Introduction to Linux​

Linux is an operating system similar to the famous Windows OS we are all used to. A computer is made up of Software, Hardware and Peripheral units as we were taught in introductory class to computer science. Operating System is a type of software (Any non-physical component of the computer is referred to as software). The Operating System manages and controls the computer's resources and facilitates the interaction between humans and the computer. The computer is uselss without an operating system.

Why Linux​

Linux is free and open source operating system and powers a wide range of devices, from smartphones, laptops, servers and supercomputers. Linux is highly customizable and secure which has made it to keep growing in popularity among developers, programmers, DEVOPs, MLOPs, Cybersecurity and power users globally.

• Open-source
• Secure
• Stable
• Flexible
• Huge Community
• Highly customizable

Linux File System​

The computer file system has a tree structure, navigating from a branch, node or leave to another is called walking the tree. You will need to navigate the tree structure to manage and use files and resources stored on the system. The image below shows the Linux file system.

• /home: User-specific files and configurations.
• /etc: System-wide configuration files.
• /var: Variable data like logs.
• /bin and /sbin: Essential and administrative binaries.

Linux Distributions​

A.k.a "distros" are customized versions of Linux OS for different end-user or target market. Popular distros include:

• Ubuntu: A very popular user friendly distribution.
• Fedora: Innovative and cutting-edge features
• Debian: Stable software repository

Linux Commands​

We split the basic commands into 2 parts: File Navigation and File Manipulation

File Navigation​

• ls: List files in or contents of a directory
• cd: Changes the current drectory
• pwd: Prints the current working directory.

File Manipulation​

Linux provides powerful commands for file creation, editting and management:

• touch: creates empty files.
• cp: Copy files from source to destination. A copy is retained in source.
• mv: Moves file from source to destination. No copy is retained in source.

Student Practice Exercises​

1. List the contents of the current directory:

ls

2. List contents of current working directory including hidden contents.

ls -a

3. Practise:

   1. Navigate to your home directory and list all its contents.
   2. Use ls -l to display detailed information about each file in the directory

4. Change directory to a specific location

   I. Create a new directory called school

   mkdir school

II. Go into the new directory

cd school

III. Navigate to /tmp directory and back to your home directory

5. create nested directories i.e one or more directories inside another directory.

mkdir -p school/classes

6. create an empty file.

touch artclass.txt

I. create a file named notes.txt in the school/classes directory II. create three files at once: teachers.txt, students.txt, subjects.txt

7. copy a file

cp notes.txt copy_of_notes.txt

8. copy a directory recursively

cp -r school/ school_copy/

9. Rename a file

mv old_name.txt new_name.txt

I. move a file to another directory:

mv notes.txt /home/backup/

10. Delete a file

rm notes.txt

11. Delete a directory and its contents recursively

rm -r classes/

12. Remove an empty directory.

rmdir school/

13. show command history

history

14. run a specific command from history

!5

15. print a text

echo "I love linux."

16. write text to a file

echo "I mean it, I really love Linux." > love_linux.txt

17. read content of a file:

cat students.txt

18. Combine files

cat students.txt classes.txt subjects.txt > combined.txt

More Practice​

1. Navigate to your home directory.
2. Create a directory called linux_practice.
3. Inside linux_practice, create two subdirectories: data and scripts.
4. Create three files inside data: file1.txt, file2.txt, and file3.txt.
5. Copy all files from data to scripts.
6. Rename file3.txt in scripts to file3_backup.txt.
7. Delete the data directory along with its contents.
8. View your command history to confirm all the steps you performed.

President Bola Ahmed Tinubu Launches Nigerian Student Loan Program: A Comprehensive Guide

On Friday, May 24, 2024, President Bola Ahmed Tinubu launched the Nigerian Student Loan Program for students in Federal universities, Polytechnics, Colleges of Education, and Technical Colleges. This initiative fulfills his pledge to ensure that tuition fees do not hinder students from completing their education.

Key Features of the Loan Program:​

– Interest-Free Loans: Students will not incur any interest on the loans. – Monthly Stipends: Recipients will receive monthly stipends to cover living expenses. – Convenient Online Application: The entire process is conducted online, ensuring ease and efficiency. – Tuition Payment: Fees are paid directly to the institutions and cover one academic session at a time. – Repayment: Repayment of the loan begins two years after completing the National Youth Service Corps (NYSC) program.

Step-by-Step Application Process​

Eligibility: – You must be a student at a federal tertiary institution. – Ensure your institution has uploaded your data to the Nigeria Education Loan Fund (NELFUND) dashboard.

Visit the NELFUND Website: – Go to the official portal: www.nelf.gov.ng – Fill out the application form with accurate and up-to-date information. – Upload the required documents, including:

• Admission letter from the Joint Admissions and Matriculation Board (JAMB).
• National Identity Number (NIN).
• Bank Verification Number (BVN). – Double-check all entries for accuracy. – Submit the completed application form online.

Await Confirmation: – Monitor your email and the NELFUND portal for updates on your application status. – Be ready to provide additional information if requested. – Stay informed about upcoming phases that may include state universities and vocational skills centers.

By following this guide, you can navigate the application process smoothly and take advantage of the financial support available to help you achieve your educational goals.

For all your EDTECH questions and enquiries, please feel free to contact us​

Unlock Your Future with Our Fullstack JavaScript Bootcamp​

Are you ready to dive into the world of web development and transform your passion for coding into a thriving career? Our Fullstack JavaScript Bootcamp is the perfect opportunity for aspiring developers to gain hands-on experience and master the skills needed to build dynamic, responsive, and scalable web applications.

In about 12 weeks, you'll journey from the fundamentals of HTML and CSS to advanced backend development with Node.js, and everything in between. Whether you're a complete beginner or looking to enhance your existing skills, this bootcamp is designed to equip you with the tools and knowledge to succeed in the fast-paced tech industry.

What sets our bootcamp apart? We're focused on practical, real-world learning. You'll work on projects that mimic the challenges faced by professional developers, using the latest technologies and best practices. By the end of the course, you'll have a portfolio of fullstack projects that showcase your ability to create robust applications from scratch.

Plus, with the rise of JavaScript as the language of the web, mastering this versatile stack opens doors to countless opportunities. From startups to tech giants, companies around the world are seeking developers who can bring innovative ideas to life with JavaScript.

Don't miss out on this chance to invest in your future. Enroll in our Fullstack JavaScript Bootcamp today and take the first step towards becoming a fullstack developer. Your dream job in tech is just a few clicks away!

Web Development Bootcamp: Fullstack JavaScript Stack Course Outline​

Week 1: Introduction to Web Development​

• Course Overview & Objectives
  • Understanding the course structure and goals.
  • Overview of the Fullstack JavaScript ecosystem.
• Introduction to the Web
  • How the web works: Client-server architecture.
  • Introduction to HTTP, HTTPS, and RESTful APIs.
• Setting Up Your Development Environment
  • Installing and configuring VSCode, Node.js, npm.
  • Introduction to Git and GitHub for version control.

Week 2: HTML & CSS Fundamentals​

• Introduction to HTML5
  • Structure of an HTML document.
  • Common HTML elements: headings, paragraphs, lists, links, and images.
  • Semantic HTML: Best practices.
• Introduction to CSS3
  • CSS Syntax and Selectors.
  • Styling text, colors, and backgrounds.
  • Box model and layout techniques: Flexbox and Grid.
• Responsive Web Design
  • Media queries and breakpoints.
  • Building responsive layouts.
  • Introduction to CSS frameworks: Material UI, Tailwind CSS.

Week 3: JavaScript Basics​

• Introduction to JavaScript
  • Understanding variables, data types, and operators.
  • Control structures: Conditional statements and loops.
  • Functions and scope in JavaScript.
• DOM Manipulation
  • Selecting and modifying DOM elements.
  • Event handling: Clicks, form submissions, and more.
  • Introduction to browser APIs: LocalStorage, Fetch API.
• JavaScript ES6+ Features
  • Let, const, and arrow functions.
  • Template literals, destructuring, and spread/rest operators.
  • Introduction to Promises and async/await.

Week 4: Version Control with Git​

• Introduction to Git & GitHub
  • Understanding version control and the importance of Git.
  • Basic Git commands: init, clone, add, commit, push, pull.
  • Working with branches and merging.
• Collaborating with GitHub
  • Creating and managing repositories.
  • Collaborating on projects: Pull requests and code reviews.
  • Resolving merge conflicts.

Week 5: Introduction to Backend Development with Node.js​

• Understanding Backend Development
  • The role of the backend in web applications.
  • Introduction to Node.js: Why use Node.js?
• Building a Simple Server with Node.js
  • Setting up a Node.js project.
  • Understanding the event-driven architecture of Node.js.
  • Handling HTTP requests and responses.
• Introduction to Express.js
  • Setting up an Express.js server.
  • Routing in Express: Handling GET, POST, PUT, DELETE requests.
  • Middleware in Express: Static files, logging, and body parsing.

Week 6: Working with Databases​

• Introduction to Databases
  • Understanding relational vs. non-relational databases.
  • Overview of SQL and NoSQL databases.
• Working with MongoDB
  • Setting up MongoDB and understanding its architecture.
  • CRUD operations with MongoDB using Mongoose.
  • Data modeling in MongoDB: Schemas and relationships.
• Connecting Node.js with MongoDB
  • Integrating MongoDB with Express.js.
  • Handling data validation and error handling.
  • Introduction to authentication and authorization.

Week 7: Frontend Development with React.js​

• Introduction to React
  • Understanding the component-based architecture of React.
  • Setting up a React project with Create React App.
  • JSX and rendering components.
• State and Props in React
  • Managing component state and passing props.
  • Handling events in React.
  • Working with forms and controlled components.
• React Hooks
  • Introduction to useState and useEffect.
  • Building custom hooks.
  • Managing global state with Context API.

Week 8: Fullstack Integration​

• Connecting React with Node.js
  • Building RESTful APIs in Express.js.
  • Fetching data from the backend and displaying it in React.
  • Handling user input and sending data to the backend.
• User Authentication
  • Implementing JWT-based authentication in Node.js.
  • Protecting routes and managing user sessions.
  • Authenticating users in React.
• Building a Fullstack Project
  • Setting up a fullstack project with React and Node.js.
  • Implementing core features: CRUD operations, authentication, and state management.
  • Deploying the application to a cloud service (Heroku, Netlify).

Week 9: Advanced Topics​

• Advanced JavaScript Concepts
  • Understanding closures, callbacks, and higher-order functions.
  • Introduction to TypeScript for type safety in JavaScript.
  • Unit testing with Jest and Mocha.
• Advanced React Techniques
  • React Router for client-side routing.
  • State management with Redux.
  • Optimizing performance with React.memo and useMemo.
• Advanced Node.js
  • Building real-time applications with Socket.io.
  • File uploads and image processing in Node.js.
  • Deploying Node.js applications to production (Docker, AWS).

Week 10: Final Project & Deployment​

• Building the Capstone Project
  • Applying everything learned to build a real-world fullstack application.
  • Group collaboration and code reviews.
  • Final project presentation and feedback.
• Deploying to Production
  • Preparing your application for deployment.
  • Deploying the frontend (React) and backend (Node.js) to cloud services.
  • Setting up CI/CD pipelines for continuous deployment.
• Career Development
  • Building a portfolio and showcasing your projects.
  • Preparing for technical interviews: Coding challenges and whiteboard problems.
  • Resume building and LinkedIn optimization.

Week 11-12: Bonus Modules & Hackathon​

• Bonus Modules
  • Introduction to GraphQL: An alternative to REST.
  • WebSockets and real-time communication.
  • Progressive Web Apps (PWAs).
• Hackathon
  • Participate in a 48-hour hackathon to build and launch a new project.
  • Work in teams to solve real-world problems.
  • Final presentations, awards, and networking.

Conclusion​

• Review and Reflection
  • Recap of key concepts and skills learned throughout the course.
  • Feedback session and Q&A.
  • Next steps for continued learning and growth in web development.

This course outline is designed to provide a comprehensive education in fullstack JavaScript development, equipping students with the skills necessary to build, deploy, and maintain modern web applications.

Every collection item in python is iterable An iterable in python can return one item at a time from a collection of items.

An iterable provides iterator for loop and comprehensions i.e list comprehensions, set comprehensions, dictionary comprehensions, and generator expressions. Don't fret if you don't understand these terms, we'll explain them in future lessons.

One of the standout attributes of Horace Learning we use close-to-real-scenarios for our examples.

Lets use a converstaion between a teacher and a student to explain iterables.

Teacher: "Today, we're going to talk about iterables and generators in Python. Can anyone give me an example of an iterable?"

Student: "Um, is a list an iterable?"

Teacher: "Excellent! Yes, a list is an iterable. Can you think of why we might use iterables?"

Student: "I think we use them in for loops, right?"

Teacher: "Correct! Iterables are very useful in for loops. Now, let's talk about generators. They're a special kind of iterable that generates values on-the-fly."

Student: "On-the-fly? What does that mean?"

Teacher: "It means the values are created as you need them, rather than all at once. This can be very memory-efficient for large datasets."

Student: "Oh, I see. Can you show us an example?"

Teacher: "Of course! Let's create a simple generator that yields the squares of numbers."

One easy way to demystify computer science is to think in your native language not the abstract machine language.

• For our first exercise, we will:
  • Get all the words from the teacher's first sentence.
  • Count the number of words in the sentence.
  • Finally, find repeated words in the sentence.

Get the Teacher's First Sentence​

sentence = "Today, we're going to talk about iterables and generators in Python."

Get all the words from the teacher's first sentence.​

words = sentence.split()
print("All words:", words)

Count the number of words in the sentence.​

word_count = len(words)
print("Word count:", word_count)

Find repeated words in the sentence.​

word_frequency = {}
repeated_words = []

for word in words: # Convert to lowercase to ignore case
word = word.lower() # Remove punctuation
word = word.strip('.,')

    # Count word frequency
    if word in word_frequency:
        word_frequency[word] += 1
        if word_frequency[word] == 2:
            repeated_words.append(word)
    else:
        word_frequency[word] = 1

print("Repeated words:", repeated_words)

NB: There are more efficient ways to find repeated words such asusing a set to store the words and then use a dictionary to count the frequency of each word, or using wordcloud with matplotlib. However, this lesson teaches iteration with the for keyword

Exercise 1:

• Print the top 10 most frequent words in the conversation.

Solution​

import string

# Combine all the dialogue into one string
conversation = """
Today, we're going to talk about iterables and generators in Python. Can anyone give me an example of an iterable?
Um, is a list an iterable?
Excellent! Yes, a list is an iterable. Can you think of why we might use iterables?
I think we use them in for loops, right?
Correct! Iterables are very useful in for loops. Now, let's talk about generators. They're a special kind of iterable that generates values on-the-fly.
On-the-fly? What does that mean?
It means the values are created as you need them, rather than all at once. This can be very memory-efficient for large datasets.
Oh, I see. Can you show us an example?
Of course! Let's create a simple generator that yields the squares of numbers.
"""

# Clean and split the text
words = conversation.lower().translate(str.maketrans('', '', string.punctuation)).split()

# Count word frequencies
word_freq = {}
for word in words:
    if len(word) > 3:  # Ignore short words
        word_freq[word] = word_freq.get(word, 0) + 1

# Sort words by frequency
sorted_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True)

# Display top 10 words
print("Top 10 most frequent words:")
for word, freq in sorted_words[:10]:
    print(f"{word}: {'*' * freq}")

How Does Iteration Work?​

• Python checks if the object is iterable by calling __iter__() method. i.e iter(object)
• If the object is iterable, Python calls __iter__() method to get an iterator.
• If it is not iterable, Python raises a TypeError exception:

  TypeError: 'int' object is not iterable

• If __iter__() method is not implementd, but __getitem__() is, Python uses the __getitem__() method to iterate over the object by index starting from 0.
• The iterator is an object with a __next__() method.
• The __next__() method returns the next item in the sequence.
• If the iterator is exhausted, Python raises a StopIteration exception.

Handson 2:

Understanding Iterables and Iterators in Python

1. Python Checks if the Object is Iterable​

class SimpleIterable:
    def __iter__(self):
        return iter([1, 2, 3])

simple = SimpleIterable()
iterator = iter(simple)  # This calls __iter__()
print(list(iterator))  # Output: [1, 2, 3]

If the object is not iterable, Python raises a TypeError exception:​

try:
    iter(42)
except TypeError as e:
    print(e) # Output: 'int' object is not iterable

If __iter__() method is not implemented, but __getitem__() is, Python uses the __getitem__() method to iterate over the object by index starting from 0.​

class SimpleIterable:
    def __getitem__(self, index):
        return [1, 2, 3][index]

simple = SimpleIterable()
iterator = iter(simple)
print(next(iterator))  # Output: 1

Iterator with __next__() method​

class SimpleIterable:
    def __iter__(self):
        return iter([1, 2, 3])

simple = SimpleIterable()
iterator = iter(simple)
print(next(iterator))  # Output: 1

In summary, a pythonic object is iterable if it has __iter__() or __getitem__() method.

Clean code guide

• If you will be iterating over an object, its not necessary to check if the object is iterable. Python will raise a TypeError exception if the object is not iterable. No point re-inventing the wheel. The built in iter() function will mostly be used by python itself than by the developer.
• Use try except to catch the TypeError exception instead of doing explicit checks. we will discuss exceptions in future lessons

Further Exercises​

• Count all sentences by the teacher.
• Count all sentences by the student.
• Count all words by the teacher.
• Count all words by the student.
• Count all punctuation marks by the teacher.
• Count all punctuation marks by the student.
• Count all vowels by the teacher.
• Count all vowels by the student.
• Count all consonants by the teacher.
• Count all consonants by the student.
• Count all numbers by the teacher.
• Count all numbers by the student.
• Count all special characters by the teacher.
• Count all special characters by the student.

Generators are a type of iterable in Python that can be used to generate a sequence of values. They are defined using a function that contains one or more yield expressions. When a generator function is called, it returns a generator object that can be iterated over lazily, yielding one value at a time.

Here's an example of a generator function that generates a sequence of even numbers:

def even_numbers(n: int) -> int:
    """
    Generate a sequence of even numbers up to n.

    Args:
        n (int): The upper limit of the sequence (exclusive).

    Yields:
        int: The next even number in the sequence.
    """
    for i in range(n):
        if i % 2 == 0:
            yield i

The yield keyword is used to return a value from the generator function and pause the execution of the function. The generator object can then be used to continue the execution of the function from the point where it was paused.

To get a value from the generator, you can use the next() function.

# Create a generator object that yields even numbers up to 10
even_nums = even_numbers(10)

# Get and print the first even number (0)
print(next(even_nums))

# Get and print the second even number (2)
print(next(even_nums))

# Get and print the third even number (4)
print(next(even_nums))

You can also use a for loop to iterate over the generator object.

def print_even_numbers(limit: int) -> None:
    """
    Print even numbers up to the given limit.

    Args:
        limit (int): The upper limit for generating even numbers (exclusive).

    Returns:
        None
    """
    for num in even_numbers(limit):
        print(num)

print_even_numbers(10)

You can also convert a generator object to a list. (But don't do this)

even_nums = even_numbers(10)
print(list(even_nums))

The yield Keyword, what does it mean?​

In Python, yield is often described as a combination of "return" and "save."

Yield = Return + Save​

When yield is encountered:

• Return: The current value is returned to the caller.
• Save: The generator's state is saved, including:
  • Local variables
  • Loop counters
  • Execution context

This saved state allows the generator to resume execution from the same point when next() is called.

Key differences from return:​

• Return exits: A return statement exits the function entirely.
• Yield pauses: A yield statement pauses the function, saving its state.

Analogy:​

Inserting bookmark in a book.

Think of yield as setting a bookmark in a book:

• You return to the reader (caller) with the current page (value).
• You save the bookmark (state) so you can resume reading from the same page later.

Note: Please don't convert a generator object to a list if you only need to iterate over it once. This will defeat the purpose of using a generator.

Note: The generator object is not a list, it is an iterator. You can only iterate over it once. If you need to use the values multiple times, you should convert the generator object to a list.

The Generator always remembers its state between calls and you can resume from there with no issues or state errors.

Benefits of Generator​

1. Memory Efficiency: Generators give values on-the-fly, so they don't require as much memory as traditional loops. This makes them ideal for processing large datasets.
2. Performance: Generators can be faster than traditional loops because they don't require as much memory.
3. Pause and Resume: Generators can pause and resume execution, which makes them ideal for processing large datasets.

Note: A generator doesn't raise a StopIteration exception, it is a feature of iterators and you don't have to worry about it. It just exits to make it clear that the generator has reached the end of the sequence.

Generator Expression​

Generator expressions are similar to list comprehensions, but they return a generator object instead of a list.

Note: Remember, in python, if its iterable, then there is a comprehension. e.g List Comprehension, Set Comprehension, Dictionary Comprehension, and now Generator Comprehension.

# Create a generator expression that yields even numbers from 0 to 9
even_nums = (i for i in range(10) if i % 2 == 0)

# Print the generator object (not the actual values)
print(even_nums)

Here is a comparison of a generator expression and a list comprehension.

# Create a generator expression for even numbers from 0 to 9
even_nums = (i for i in range(10) if i % 2 == 0)

# Create a list comprehension for even numbers from 0 to 9
even_nums_list = [i for i in range(10) if i % 2 == 0]

# Print the generator object (not the actual values)
print(even_nums)

# Print the list of even numbers
print(even_nums_list)

Notice generator expression is wrapped in parentheses (), while list comprehension is wrapped in square brackets [].

Use Cases for Generator Expressions​

User generator expressions when you need to generate a sequence of values on-the-fly, but don't need to store all the values in memory. Consider the cost of storing intermediate values in memory when choosing between a generator expression and a list comprehension. Note: When writting any comprehension, if the code spans more than one line, you may be better off with traditional for loop. For the following reasons:

• Readability
• Debugging
• Complexity
• PEP8
• Flake8 However, consider the following exceptions to the rule:
• Line lenght limit rules
• Complex operations
• Team code guide/rule
• Generator expressions are always more efficient for large datasets than a loop that build a list.

Examples​

# List comprehension to create a list of squares
squares = [x**2 for x in range(10)]

# Explanation:
# - This creates a list called 'squares'
# - It uses a list comprehension to generate squares of numbers
# - 'x**2' calculates the square of x
# - 'for x in range(10)' iterates over numbers 0 to 9
# - The result is a list of squares: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Multi-line comprehension (might be better as a loop)​

# Define a complex calculation using list comprehension
complex_calculation = [
    (x**2 + y**2) / (x + y)  # Calculate (x^2 + y^2) / (x + y)
    for x in range(1, 10)    # Outer loop: x from 1 to 9
    for y in range(1, 10)    # Inner loop: y from 1 to 9
    if (x + y) % 2 == 0      # Only include results where x + y is even
]

# Result:
# [1.0, 2.5, 2.0, 3.5, 3.0, 4.5, 4.0, 5.5, 5.0, 2.5, 4.0, 3.5, 5.0, 4.5, 6.0, 5.5, 7.0, 2.0, 3.5, 5.0, 4.5, 6.0, 5.5, 7.0, 6.5]

The above might be clearer as:​

# Initialize an empty list to store the results
complex_calculation = []

# Outer loop: iterate over x from 1 to 9
for x in range(1, 10):
    # Inner loop: iterate over y from 1 to 9
    for y in range(1, 10):
        # Check if the sum of x and y is even
        if (x + y) % 2 == 0:
            # Calculate the result using the formula (x^2 + y^2) / (x + y)
            result = (x**2 + y**2) / (x + y)
            # Append the calculated result to the complex_calculation list
            complex_calculation.append(result)

# At this point, complex_calculation contains all the results

Thank you for reading this article. I hope you found it helpful. Next lesson, we will discuss map, filter vs list comprehension.

Please follow, like and subscribe to my channel and social media pages.

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AI Bootcamp Course Outline​

Week 1: Introduction to AI & Machine Learning​

• Overview of AI and its Applications
  • What is AI?
  • History and evolution of AI
  • Real-world applications of AI
• Introduction to Machine Learning
  • Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
  • Key concepts: Algorithms, models, training, and evaluation
  • Overview of the AI development lifecycle
• Setting Up the Environment
  • Installing Python and essential libraries (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch)
  • Introduction to Jupyter Notebooks

Week 2: Data Science Fundamentals​

• Understanding Data
  • Types of data: Structured, unstructured, and semi-structured
  • Data collection methods and sources
  • Data cleaning and preprocessing techniques
• Exploratory Data Analysis (EDA)
  • Data visualization tools and techniques
  • Identifying patterns and insights in data
  • Feature engineering and selection

Week 3: Supervised Learning​

• Regression Analysis
  • Linear and logistic regression
  • Model evaluation: R-squared, RMSE, confusion matrix, and ROC curve
• Classification Algorithms
  • Decision trees, k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM)
  • Hyperparameter tuning and cross-validation
• Hands-on Project: Building a Predictive Model

Week 4: Unsupervised Learning​

• Clustering Algorithms
  • K-means clustering, Hierarchical clustering, and DBSCAN
• Dimensionality Reduction
  • Principal Component Analysis (PCA) and t-SNE
  • Application of dimensionality reduction in real-world scenarios
• Anomaly Detection
  • Techniques for identifying outliers in data
• Hands-on Project: Customer Segmentation using Clustering

Week 5: Neural Networks & Deep Learning​

• Introduction to Neural Networks
  • Understanding perceptrons and activation functions
  • Building and training a simple neural network
• Deep Learning Concepts
  • Convolutional Neural Networks (CNNs) for image processing
  • Recurrent Neural Networks (RNNs) for sequence data
  • Transfer learning and pre-trained models
• Hands-on Project: Image Classification using CNNs

Week 6: Natural Language Processing (NLP)​

• Introduction to NLP
  • Text preprocessing techniques (tokenization, stemming, lemmatization)
  • Word embeddings (Word2Vec, GloVe)
• NLP Models and Applications
  • Sentiment analysis, Named Entity Recognition (NER), and Machine Translation
  • Building chatbots and text classifiers
• Hands-on Project: Sentiment Analysis using NLP

Week 7: Reinforcement Learning​

• Reinforcement Learning Fundamentals
  • Markov Decision Processes (MDP)
  • Exploration vs. Exploitation trade-off
• Key Algorithms
  • Q-Learning, Deep Q-Networks (DQN), and Policy Gradients
• Hands-on Project: Building an AI Agent for a Game

Week 8: AI Ethics and Responsible AI​

• Ethical Considerations in AI
  • Bias in AI models
  • Fairness, transparency, and accountability in AI systems
• Privacy and Security
  • Data privacy laws and regulations
  • Ensuring data security in AI applications
• AI for Social Good
  • Case studies on AI applications in healthcare, environment, and education

Week 9: AI in Production​

• Model Deployment
  • Techniques for deploying machine learning models
  • Introduction to cloud platforms (AWS, Google Cloud, Azure) for AI
  • API integration and serving models
• Monitoring and Maintenance
  • Model performance tracking and optimization
  • Handling model drift and updating models in production
• Hands-on Project: Deploying an AI Model to the Cloud

Week 10: Capstone Project​

• Project Planning and Development
  • Selecting a project topic and defining objectives
  • Data collection, preprocessing, and model selection
• Implementation and Evaluation
  • Building, training, and testing the model
  • Evaluating the model's performance and refining it
• Presentation and Feedback
  • Presenting the project to peers and instructors
  • Receiving feedback and making improvements

Week 11: Industry Applications & Trends​

• AI in Different Industries
  • AI in healthcare, finance, retail, and autonomous vehicles
• Latest Trends in AI
  • Generative AI, AI for Edge Computing, and explainable AI
• Career Paths in AI
  • Roles in AI: Data Scientist, Machine Learning Engineer, AI Researcher
  • Building a career in AI: skills, certifications, and job search tips

Week 12: Graduation and Next Steps​

• Final Project Showcase
  • Presenting the capstone projects to a panel of experts
  • Peer review and feedback
• Certificate of Completion
  • Awarding certificates to participants
• Networking and Career Support
  • Connecting with industry professionals
  • Resume building and interview preparation

This 12-week AI Bootcamp will equip you with the essential skills and knowledge to excel in the rapidly growing field of artificial intelligence.

Horace Learning adopted the mobile first design approach.

Media Query​

Types of Devices​

Accesibility on all devices​

This blog post explores how you can make the best use of the trove of resources available online and how you can use online learning system as a personal teacher to ace all your exams.

Horace Blog is a publication of latest changes and version releases of Horace Learning.

We blog about technology best practices that will affect how you use and interact with Horace Learning Management System