First Step Towards Practically Learning Artificial Intelligence

I have written a blog about what Artificial Intelligence is and how it is revolutionising every field. But it is all theoretical. I was confused when I started learning AI. Andrew Ng is one of the pioneers in the industry and I pursued his course on Coursera, "AI for Everyone". And later did a course, 'Elements of AI', offered by Reaktor and University of Helsinki. Through these, I understood what AI is, theoretically. But I still didn't know where to practically start my journey in this awesome field. Here I have written this blog, as a beginner guide (I am a beginner too), and we can then start our exploration through YouTube or online courses.

What is AI?

AI refers to the simulation of human intelligence processes by machines, encompassing tasks such as learning, reasoning, problem-solving, and decision-making. To begin your journey into AI, it's essential to grasp fundamental concepts like machine learning, deep learning, neural networks, and natural language processing.

Where to Start Learning AI

Starting your AI journey begins with building a solid foundation in fundamental concepts. Begin by understanding key concepts such as machine learning, deep learning, neural networks, and natural language processing (NLP). These concepts form the backbone of AI and provide the necessary framework for advanced learning. For instance, machine learning focuses on algorithms that enable machines to learn from data and make predictions, while deep learning involves neural networks capable of learning intricate patterns and features from vast datasets.

For beginners, starting with online resources and courses can provide a structured introduction to AI concepts. Platforms like Coursera, edX, and Udacity offer beginner-friendly courses that cover AI fundamentals, algorithms, and applications. Additionally, exploring online AI communities and forums can help us stay updated on industry trends and connect with fellow learners and experts.

Languages Needed for AI

Proficiency in programming languages is crucial for AI development. Python, with its simplicity, versatility, and rich libraries like TensorFlow and PyTorch, is widely preferred for AI projects. Learning Python basics and familiarizing yourself with AI libraries can set a solid foundation for your AI journey. As a beginner, experimenting with basic AI programs can enhance your understanding and practical skills. Start with simple projects like creating a chatbot using natural language processing libraries, building a digit recognition model with machine learning algorithms, or implementing a basic neural network for image classification.

Example 1:

To illustrate, consider a simple Python code snippet for building a basic linear regression model:

import numpy as np
from sklearn.linear_model import LinearRegression

# Sample data
x = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])

# Initialize and fit the model
model = LinearRegression().fit(x, y)

# Predict using the model
predicted_values = model.predict([[6]])
print(predicted_values)  => # Output: [12]

Explanation:

1. Sample Data

   - Input data 'x': [[1], [2], [3], [4], [5]]

   - Corresponding output data 'y': [2, 4, 6, 8, 10]

2. Model Training

The program initializes a linear regression model (LinearRegression()) and fits it to the sample data (x, y). This process involves finding the best-fitting line (or hyperplane in higher dimensions) that minimizes the sum of squared differences between the actual output values 'y' and the predicted values from the model.

3. Prediction

After training the model, the program predicts the output for a new input value of `6` using the trained model. The prediction is made with the line:

predicted_values = model.predict([[6]])

Here, the input value 6 is passed as an array [[6]] because scikit-learn's 'predict' method expects input data in the form of a 2D array, even for single feature inputs.

4. Output

The predicted output value for the input 6 is calculated by the trained model based on the linear relationship it learned during training. Since the input-output relationship in the sample data is linear (each input 'x' is doubled to get the output 'y'), the predicted value for input 6 would also follow this linear relationship.

Mathematically, the model has learned that the output 'y' is twice the input 'x', i.e., y = 2x. Therefore, when the input 'x' is 6, the predicted output 'y' is 2 * 6 = 12.

So, the output of 12 is obtained because the linear regression model has learned a linear relationship where the output is twice the input, and this relationship is used to predict the output for the input value 6.

Example 2:

Before trying this example, I thought sentiment analysis was a huge process. But just by using the Python library, it becomes a simple program. (But it is a difficult task for those who developed that library though). A simple sentiment analysis project using Python's NLP libraries like NLTK or spaCy. Sentiment analysis involves classifying text data into positive, negative, or neutral sentiments.

Here's an example of sentiment analysis using NLTK:

import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer

# Initialize sentiment analyzer
sid = SentimentIntensityAnalyzer()

# Sample text for analysis
text = "This movie is fantastic! I absolutely loved it."

# Analyze sentiment
sentiment_score = sid.polarity_scores(text)

# Print sentiment score
print(sentiment_score)  # Output: {'neg': 0.0, 'neu': 0.296, 'pos': 0.704, 'compound': 0.8016}

Explanation:

1. Importing Libraries

	'import nltk'

This imports the Natural Language Toolkit (NLTK) library, which is a popular library for natural language processing tasks in Python.

from nltk.sentiment.vader import SentimentIntensityAnalyzer 

This line specifically imports the SentimentIntensityAnalyzer class from NLTK's Vader module, which is used for sentiment analysis.

2. Initializing Sentiment Analyzer

sid = SentimentIntensityAnalyzer()

This line initializes an instance of the 'SentimentIntensityAnalyzer' class, which is used to analyse the sentiment (positive, negative, or neutral) of text.

3. Sample Text for Analysis

text = "This movie is fantastic! I absolutely loved it." 

This line assigns a sample text to the variable 'text'. The text is a positive sentiment statement about a movie, indicating that the speaker found the movie fantastic and loved it.

4. Analyzing Sentiment

sentiment_score = sid.polarity_scores(text) 

This line analyses the sentiment of the sample text. The 'polarity_scores' method returns a dictionary containing sentiment scores for the text, including scores for negative, neutral, positive, and compound sentiments.

5. Printing Sentiment Score

This line prints the sentiment scores dictionary obtained from the sentiment analysis. The output is in the format:

     {'neg': 0.0, 'neu': 0.296, 'pos': 0.704, 'compound': 0.8016}

neg: The negativity score, which is 0.0 in this case, indicating no negative sentiment in the text.

neu: The neutrality score, which is 0.296 in this case, indicating a moderate level of neutral sentiment in the text.

pos: The positivity score, which is 0.704 in this case, indicating a high level of positive sentiment in the text.

compound: The compound score, which is 0.8016 in this case, is a normalized score that combines the scores of neg, neu, and pos. It ranges from -1 (extremely negative) to 1 (extremely positive), where values closer to 1 indicate more positive sentiment.

There are even more common AI projects like Handwritten digit recognition, Stock price prediction, image classification, movie recommendation system and more.

Best Online Courses Available

Some online certification courses that can help improve skills in AI.

Coursera: Machine Learning by Stanford University, Deep Learning Specialization by deeplearning.ai, Natural Language Processing Specialization by deeplearning.ai, Reinforcement Learning Specialization by University of Alberta, IBM Data Science Professional Certificate

edX: Artificial Intelligence by Columbia University, Machine Learning by Microsoft, Deep Learning with Python and PyTorch by IBM

Udacity: Artificial Intelligence Nanodegree, Machine Learning Engineer Nanodegree, Deep Learning Nanodegree

Google: TensorFlow Developer Certificate, TensorFlow Advanced Certification, Google Cloud AI Certification

MIT OpenCourseWare: Introduction to Deep Learning, Introduction to Computational Thinking and Data Science

Kaggle Courses: Intro to Machine Learning, Intermediate Machine Learning, Natural Language Processing

Fast.ai: Practical Deep Learning for Coders

Microsoft Learn: AI-900: Microsoft Azure AI Fundamentals, DP-100: Designing and Implementing a Data Science Solution on Azure

Amazon AWS Training and Certification: AWS Certified Machine Learning Specialty

These courses cover various topics such as machine learning, deep learning, natural language processing, reinforcement learning, computer vision, and more.

In conclusion, start learning today. There are a lot of YouTube channels and online platforms offering free education. Try out examples. Practical learning is always the best. Never do certification courses just for earning a certificate. After completing a course, we should try projects on our own. This is not just for AI related courses, it applies for learning in general. Pursuing online certification courses in AI is a great way to enhance our skills and stay updated with the latest advancements in artificial intelligence. Online courses offer a structured learning path with hands-on projects, expert instruction, and industry-recognized certifications, making them valuable assets for anyone looking to build a career in AI or enhance their existing skills. So, start exploring today and let's take our AI skills to the next level!

Image source: MJH Shikder from Pixabay