlow
  • TensorFlow data structures (tensors, variables)
  • Basic mathematical operations in TensorFlow
  • TensorFlow workflow (building, training, evaluating models)
Module 3 – Deep Learning
  • Introduction to deep learning
  • Convolutional neural networks (CNNs)
  • Recurrent neural networks (RNNs)
Module 4 – Natural Language Processing
  • Text processing
  • Sentiment analysis
  • Language models
Module 5 – Neural Networks
  • Introduction to neural networks
  • Basic neural network concepts (perceptron, activation functions)
  • Building and training neural networks with TensorFlow
Module 6 – Computer Vision
  • Image processing
  • Object detection
  • Image classification
Module 7 – Ethics and Future of AI
  • Ethics in AI
  • Bias in AI systems
  • Future directions of AI
SQL for Data Analytics

Module 1 – Introduction to MySQL
  • Overview of MySQL
  • Installing MySQL
  • Basic MySQL commands
Module 2 – Data Manipulation
  • INSERT statements
  • UPDATE statements
  • DELETE statements
Module 3 – SQL Queries
  • SELECT statements
  • Filtering and sorting data
  • Joining tables
  • Subqueries
Module 4 – Stored Procedures
  • Introduction to stored procedures
  • Creating and executing stored procedures
Module 5 - Triggers
  • Introduction to triggers
  • Creating and managing triggers
Module 6 – Views
  • Introduction to views
  • Creating and managing views
  • Using views for data simplification
Module 7 – Data Analysis with SQL
  • Aggregate functions
  • Grouping and aggregating data
Module 8 – Database Administration
  • User management
  • Privileges and access control
Module 8 – Backup and Restoration
  • Importance of backups
  • Types of backups (full, incremental, differential)
PowerBI Data Analytics

Module 1 – Introduction
  • Overview of Power BI
  • Benefits of using Power BI
Module 2 – Data Connectivity
  • Connecting to data sources
  • Importing and transforming data
Module 3 – Data Modeling
  • Creating data models
  • Defining relationships
  • Using DAX
Module 4 – Visualization
  • Creating visualizations (charts, tables, maps)
  • Customizing visualizations
  • Using visualization best practices
Module 5 – Reporting
  • Creating reports and dashboards
  • Adding visualizations and text
  • Using filters and slicers
Module 6 – Advanced Visualization
  • Using advanced visualization features (e.g., treemaps, scatter plots)
  • Creating custom visualizations
  • Using Power BI's advanced visualization tools
Module 7 – Data Analysis with DAX
  • Introduction to DAX
  • Using DAX formulas for calculations
  • Creating calculated columns and measures
Module 8 – Power BI Best Practices
  • Data modeling best practices

R Programming

Module 1 – Introduction to R
  • Overview of R
  • Installing and setting up R
  • Basic R syntax
Module 2 – Data Manipulation
  • Importing and exporting data
  • Data cleaning and preprocessing
  • Data transformation and aggregation
  • Using dplyr and tidyr packages
Module 3 – Data Visualization
  • Introduction to ggplot2
  • Creating plots (scatter plots, bar charts, histograms)
  • Customizing plots (colors, labels, themes)
Module 4 – Data Mining and Machine Learning
  • Introduction to machine learning
  • Supervised and unsupervised learning

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