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

Machine Learning & Artificial Intelligence

Courses Information

Curriculum

Core Python Module 1 - Introduction to Python

Module 2 - Variables and Data Types

Module 3 - Control Structures

Module 4 - Functions

Module 5 - Modules and Packages

Module 6 - File Input/Output

Module 7 - Object-Oriented Programming

Module 8 - Error Handling

Python For Data Analysis Module 1: NumPy for Numerical Computing

Module 2 - Pandas for Data Manipulation

Module 3 - Data Visualization with Matplotlib and Seaborn

Module 4 - Data Preprocessing and Cleaning

Statistics Module 1 - Descriptive Statistics

Module 2 - Inferential Statistics

Module 3 - Probability Theory

Module 4 - Descriptive Statistics

Machine Learning Module 1 - Introduction to Machine Learning

Module 2 - Supervised Learning

Module 3 – Unsupervised Learning

Module 4 – Data Preprocessing

Module 5 – Error Types and Model Interpretation

Module 6 – Machine Learning in Practice

Module 7 – Project Development and Deployment

Artificial Intelligence Module 1 – Introduction to Artificial Intelligence

Module 2 – TensorFlow

Module 3 – Deep Learning

Module 4 – Natural Language Processing

Module 5 – Neural Networks

Module 6 – Computer Vision

Module 7 – Ethics and Future of AI

SQL for Data Analytics Module 1 – Introduction to MySQL

Module 2 – Data Manipulation

Module 3 – SQL Queries

Module 4 – Stored Procedures

Module 5 - Triggers

Module 6 – Views

Module 7 – Data Analysis with SQL

Module 8 – Database Administration

Module 8 – Backup and Restoration

PowerBI Data Analytics Module 1 – Introduction

Module 2 – Data Connectivity

Module 3 – Data Modeling

Module 4 – Visualization

Module 5 – Reporting

Module 6 – Advanced Visualization

Module 7 – Data Analysis with DAX

Module 8 – Power BI Best Practices

R Programming Module 1 – Introduction to R

Module 2 – Data Manipulation

Module 3 – Data Visualization

Module 4 – Data Mining and Machine Learning

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faqs

Frequently asked questions

Core Python

Module 1 - Introduction to Python

Python For Data Analysis

Module 1: NumPy for Numerical Computing

Statistics

Module 1 - Descriptive Statistics

Machine Learning

Module 1 - Introduction to Machine Learning

Artificial Intelligence

Module 1 – Introduction to Artificial Intelligence

SQL for Data Analytics

Module 1 – Introduction to MySQL

PowerBI Data Analytics

Module 1 – Introduction

R Programming

Module 1 – Introduction to R