This repository contains note and references of Google Developer Student Club - ML Study Jams 2024 conducted at Charusat University, Anand, India on 12th and 13th March 2024
Notion Notes --> Link
AI stands for Artificial Intelligence. Artificial Intelligence is a branch of study which focuses on understanding and developing Machines which think, learn and act like human. These type of machines can help solve problems, learn and explain information, analyze a situation and draw conclusion to it.
ML stands for Machine Learning. Machine Learning are the model and algorithms which help a machine learn new things. ML algorithms are algorithms which are used to train a model for future use.
DL stands for Deep Learning. Deep Learning is a subfield of ML which uses neutral networks to make a machine understand and process like human brain.
- Based on the training and test data set there are ML models get divided into 3 categories
- Overfitting → Training Data set too big, Test Data set too small.
- Underfitting → Training Data set too small, Test Data set too big.
- Optimal → Training Data set normal, Test Data set normal.
- Model with too high accuracy and hard approach are called overfitting algorithms.
- These algorithms try to cover all the possibility.
- This type of model has low bais and high or very high variance.
- The behaviour of the overfitting model can be represented by the following graph.
- Underfitting models have very low accuracy.
- This type of model doesn’t capture the underlying trend of the data.
- This type of model has high or very high bais and low variance.
- The behaviour of the underfitting model can be represented by the following graph.
- Optimised ML model is the most appropriate model for normal use as it as appropriate bais and appropriate variance.
- This model tries to generize the result.
- The behaviour of optimised model can be understood by following graph.
- The behaviour of all three types of model is represented here.
Classification ⇒ It clearly defines the state of result (between 0 to 1)
Regression ⇒ It predicts the state of result (0 or 1)
-
Behaviour → Straight line progress
-
Loss Function → Reviews the model and give a detailed feedback to the model
-
Equation → y = mx+c
y ⇒ output of model
m ⇒ slope
-
Error = Loss function - y
- Behaviour → Non-linear regression (Can be wavy line progression)
- Activation function → This function is used to convert the result of the model in the range of 0 and 1.
- Behaviour → Non-linear (Can be of any shape (circle, square, etc.))
- Support vector maximize the distance between the 2 or more rival groups by adding neutral group between them.
- Example → We have to arrange seating arrangement of 2 rival polical parties in an events, then we will make a party sit on extreme corner, the other party on the other corner and people not beloning to any party will be seated between them.
Now as soon as we give a prompt to a ML model it does 3 things
- NLU → Natural Language Understanding
- NLP → Natural Language Processing
- NLG → Natural Language Generation
-
This step divides prompt into tokens
-
Tokenization can be divided into 2 steps -
Divide prompt into smaller sentence
Divide sentence token into word token
!pip install nltk # '!' is used to install nltk in the working environment.
import nltk # Natural Language Tokenization Keras.
# nltk.download('punkt') # Uncomment this line in case of error.
from nltk.tokenize import word_tokenize # Used for Word Tokenization.
from nltk.tokenize import sent_tokenize # Used for Sentence Tokenization.
prompt = "Hi everyone, I am Harsh from CSPIT IT 4th semester. I had attended a super awesome GDSC event. Name of the event was GDSC ML Study Jams." # Assigned a string to 'prompt' variable.'
TokenizedSentence = sent_tokenize(prompt) # 'sent_tokenize' function will tokenize the prompt into sentence tokens.
print(TokenizedSentence) # Output -> ['Hi everyone, I am Harsh from CSPIT IT 4th semester.', 'I had attended a super awesome GDSC event.', 'Name of the event was GDSC ML Study Jams.']
TokenizedWord = word_tokenize(prompt) # 'word_tokenize' function will tokenize the prompt into word tokens.
print(TokenizeWord) # Output -> ['Hi', 'everyone', ',', 'I', 'am', 'Harsh', 'from', 'CSPIT', 'IT', '4th', 'semester', '.', 'I', 'had', 'attended', 'a', 'super', 'awesome', 'GDSC', 'event', '.', 'Name', 'of', 'the', 'event', 'was', 'GDSC', 'ML', 'Study', 'Jams', '.']- This step excludes unwanted tokens from the prompt.
- Stop words are words which are not too useful like a, the, etc. which can be ignored by the ML program
!pip install nltk
import nltk
nltk.download('stopwords') # stopwords is a module which stores stopwords, Stopwords -> words which don't hold meaning and are just used for grammar
from nltk.corpus import stopwords # Corpus is used to store data, Data -> Corpus(Latten)
message = "Hi everyone, I am Harsh Murjani. Nice to see you all!" # Assigns the string in variable 'message'
stopwords_list = set(stopwords.words('english')) # Convert stopwords to a set for efficiency
filtered_message = [word for word in message.split() if word not in stopwords_list] # Filter outs the stopwords from message by comparing each word in message to each word in stopwords_list
print(filtered_message) # Output -> ['Hi','everyone,'I','Harsh','Murjani','Nice','see','all!']- Stemmatization and lemmatization both are used to get the stem word of the word token
Stemmatization
- Stemmatization is faster as compared to lemmatization.
- Stemmatized is not advised if we have to translate a prompt.
- Stemmatization has less accuracy as compared to Lemmatization.
Lemmatization
- Lemmatization is slower as compared to stemmatization.
- Lemmatization is not adviced if we have to summarize a prompt.
- Lemmatization has more accuracy.
- It is the most commonly used Stemmer.
!pip install nltk
import nltk
from nltk.stem import PorterStemmer # PorterStemmer is method of stemmatization
stemmer = PorterStemmer() # Assigns the function defination of PorterStemmer to stemmer for convince of use
'''Case - 1 -> Stemmatizing single words'''
print(stemmer.stem("sitting")) # The output will be -> sit
'''Case - 2 -> Stemmatizing bulk words'''
for word in words: # This will iterate through the word list
print(word+" -> "+stemmer.stem(word)) # This will print the stem words of the given word list- RegExpStemmer is Regular Expression Stemmer which is used to remove any prefix or suffix of a word token.
- This is a customisable stemmatization function.
!pip install nltk
import nltk
from nltk.stem import RegexpStemmer
stemmer = RegexpStemmer('ing$|s$', min=4) # This will assign the function defination of RegexpStemmer to stemmer which will tell stemmer to check 2 conditions, 1st -> if a word ends with ing or s the stemmer will remove ing or s to get the stem word, 2nd -> the length of the word must be minimum of 4 characters.
'''Case - 1 -> Stemmatizing single words'''
print(stemmer.stem("eating")) # Length is 6, so word will be stemmed #The output will be -> eat
print(stemmer.stem("roses")) # Length is 5, so word will be stemmed #The output will be -> rose
'''Case - 2 -> Stemmatizing bulk words'''
for word in words: # This will iterate through the word list
print(word+" -> "+stemmer.stem(word)) # This will print the stem words of the given word list- It is a better version of Porter Stemmer
!pip install nltk
import nltk
from nltk.stem import SnowballStemmer
stemmer = SnowballStemmer('english') # This assigns the function defination of Snowball Stemmer to stemmer for english language, this is also done for convince of use
'''Case - 1 -> Stemmatizing single words'''
print(stemmer.stem("fairly")) # The output will be -> fair #If we apply PorterStemmer on this word the output will be fairli
'''Case - 2 -> Stemmatizing bulk words'''
for word in words: # This will iterate through the word list
print(word+" -> "+stemmer.stem(word)) # This will print the stem words of the given word list- One hot ecoding algorithm uses sparse matrix to convert word into vector
- It assigns a unique vector to each category in a categorical variable.
- All elements in the vector are set to 0, except for the position corresponding to the category the data point belongs to, which is set to 1.
Category → E-Commerece Products
Type 1 → Clothes [1,0,0,0]
Type 2 → Electronics [0,1,0,0]
Type 3 → Books [0,0,1,0]
Type 4 → Pharmacy [0,0,0,1]
!pip install pandas
import pandas as pd
data_list = ['Clothes', 'Electronics', 'Books', 'Pharmacy'] # Defines the categories for products like clothes, electronics, etc. in a list.
data_set = pd.DataFrame(data_list, columns=['Product']) # This uses a function in Pandas library to create DataFrames of the list # This is done because get_dummies function on works on DataFrame
DF = pd.get_dummies(data_set, columns=['Product']) # This uses a function get_dummies to get OHE of Product column from the DataFrame called data_set
print(DF)
'''
The Output of this program will be:
Product_Books Product_Clothes Product_Electronics Product_Pharmacy
0 False True False False
1 False False True False
2 True False False False
3 False False False True
'''Category 1 → Flower
Type 1 → Rose
Type 2 → Tulip
Type 3 → Lily
Type 4 → Sunflower
Type 5 → Daffodil
Category 2 → Color
Type 1 → Red
Type 2 → Green
Type 3 → Blue
!pip install pandas
import pandas as pd
data_list = [['Rose','Red'],['Tulip','Green'],['Lily','Blue'],['Sunflower',''],['Daffodil','']] # This creates a list with subsets ['Rose','Red'],['Tulip','Green'] and so on..
data_set = pd.DataFrame(data_list, columns=['Flower','Color']) # This uses a function in Pandas library to create DataFrames of the subsets in data_list # This is done because get_dummies function on works on DataFrame
DF = pd.get_dummies(data_set, columns=['Flower','Color']) # This uses a function get_dummies to get OHE of Flower and Color column from the DataFrame called data_set
print(DF)
'''
The output of the above code will be similar to this
Flower_Daffodil Flower_Lily Flower_Rose Flower_Sunflower Flower_Tulip Color_ Color_Blue Color_Green Color_Red
0 False False True False False False False False True
1 False False False False True False False True False
2 False True False False False False True False False
3 False False False True False True False False False
4 True False False False False True False False False
'''- This creates a vector which will store the number of occurance of a word.
Sentence 1 → This book is good novel
Sentence 2 → This book is science reference book
Sentence 3 → This book is good science fiction novel
'''
The vector matrix of the above example can be represented by
this book is good novel science reference fiction
1 1 1 1 1 1 0 0 0
2 1 2 1 0 0 1 1 0
3 1 1 1 1 1 1 0 1
'''sentences = [
"This book is good novel",
"This book is science reference book",
"This book is good science fiction novel"
] # Assigns sentences to 'sentences' variable
tokenized_sentences = [sentence.lower().split() for sentence in sentences] # This tokenises the sentences and convert them to lowercase to avoid similar duplicates # If lowering is not done Book and book are considered 2 different tokens
vocabulary = set() # Creates an empty set
for sentence in tokenized_sentences:
for word in sentence:
vocabulary.add(word) # This will add uniques words to vocabulary set
# vocabulary -> [[this],[book],[is],[good],[novel],[science],[reference],[fiction]]
BoW = {} # This creates a empty set to store result called 'BoW' -> Bag of words # Example -> (you can have a look at output)
for sentence in tokenized_sentences: # This will iterate for every sentence in 'tokenized_sentences' -> In our case it will iterate 3 times, because we have 3 sentences
word_counts = {} # This creates a empty set which will store the word and number of occurrence of that word in sentence called 'word_counts' # Example -> {'this':1,'book':1}
for word in sentence: # This will iterate through every word in a sentence
if word in word_counts: # This will check if the current word has already occured in sentence or not
word_counts[word] += 1 # This will add 1 to 'word_counts' if the word has already occured
else:
word_counts[word] = 1 # This will assign 1 to the 'word_counts' indicating the occurrence of the word is once.
BoW[tuple(sentence)] = word_counts # This will convert the 'word_counts' into tuple (from list) and append it to 'BoW' # If we don't convert it to tuple we are unable to directly print the result.
for sentence, word_counts in BoW.items(): # This will iterate through 'sentence' and 'word_counts' in 'BoW' variable
print(f"Sentence: {' '.join(sentence)}") # This will print the current sentence
print(f"Word Counts: {word_counts}") # This will print the 'word': occurence_of_word in the current sentence.
print() # This will add a \n to make the output more clearer
'''
The output will be
Sentence: this book is good novel
Word Counts: {'this': 1, 'book': 1, 'is': 1, 'good': 1, 'novel': 1}
Sentence: this book is science reference book
Word Counts: {'this': 1, 'book': 2, 'is': 1, 'science': 1, 'reference': 1}
Sentence: this book is good science fiction novel
Word Counts: {'this': 1, 'book': 1, 'is': 1, 'good': 1, 'science': 1, 'fiction': 1, 'novel': 1}
'''- This is similar to BoW algorithm but the difference is that this algorithm converts the occurence into fractions to avoid null elements.
Sentence 1 → Cat eats food
Sentence 2 → Bat eats food
Sentence 3 → Dog eats food
The matrix for the above example will be
'''
cat bat dog eats food
1/3 1/3 1/3 3/3 3/3
'''from math import log2
# Define sentences
sentences = [
"Cat eats food",
"Bat eats food",
"Dog eats food"
]
# Function to calculate Term Frequency (TF)
def calculate_tf(word, sentence):
"""Calculates the TF of a word in a sentence."""
word_count = sentence.count(word)
total_words = len(sentence.split())
return word_count / total_words
# Function to calculate Document Frequency (DF)
def calculate_df(word, sentences):
"""Calculates the DF of a word in a corpus of sentences."""
document_count = 0
for sentence in sentences:
if word in sentence:
document_count += 1
return document_count
# Function to calculate Inverse Document Frequency (IDF)
def calculate_idf(word, sentences):
"""Calculates the IDF of a word in a corpus of sentences."""
total_documents = len(sentences)
df = calculate_df(word, sentences)
if df == 0:
# Avoid division by zero (words appearing in all documents)
return 0
return log2(total_documents / df)
# Function to calculate TF-IDF
def calculate_tfidf(word, sentence, sentences):
"""Calculates the TF-IDF of a word in a sentence."""
tf = calculate_tf(word, sentence)
idf = calculate_idf(word, sentences)
return tf * idf
# Calculate TF-IDF for each word in each sentence
word_tfidf = {}
for sentence in sentences:
for word in sentence.split():
tfidf = calculate_tfidf(word, sentence, sentences)
# Store TF-IDF in a dictionary
if word not in word_tfidf:
word_tfidf[word] = {}
word_tfidf[word][sentence] = tfidf
# Print Results
print("TF-IDF Scores:")
for word, sentence_tfidfs in word_tfidf.items():
print('')
print(f"Word: {word}")
for sentence, tfidf in sentence_tfidfs.items():
print(f"\tSentence: '{sentence}', TF-IDF: {tfidf:.4f}")
'''
The output for the above code will be
TF-IDF Scores:
Word: Cat
Sentence: 'Cat eats food', TF-IDF: 0.5283
Word: eats
Sentence: 'Cat eats food', TF-IDF: 0.0000
Sentence: 'Bat eats food', TF-IDF: 0.0000
Sentence: 'Dog eats food', TF-IDF: 0.0000
Word: food
Sentence: 'Cat eats food', TF-IDF: 0.0000
Sentence: 'Bat eats food', TF-IDF: 0.0000
Sentence: 'Dog eats food', TF-IDF: 0.0000
Word: Bat
Sentence: 'Bat eats food', TF-IDF: 0.5283
Word: Dog
Sentence: 'Dog eats food', TF-IDF: 0.5283
'''- Has semantic meaning.
- Easy to generate
- Is a pretrained model
- It uses Neutral Network
Sentence → I am here for machine learning
from gensim.models import Word2Vec
# Define the input sentence
sentence = "I am here for machine learning"
# Tokenize the sentence into words
words = sentence.split()
# Train the Word2Vec model
model = Word2Vec([words], vector_size=100, window=5, min_count=1, sg=0)
# Retrieve the word vectors
word_vectors = model.wv
# Print word vectors
for word in words:
print(f"\nWord: {word}\nVector: \n{word_vectors[word]}")
'''
Output ->
Word: I
Vector:
[-8.7274825e-03 2.1301615e-03 -8.7354420e-04 -9.3190884e-03
-9.4281426e-03 -1.4107180e-03 4.4324086e-03 3.7040710e-03
-6.4986930e-03 -6.8730675e-03 -4.9994122e-03 -2.2868442e-03
-7.2502876e-03 -9.6033178e-03 -2.7436293e-03 -8.3628409e-03
-6.0388758e-03 -5.6709289e-03 -2.3441375e-03 -1.7069972e-03
-8.9569986e-03 -7.3519943e-04 8.1525063e-03 7.6904297e-03
-7.2061159e-03 -3.6668312e-03 3.1185520e-03 -9.5707225e-03
1.4764392e-03 6.5244664e-03 5.7464195e-03 -8.7630618e-03
-4.5171441e-03 -8.1401607e-03 4.5956374e-05 9.2636338e-03
5.9733056e-03 5.0673080e-03 5.0610625e-03 -3.2429171e-03
9.5521836e-03 -7.3564244e-03 -7.2703874e-03 -2.2653891e-03
-7.7856064e-04 -3.2161034e-03 -5.9258583e-04 7.4888230e-03
-6.9751858e-04 -1.6249407e-03 2.7443992e-03 -8.3591007e-03
7.8558037e-03 8.5361041e-03 -9.5840869e-03 2.4462664e-03
9.9049713e-03 -7.6658037e-03 -6.9669187e-03 -7.7365171e-03
8.3959233e-03 -6.8133592e-04 9.1444086e-03 -8.1582209e-03
3.7430846e-03 2.6350426e-03 7.4271322e-04 2.3276759e-03
-7.4690939e-03 -9.3583735e-03 2.3545765e-03 6.1484552e-03
7.9856887e-03 5.7358947e-03 -7.7733636e-04 8.3061643e-03
-9.3363142e-03 3.4061326e-03 2.6675343e-04 3.8572443e-03
7.3857834e-03 -6.7251669e-03 5.5844807e-03 -9.5222248e-03
-8.0445886e-04 -8.6887367e-03 -5.0986730e-03 9.2892265e-03
-1.8582619e-03 2.9144264e-03 9.0712793e-03 8.9381328e-03
-8.2084350e-03 -3.0123137e-03 9.8866057e-03 5.1044310e-03
-1.5880871e-03 -8.6920215e-03 2.9615164e-03 -6.6758976e-03]
Word: am
Vector:
[-0.00713902 0.00124103 -0.00717672 -0.00224462 0.0037193 0.00583312
0.00119818 0.00210273 -0.00411039 0.00722533 -0.00630704 0.00464722
-0.00821997 0.00203647 -0.00497705 -0.00424769 -0.00310898 0.00565521
0.0057984 -0.00497465 0.00077333 -0.00849578 0.00780981 0.00925729
-0.00274233 0.00080022 0.00074665 0.00547788 -0.00860608 0.00058446
0.00686942 0.00223159 0.00112468 -0.00932216 0.00848237 -0.00626413
-0.00299237 0.00349379 -0.00077263 0.00141129 0.00178199 -0.0068289
-0.00972481 0.00904058 0.00619805 -0.00691293 0.00340348 0.00020606
0.00475375 -0.00711994 0.00402695 0.00434743 0.00995737 -0.00447374
-0.00138926 -0.00731732 -0.00969783 -0.00908026 -0.00102275 -0.00650329
0.00484973 -0.00616403 0.00251919 0.00073944 -0.00339215 -0.00097922
0.00997913 0.00914589 -0.00446183 0.00908303 -0.00564176 0.00593092
-0.00309722 0.00343175 0.00301723 0.00690046 -0.00237388 0.00877504
0.00758943 -0.00954765 -0.00800821 -0.0076379 0.00292326 -0.00279472
-0.00692952 -0.00812826 0.00830918 0.00199049 -0.00932802 -0.00479272
0.00313674 -0.00471321 0.00528084 -0.00423344 0.0026418 -0.00804569
0.00620989 0.00481889 0.00078719 0.00301345]
Word: here
Vector:
[-8.2426779e-03 9.2993546e-03 -1.9766092e-04 -1.9672764e-03
4.6036304e-03 -4.0953159e-03 2.7431143e-03 6.9399667e-03
6.0654259e-03 -7.5107943e-03 9.3823504e-03 4.6718083e-03
3.9661205e-03 -6.2435055e-03 8.4599797e-03 -2.1501649e-03
8.8251876e-03 -5.3620026e-03 -8.1294188e-03 6.8245591e-03
1.6711927e-03 -2.1985089e-03 9.5136007e-03 9.4938548e-03
-9.7740470e-03 2.5052286e-03 6.1566923e-03 3.8724565e-03
2.0227872e-03 4.3050171e-04 6.7363144e-04 -3.8206363e-03
-7.1402504e-03 -2.0888723e-03 3.9238976e-03 8.8186832e-03
9.2591504e-03 -5.9759365e-03 -9.4026709e-03 9.7643770e-03
3.4297847e-03 5.1661171e-03 6.2823449e-03 -2.8042626e-03
7.3227035e-03 2.8302716e-03 2.8710044e-03 -2.3803699e-03
-3.1282497e-03 -2.3701417e-03 4.2764368e-03 7.6057913e-05
-9.5842788e-03 -9.6655441e-03 -6.1481940e-03 -1.2856961e-04
1.9974159e-03 9.4319675e-03 5.5843508e-03 -4.2906962e-03
2.7831673e-04 4.9643586e-03 7.6983096e-03 -1.1442233e-03
4.3234206e-03 -5.8143795e-03 -8.0419064e-04 8.1000505e-03
-2.3600650e-03 -9.6634552e-03 5.7792603e-03 -3.9298222e-03
-1.2228728e-03 9.9805174e-03 -2.2563506e-03 -4.7570644e-03
-5.3293873e-03 6.9808899e-03 -5.7088719e-03 2.1136629e-03
-5.2556600e-03 6.1207139e-03 4.3573068e-03 2.6063549e-03
-1.4910829e-03 -2.7460635e-03 8.9929365e-03 5.2157748e-03
-2.1625196e-03 -9.4703101e-03 -7.4260519e-03 -1.0637414e-03
-7.9494715e-04 -2.5629092e-03 9.6827205e-03 -4.5852066e-04
5.8737611e-03 -7.4475873e-03 -2.5060738e-03 -5.5498634e-03]
Word: for
Vector:
[ 9.4563962e-05 3.0773198e-03 -6.8126451e-03 -1.3754654e-03
7.6685809e-03 7.3464094e-03 -3.6732971e-03 2.6427018e-03
-8.3171297e-03 6.2054861e-03 -4.6373224e-03 -3.1641065e-03
9.3113566e-03 8.7338570e-04 7.4907029e-03 -6.0740625e-03
5.1605068e-03 9.9228229e-03 -8.4573915e-03 -5.1356913e-03
-7.0648370e-03 -4.8626517e-03 -3.7785638e-03 -8.5361991e-03
7.9556061e-03 -4.8439382e-03 8.4236134e-03 5.2625705e-03
-6.5500261e-03 3.9578713e-03 5.4701497e-03 -7.4265362e-03
-7.4057197e-03 -2.4752307e-03 -8.6257253e-03 -1.5815723e-03
-4.0343284e-04 3.2996845e-03 1.4418805e-03 -8.8142155e-04
-5.5940580e-03 1.7303658e-03 -8.9737179e-04 6.7936908e-03
3.9735902e-03 4.5294715e-03 1.4343059e-03 -2.6998555e-03
-4.3668128e-03 -1.0320747e-03 1.4370275e-03 -2.6460087e-03
-7.0737829e-03 -7.8053069e-03 -9.1217868e-03 -5.9351693e-03
-1.8474245e-03 -4.3238713e-03 -6.4606704e-03 -3.7173224e-03
4.2891586e-03 -3.7390434e-03 8.3781751e-03 1.5339935e-03
-7.2423196e-03 9.4337985e-03 7.6312125e-03 5.4932819e-03
-6.8488456e-03 5.8226790e-03 4.0090932e-03 5.1853694e-03
4.2559016e-03 1.9397545e-03 -3.1701624e-03 8.3538452e-03
9.6121803e-03 3.7926030e-03 -2.8369951e-03 7.1275235e-06
1.2188185e-03 -8.4583247e-03 -8.2239453e-03 -2.3101569e-04
1.2372875e-03 -5.7433806e-03 -4.7252737e-03 -7.3460746e-03
8.3286157e-03 1.2129784e-04 -4.5093987e-03 5.7017053e-03
9.1800150e-03 -4.0998720e-03 7.9646818e-03 5.3754342e-03
5.8791232e-03 5.1259040e-04 8.2130842e-03 -7.0190406e-03]
Word: machine
Vector:
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