Let Machine Learning Turn into Your Side Hustle with Automated Content Generation : Step-by-Step Beginner's Guide

 

Machine learning, particularly in the field of Natural Language Processing (NLP), offers exciting opportunities for students and freelancers to create side hustles. Here's a simple, realistic plan tailored for individuals with no prior experience:

Step 1: Understand the Basics of NLP

Before diving into content generation, familiarize yourself with Natural Language Processing (NLP). It’s the technology behind text generation and optimization. Start with free resources like introductory articles or YouTube tutorials. Key concepts to learn include:

• Tokenization: Breaking text into smaller units (words or phrases).

• Sentiment Analysis: Understanding the emotional tone of text.

• Text Generation: Using AI models to create human-like text.

Here's a ready-made Python code to get you started with tokenization and sentiment analysis:

python
# Import necessary libraries
import nltk
from nltk.tokenize import word_tokenize, sent_tokenize
from textblob import TextBlob
import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np

# Download necessary NLTK data
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')

# Step 1: Tokenization
def tokenize_text(text):
    # Word tokenization
    words = word_tokenize(text)
    print("Word Tokenization:", words)
    
    # Sentence tokenization
    sentences = sent_tokenize(text)
    print("Sentence Tokenization:", sentences)
    
    # TextBlob tokenization
    blob = TextBlob(text)
    print("TextBlob Tokenization:", blob.words)

# Example text for tokenization
example_text = "Hello, world! This is a sample text for tokenization. It contains multiple sentences."

# Tokenize the example text
tokenize_text(example_text)

# Step 2: Sentiment Analysis
def analyze_sentiment(text):
    # Using TextBlob for sentiment analysis
    blob = TextBlob(text)
    sentiment = blob.sentiment.polarity
    if sentiment > 0:
        print("The sentiment is positive.")
    elif sentiment < 0:
        print("The sentiment is negative.")
    else:
        print("The sentiment is neutral.")

# Example texts for sentiment analysis
positive_text = "I love this product! It's amazing and works perfectly."
negative_text = "This is the worst product I've ever bought. It's a complete waste of money."

# Analyze sentiment
print("\nSentiment Analysis:")
analyze_sentiment(positive_text)
analyze_sentiment(negative_text)

# Step 3: Text Generation (using a simple LSTM model)
# Load the Tiny Shakespeare dataset
dataset, info = tfds.load('tiny_shakespeare', with_info=True, as_supervised=False)

# Preprocess the data
def preprocess_data(dataset):
    text = []
    for example in dataset['train']:
        text.append(example['text'].numpy().decode('utf-8'))
    text = ' '.join(text)
    return text

# Tokenize the text
tokenizer = tf.keras.preprocessing.text.Tokenizer(char_level=True)
tokenizer.fit_on_texts([preprocess_data(dataset)])
total_words = len(tokenizer.word_index) + 1

# Create sequences for training
input_sequences = []
for line in preprocess_data(dataset).split('\n'):
    token_list = tokenizer.texts_to_sequences([line])[0]
    for i in range(1, len(token_list)):
        n_gram_sequence = token_list[:i+1]
        input_sequences.append(n_gram_sequence)

# Pad sequences
max_sequence_len = max([len(x) for x in input_sequences])
input_sequences = np.array(tf.keras.preprocessing.sequence.pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))

# Create predictors and label
X, y = input_sequences[:,:-1],input_sequences[:,-1]
y = tf.keras.utils.to_categorical(y, num_classes=total_words)

# Build the model
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(total_words, 100, input_length=max_sequence_len-1),
    tf.keras.layers.LSTM(150, return_sequences=True),
    tf.keras.layers.LSTM(150),
    tf.keras.layers.Dense(total_words, activation='softmax')
])

# Compile the model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Train the model
model.fit(X, y, epochs=10, verbose=1)

# Function to generate text
def generate_text(seed_text, next_words, model, max_sequence_len):
    for _ in range(next_words):
        token_list = tokenizer.texts_to_sequences([seed_text])[0]
        token_list = tf.keras.preprocessing.sequence.pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
        predicted = model.predict(token_list, verbose=0)
        predicted = np.argmax(predicted, axis=-1)
        output_word = ""
        for word, index in tokenizer.word_index.items():
            if index == predicted:
                output_word = word
                break
        seed_text += " " + output_word
    return seed_text

# Generate text
seed_text = "To be or not to be"
generated_text = generate_text(seed_text, 20, model, max_sequence_len)
print("\nGenerated Text:", generated_text)

This code provides a comprehensive example of tokenization, sentiment analysis, and text generation using Python libraries like NLTK, TextBlob, and TensorFlow. Here's a breakdown:
Tokenization: The tokenize_text function demonstrates word and sentence tokenization using NLTK and TextBlob.
Sentiment Analysis: The analyze_sentiment function uses TextBlob to determine the sentiment of given text.
Text Generation: A simple LSTM model is trained on the Tiny Shakespeare dataset to generate text based on a seed phrase.

Remember, this is a basic example to get you started. For real-world applications, you might need to fine-tune models, use more sophisticated techniques, or leverage pre-trained models like those from Hugging Face's Transformers library for better results.

Step 2: Choose Free or Affordable Tools

You don’t need to build models from scratch. Start by using pre-built AI tools that are beginner-friendly:

• ChatGPT: Ideal for generating blog posts, captions, and product descriptions.

• Narrato AI: Automates social media and blog content creation.

• Rytr: Provides templates for short-form content like ads and emails.

Explore free versions of these tools to practice generating content.

Here's a ready-made Python code to help beginners choose and explore free or affordable AI tools for content generation:

Here's a ready-made Python code to help beginners choose and explore free or affordable AI tools for content generation:

python
import requests
import json

# Step 2: Choose Free or Affordable Tools

def get_chatgpt_response(prompt):
    """
    Function to get a response from ChatGPT API.
    Note: You need to replace 'YOUR_API_KEY' with your actual API key.
    """
    url = "https://api.openai.com/v1/chat/completions"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer YOUR_API_KEY"
    }
    data = {
        "model": "gpt-3.5-turbo",
        "messages": [{"role": "user", "content": prompt}],
        "temperature": 0.7
    }
    response = requests.post(url, headers=headers, data=json.dumps(data))
    if response.status_code == 200:
        return response.json()['choices'][0]['message']['content']
    else:
        return f"Error: {response.status_code}"

def get_narrato_content(prompt):
    """
    Function to get content from Narrato AI.
    Note: You need to replace 'YOUR_API_KEY' with your actual API key.
    """
    url = "https://api.narrato.io/v1/content"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer YOUR_API_KEY"
    }
    data = {
        "prompt": prompt,
        "tone": "informative",
        "length": "short"
    }
    response = requests.post(url, headers=headers, data=json.dumps(data))
    if response.status_code == 200:
        return response.json()['content']
    else:
        return f"Error: {response.status_code}"

def get_rytr_content(prompt):
    """
    Function to get content from Rytr API.
    Note: You need to replace 'YOUR_API_KEY' with your actual API key.
    """
    url = "https://api.rytr.me/v1/generate"
    headers = {
        "Content-Type": "application/json",
        "Authentication": f"Bearer YOUR_API_KEY"
    }
    data = {
        "prompt": prompt,
        "use_case": "blog_post",
        "tone": "informative",
        "length": "short"
    }
    response = requests.post(url, headers=headers, data=json.dumps(data))
    if response.status_code == 200:
        return response.json()['content']
    else:
        return f"Error: {response.status_code}"

# Example usage
if __name__ == "__main__":
    # ChatGPT
    print("ChatGPT Response:")
    print(get_chatgpt_response("Generate a blog post about the benefits of yoga."))

    # Narrato AI
    print("\nNarrato AI Content:")
    print(get_narrato_content("Create a social media post about a new product launch."))

    # Rytr
    print("\nRytr Content:")
    print(get_rytr_content("Write an email draft for a customer support request."))

• ChatGPT: The get_chatgpt_response function sends a prompt to the ChatGPT API and returns the generated content. You need to replace YOUR_API_KEY with your actual API key.
• Narrato AI: The get_narrato_content function sends a prompt to the Narrato AI API to generate content. Replace YOUR_API_KEY with your actual API key.
• Rytr: The get_rytr_content function sends a prompt to the Rytr API to generate content. Replace YOUR_API_KEY with your actual API key.

Remember to install the requests library (pip install requests) before running this code. Also, ensure you have the necessary API keys for each service. This code provides a starting point for beginners to explore these AI tools for content generation.

 
If this is your first time running a python code, I'd suggest using Visual Studio Code. The setup is really easy and takes just 5 minutes.

Step 3: Learn Basic Content Optimization

SEO Basics

What is SEO?

SEO, or Search Engine Optimization, is the process of optimizing your content to rank higher in search engine results. Here's how you can start:

Keyword Research: Use free tools like Google Keyword Planner to find keywords relevant to your niche. For example, if you're writing about fitness, search for terms like "yoga benefits," "weight loss tips," or "home workouts." These tools will show you search volume and competition, helping you choose the right keywords.

Long-tail Keywords: These are longer, more specific phrases that are less competitive but can drive targeted traffic. For instance, "yoga for beginners at home" instead of just "yoga."

Use AI Tools: While tools like Clearscope or Article Forge are paid, you can start with free alternatives like Yoast SEO (for WordPress) or the SEO extension in Google Docs to get a feel for keyword optimization.

Sentiment Analysis

Why Sentiment Matters

Sentiment analysis helps ensure your content resonates with the intended audience. Here's how to approach it:

Understand Your Audience: Know the emotional tone your clients want. For example, if you're writing for a fitness blog, the tone might be motivational and positive.

Use Free Tools: Tools like TextBlob or MonkeyLearn offer free sentiment analysis APIs. You can use these to analyze your generated content and adjust the tone accordingly.

Read and Analyze: Read content in your niche to understand the common sentiment. For instance, fitness blogs often use positive, encouraging language.

Adjust Your AI Prompts: When using AI tools like ChatGPT, include instructions on the desired sentiment in your prompts. For example, "Write a blog post about yoga benefits in a positive, uplifting tone."

Step 4: Practice with Real Examples

Pick a Niche

Why Choose a Niche?

Focusing on a specific niche helps you become an expert in that area, making your content more valuable. Here are some ideas:

Fitness Blogs: Write about yoga, weight loss, or home workouts.

E-commerce Product Descriptions: Describe products in a way that highlights their benefits and features.

Tips:

Start Small: Choose a niche you're interested in or have some knowledge about. This makes content creation easier and more enjoyable.

Research: Look at existing content in your chosen niche to understand what works and what doesn't.

Use AI Tools to Create Sample Content

How to Use AI Tools:

ChatGPT: Use it to generate blog posts, social media captions, or product descriptions. For example, "Generate a blog post about the benefits of yoga for beginners."

Narrato AI: Automate content creation for social media or blogs. For instance, "Create a social media post about a new fitness app."

Rytr: Use templates to generate short-form content like ads or emails. For example, "Write an email draft for a customer support request."

Tips:

Experiment: Try different prompts and see what kind of content the AI generates. Adjust your prompts to get the desired results.

Quality Check: Always review and edit AI-generated content to ensure it meets your standards and aligns with your brand voice.

Optimize Using Free SEO Tools

How to Optimize:

Google Keyword Planner: Use this to find keywords and optimize your content. For example, if you're writing about yoga, include keywords like "yoga for beginners," "yoga poses," or "yoga benefits."

Yoast SEO: If you're using WordPress, this plugin provides real-time SEO suggestions as you write.

But you'll need to learn more about SEO in general as well, knowledge doesn't hurt.

Keyword Density: Aim for a natural keyword density (1-2% of your content). Over-optimization can lead to penalties.

Readability: Ensure your content is easy to read. Use short sentences, subheadings, and bullet points to break up text.

By following these steps, you'll be well on your way to creating optimized, engaging content that can attract clients and boost your side hustle in machine learning content generation. 

Before going any further, here's what you'll learn: how to create an eye-catching portfolio using free tools like Canva, and how to set up profiles on freelance platforms that attract clients looking for AI-generated content. These steps are crucial for showcasing your skills and finding your first paying clients.

Step 5: Build a Portfolio That Showcases Your ML Content Skills

Why You Need a Portfolio

A portfolio serves as your digital resume, giving potential clients a taste of what you can do. Even if you're just starting out, having a few quality samples makes a huge difference in landing your first gigs.

Creating Your Portfolio with Canva (The Easy Way)

Canva is perfect for beginners because it's free, user-friendly, and has tons of professional templates. Here's how to create your portfolio:

Sign up for a free Canva account: Head to Canva.com and create an account using your email address.

Choose a portfolio template: After logging in, search for "portfolio" in the templates section. You'll find plenty of free options designed for showcasing creative work.

Decide on format: Determine whether you want a single-page portfolio or a multi-page presentation. For beginners, a simple multi-page presentation works well to showcase different types of content.

Customize your template: Replace the placeholder text with your information. Include your name, a brief bio highlighting your AI content generation skills, and contact details.

Add your work samples: Create separate sections for different types of content - blog posts, social media captions, product descriptions. For each sample, include a brief description of the process you used to generate and optimize it.

When designing your portfolio, keep these tips in mind:

Use consistent colors and fonts: Stick to 2-3 colors and fonts to maintain a professional look.

Include before-and-after examples: Show how you've improved content using AI tools and optimization techniques.

Keep it simple: Don't overwhelm viewers with too much text or complex designs, especially when you're just starting out.

Make it easy to navigate: Use clear section headings and a logical flow from one page to the next.

Remember, your portfolio doesn't need to be perfect right away. Start with 3-5 strong examples and update it as you create more content.

Step 6: Offer Services on Freelance Platforms

Once your portfolio is ready, it's time to find clients by setting up profiles on freelance platforms.

Choosing the Right Platform

Each platform has its strengths:

Upwork: Great for finding longer-term clients and building relationships. Clients typically post jobs, and you submit proposals.

Fiverr: Better for offering specific, packaged services (called "Gigs"). Clients come to you based on your service listings.

LinkedIn: Perfect for networking and finding clients in specific industries. Less commission fees than dedicated freelance platforms.

For beginners, I recommend starting with either Upwork or Fiverr, as they have more straightforward processes for finding your first clients.

Creating a Standout Profile

Here's how to set up an attractive profile on these platforms:

Choose a professional profile picture: Use a clear, well-lit headshot that looks approachable and professional.

Create a compelling profile title: Make it specific to AI content generation. Instead of "Content Writer," try "AI-Powered Content Generator & SEO Optimizer".

Write a powerful overview/description: In the first 250 characters (what shows before clients click "more"), highlight your AI content skills and what makes you different6. For example: "I use advanced ML tools to create engaging, SEO-optimized content that saves you time and drives traffic. Specializing in [your niche] content."

Set competitive rates: As a beginner, start with slightly lower rates to build reviews. Research what others with similar skills charge and position yourself accordingly.

Highlight your services: Be specific about what you offer - blog post generation, product descriptions, social media content, SEO optimization.

Standing Out to Clients

To attract your first clients:

Be personal in your proposals, take time to research each client and reference specific details about their business. Generic templates are immediately obvious and often ignored.

Focus on client benefits instead of talking about your skills, explain how you'll save them time, money, or effort. For example: "I can deliver 5 SEO-optimized blog posts per week, saving you 10+ hours of writing and research".

Offer value upfront in your proposals, include a quick tip or suggestion related to their project. This demonstrates your expertise and willingness to help.

Target small businesses and solopreneurs because these clients often need affordable content solutions and are more willing to work with beginners. Look for job postings that mention "limited budget" or "startup".

Start with smaller projects to build confidence and reviews by taking on manageable projects like single blog posts or social media captions before pitching for larger contracts.

Remember that landing your first client takes persistence. You might need to send 10-20 proposals before getting your first gig. Don't get discouraged - each proposal is practice for the next one!

You'll be well on your way to building a profitable side hustle in AI content generation. The key is to start small, deliver quality work, collect reviews, and gradually expand your services as you gain confidence and expertise.