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Artificial intelligence makes it easier to create conversion-friendly content.
Arthur Samuel introduced machine learning in 1959. Machine learning is a form of artificial intelligence that allows computers to learn without having to be programmed. It provides an array of algorithms and techniques to create computer programs that automatically improve their performance on certain tasks.
How Machine Learning is Used to Improve Content MarketingBecause machine learning helps marketers identify what customers want and what they don’t, it is a key component of content marketing. Machine learning also allows marketers to create content that is more likely to generate conversions and improve their return on investment.
As machine learning is increasingly used in content marketing, the future possibilities are endless. We can also expect AI to assume more and more of the responsibilities that marketers have.
What are The Most Effective Applications of Machine Learning in Content Marketing?Machine learning is an artificial intelligence type that uses data to make predictions. Many industries use machine learning algorithms, including finance, healthcare, and many others. Machine learning is most commonly used in content marketing.
Machine learning can be used by content marketers to improve their content and optimize marketing campaigns. Sentiment analysis is one way they do this. It allows them to see what moods readers might be in when reading their content. This allows them to write engaging copy for their readers.
Marketers can also use machine learning to predict what people want to read, using predictive analysis. This allows them to make predictions based on their day and week. This allows them to ensure that they always have the most relevant content for their audience.
What is Predictive Analytics?Predictive analytics refers to the process of extracting data from sources in order to predict the future. This is a method that allows companies to use historical data and trends to predict future outcomes.
Predictive analytics can also be used to generate content and customer engagement. It can also be used to predict customer behavior and help them with their needs. Businesses can prepare for their customers’ needs before they contact them. Predictive analytics is also useful in content generation. It predicts what content customers will like and what topics they are most interested in.
Predictive Analytics: How Machine Learning can Assist
Machine learning, a subset of artificial intelligence, is used to assist with predictive analytics. Machine learning provides insight into the future to support business decisions. Machine learning has been used to predict the future of the stock market for many years. Machine learning is also being used to predict content.
Conclusion: Machine Learning can be Used to Boost Your Content Creation Efforts
Machine learning is the future. This will allow you to create content that is meaningful and resonates with your audience. It is a great tool to boost your content creation.
You're reading Machine Learning: What Is Machine Learning, And How It Is Help With Content Marketing?
Machine Learning Is Growing Significantly In Business
The improvements in technology and the availability of Machine Learning capacities, such as TensorFlow or Cloud services like Google Cloud AI. This operational tool such as Talent has helped to improve the skills and adopt the Machine Learning theories and accelerate delivery of alternatives.
Machine Learning is growing rapidlyThe key reasons for this would be the improvements in accessibility and costs of information storage and calculate, with much more availability to Machine Learning capacities. Because this generated the perfect storm for businesses research on how to exploit this field of Data Science. But, Machine Learning remains basically about statistical modelling using information – so the information remains crucial. Data Science, as well as the areas about Machine Learning, are in high demand in most businesses that are driving this momentum in the programmer level to educate and empower that alignment with business opportunities or goals could be confirmed.
We’re also beginning to find some revolutionary applications of Machine Learning within our client base as businesses begin as a consequence of the lowering of several obstacles to its adoption. Also, E-commerce sites through indicating next greatest actions (NBA) in gambling and gambling platforms to forecasting supply chain demands based on additional measurements like key and weather events, our clients are researching Machine Learning initiatives might help improve the consumer and client experience, increase earnings & conversions.
The first case of that is a worldwide pharmaceutical company, Bayer CropScience AG that utilized Machine learning how to discover a remedy for farmers. Weeds that harm crops are a problem for farmers because farming began. A suitable solution would be to employ a narrow spectrum that efficiently kills the specific species from the area while using as few undesirable side effects as you can. However, to be able to do so, farmers need to correctly identify the weeds in their own fields.
Using Talent Real-time Big Data, the company managed to come up with a brand new application that farmers could download at no cost. The program uses Machine Learning and Artificial Intelligence to accommodate photographs of weeds at the organization’s database with marijuana photographs farmers send. Available all around the Earth, the photograph database resides on a personal cloud saved on AWS. It offers to grow the chance to precisely predict the effect of her or his activities like, selection of seed collection, program rate of crop protection products, or crop timing. The outcome is a much more efficient method of farming which increases return and enables farmers to become more environmentally conscious of their activities.
Possible to reinvent“This is simply an example of the Machine Learning can alter a company, by allowing success more readily and economically than conventional coding-centric approaches. Owing to the open source, standards-based structure, Machine Learning models could be easily deployed to business programs and bridge the skills gap that typically exists between information scientists and IT programmers.”
As accessibility and adoption for this technology raise, Machine Learning will continue to encourage increasingly more sophisticated use cases to assist organisations to drive new inventions and improved customer experiences. A lot of individuals now begin to chat about Cognitive Computing since the nirvana of Machine Learning where systems can learn at scale, reason with the goal and also socialize with people more obviously. By imitating the human mind and the way that people process and conclude information through an idea, expertise, as well as the sensations, Cognitive Learning guarantees to help deliver top end programs of Machine Learning like personal vision and recognition, genuinely intelligent chat-bots, flexible handwriting recognition and much more.
Rapid improvements in hardware production are helping provide the compute power necessary for this cognitive software available in committed processors that help optimise processing and decrease the hardware footprint normally needed to support such programs.
AI and Machine Learning is the most critical technology for creation but it’s widely recognized that there are not the skills set up to reap the benefits. The skills gap is not anything new, but it will continue to evolve as new technologies become more complicated and it’s something which will always be on the peak of the schedule and need to be handled as the workforce becomes increasingly focused.
For all the reasons mentioned here, it is apparent that Machine Learning has the capacity to reinvent an assortment of business processes, and we’re seeing a few of that software today. I am really excited to find out the Machine Learning adoption grows and can affect change from the venture.
Car Price Prediction – Machine Learning Vs Deep Learning
This article was published as a part of the Data Science Blogathon
1. ObjectiveIn this article, we will be predicting the prices of used cars. We will be building various Machine Learning models and Deep Learning models with different architectures. In the end, we will see how machine learning models perform in comparison to deep learning models.
2. Data UsedHere we have used the data from a hiring competition that was live on chúng tôi Use the below link to access the data and use it for your analysis.
3. Data InspectionIn this section, we will explore the data. First Let’s see what columns we have in the data and their data types along with missing values information.
We can observe that data have 19237 rows and 18 columns.
There are 5 numeric columns and 13 categorical columns. With the first look, we can see that there are no missing values in the data.
‘Price‘ column/feature is going to be the target column or dependent feature for this project.
Let’s see the distribution of the data.
4. Data PreparationHere we will clean the data and prepare it for training the model.
‘ID’ columnWe are dropping the ‘ID’ column since it does not hold any significance for car Price prediction.
df.drop('ID',axis=1,inplace=True) ‘Levy’ columnAfter analyzing the ‘Levy’ column we found out that it does contain the missing values but it was given as ‘-‘ in the data and that’s why we were not able to capture the missing values earlier in the data.
Here we will impute ‘-‘ in the ‘Levy’ column with ‘0’ assuming there was no ‘Levy’. We can also impute it with ‘mean’ or ‘median’, but that’s a choice that you have to make.
df['Levy']=df['Levy'].replace('-',np.nan) df['Levy']=df['Levy'].astype(float) levy_mean=0 df['Levy'].fillna(levy_mean,inplace=True) df['Levy']=round(df['Levy'],2) ‘Mileage’ column‘Mileage’ column here means how many kilometres the car has driven. ‘km’ is written in the column after each reading. We will remove that.
#since milage is in KM only we will remove 'km' from it and make it numerical df['Mileage']=df['Mileage'].apply(lambda x:x.split(' ')[0]) df['Mileage']=df['Mileage'].astype('int') ‘Engine Volume’ columnIn the ‘Engine Volumn’ column along with the Engine Volumn ‘type’ of the engine(Turbo or not Turbo) is also written. We will create a new column that shows the ‘type’ of ‘Engine’.
df['Turbo']=df['Engine volume'].apply(lambda x:1 if 'Turbo' in str(x) else 0) df['Engine volume']=df['Engine volume'].apply(lambda x:str(x).replace('Turbo','')) df['Engine volume']=df['Engine volume'].astype(float) ‘Doors’ Column df['Doors'].unique()Output:
‘Doors’ column represents the number of doors in the car. But as we can see it is not clean. Let’s clean
Handling ‘Outliers’This we will examine across numerical features.
cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[0]]); sns.boxplot(df[cols[1]]); sns.boxplot(df[cols[2]]); sns.boxplot(df[cols[3]]); sns.boxplot(df[cols[4]]);
As we can see there are outliers in ‘Levy’,’Engine volume’, ‘Mileage’, ‘Cylinders’ columns. We will remove these outliers using Inter Quantile Range(IQR) method.
def find_outliers_limit(df,col): print(col) print('-'*50) #removing outliers q25, q75 = np.percentile(df[col], 25), np.percentile(df[col], 75) iqr = q75 - q25 print('Percentiles: 25th=%.3f, 75th=%.3f, IQR=%.3f' % (q25, q75, iqr)) # calculate the outlier cutoff cut_off = iqr * 1.5 lower, upper = q25 - cut_off, q75 + cut_off print('Lower:',lower,' Upper:',upper) return lower,upper def remove_outlier(df,col,upper,lower): # identify outliers outliers = [x for x in df[col] if x upper] print('Identified outliers: %d' % len(outliers)) # remove outliers print('Non-outlier observations: %d' % len(outliers_removed)) return final outlier_cols=['Levy','Engine volume','Mileage','Cylinders'] for col in outlier_cols: lower,upper=find_outliers_limit(df,col) df[col]=remove_outlier(df,col,upper,lower)Let’s examine the features after removing outliers.
plt.figure(figsize=(20,10)) df[outlier_cols].boxplot()We can observe that there are no outliers in the features now.
Creating Additional FeaturesWe see that ‘Mileage’ and ‘Engine Volume’ are continuous variables. While performing regression I have observed that binning such variables can help increase the performance of the model. So I am creating the ‘Bin’ features for these features/columns.
labels=[0,1,2,3,4,5,6,7,8,9] df['Mileage_bin']=pd.cut(df['Mileage'],len(labels),labels=labels) df['Mileage_bin']=df['Mileage_bin'].astype(float) labels=[0,1,2,3,4] df['EV_bin']=pd.cut(df['Engine volume'],len(labels),labels=labels) df['EV_bin']=df['EV_bin'].astype(float) Handling Categorical featuresI have used Ordinal Encoder to handle the categorical columns. OrdinalEncoder works similar to LabelEncoder but OrdinalEncoder can be applied to multiple features while LabelEncoder can be applied to One feature at a time. For more details please visit the below links
num_df=df.select_dtypes(include=np.number) cat_df=df.select_dtypes(include=object) encoding=OrdinalEncoder() cat_cols=cat_df.columns.tolist() encoding.fit(cat_df[cat_cols]) cat_oe=encoding.transform(cat_df[cat_cols]) cat_oe=pd.DataFrame(cat_oe,columns=cat_cols) cat_df.reset_index(inplace=True,drop=True) cat_oe.head() num_df.reset_index(inplace=True,drop=True) cat_oe.reset_index(inplace=True,drop=True) final_all_df=pd.concat([num_df,cat_oe],axis=1)Checking correlation
final_all_df['price_log']=np.log(final_all_df['Price'])We can observe that features are not much correlated in the data. But there is one thing that we can notice is that after log transforming ‘Price’ column, correlation with few features got increased which is a good thing. We will be using log-transformed ‘Price’ to train the model. Please visit mentioned link below to better understand how feature transformations help improve model performance.
5. Data Splitting and ScalingWe have done an 80-20 split on the data. 80% of the data will be used for training and 20% data will be used for testing.
We will also scale the data since feature values in data do not have the same scale and having different scales can produce poor model performance.
cols_drop=['Price','price_log','Cylinders'] X=final_all_df.drop(cols_drop,axis=1) y=final_all_df['Price'] X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=25) scaler=StandardScaler() X_train_scaled=scaler.fit_transform(X_train) X_test_scaled=scaler.transform(X_test) 6. Model BuildingWe built LinearRegression, XGBoost, and RandomForest as machine learning models and two deep learning models one having a small network and another having a large network.
We built base models of LinearRegression, XGBoost, and RandomForest so there is not much to show about these models but we can see the model summary and how they converge with deep learning models that we built.
Deep Learning Model – Small Network model summary model_dl_small.summary() Deep Learning Model – Small Network _Train & Validation Loss #plot the loss and validation loss of the dataset history_df = pd.DataFrame(model_dl_small.history.history) plt.figure(figsize=(20,10)) plt.plot(history_df['loss'], label='loss') plt.plot(history_df['val_loss'], label='val_loss') plt.xticks(np.arange(1,epochs+1,2)) plt.yticks(np.arange(1,max(history_df['loss']),0.5)) plt.legend() plt.grid() Deep Learning Model – Large Network model summary model_dl_large.summary() Deep Learning Model – Large Network _Train & Validation Loss #plot the loss and validation loss of the dataset history_df = pd.DataFrame(model_dl_large.history.history) plt.figure(figsize=(20,10)) plt.plot(history_df['loss'], label='loss') plt.plot(history_df['val_loss'], label='val_loss') plt.xticks(np.arange(1,epochs+1,2)) plt.yticks(np.arange(1,max(history_df['loss']),0.5)) plt.legend() plt.grid()6.1 Model Performance
We have evaluated the models using Mean_Squared_Error, Mean_Absolute_Error, Mean_Absolute_Percentage_Error, Mean_Squared_Log_Error as performance matrices, and below are the results we got.
We can observe that Deep Learning Model did not perform well in comparison with Machine Learning Models. RandomForest performed really well among Machine Learning Model.
Let’s visualize the results from Random Forest.
7. Result Visualization y_pred=np.exp(model_rf.predict(X_test_scaled)) number_of_observations=20 x_ax = range(len(y_test[:number_of_observations])) plt.figure(figsize=(20,10)) plt.plot(x_ax, y_test[:number_of_observations], label="True") plt.plot(x_ax, y_pred[:number_of_observations], label="Predicted") plt.title("Car Price - True vs Predicted data") plt.xlabel('Observation Number') plt.ylabel('Price') plt.xticks(np.arange(number_of_observations)) plt.legend() plt.grid() plt.show()We can observe in the graph that the model is performing really well as seen in performance matrices as well.
8. CodeCode was done on jupyter notebook. Below is the complete code for the project.
# Loading Libraries import pandas as pd import numpy as np from sklearn.preprocessing import OrdinalEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_log_error,mean_squared_error,mean_absolute_error,mean_absolute_percentage_error import datetime from sklearn.ensemble import RandomForestRegressor from sklearn.linear_model import LinearRegression from xgboost import XGBRegressor from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt import seaborn as sns from keras.models import Sequential from keras.layers import Dense from prettytable import PrettyTable df=pd.read_csv('../input/Participant_Data_TheMathCompany_.DSHH/train.csv') df.head() # Data Inspection df.shape df.describe().transpose() df.info() sns.pairplot(df, diag_kind='kde') # Data Preprocessing df.drop('ID',axis=1,inplace=True) df['Levy']=df['Levy'].replace('-',np.nan) df['Levy']=df['Levy'].astype(float) levy_mean=0 df['Levy'].fillna(levy_mean,inplace=True) df['Levy']=round(df['Levy'],2) milage_formats=set() def get_milage_format(x): x=x.split(' ')[1] milage_formats.add(x) df['Mileage'].apply(lambda x:get_milage_format(x)); milage_formats #since milage is in KM only we will remove 'km' from it and make it numerical df['Mileage']=df['Mileage'].apply(lambda x:x.split(' ')[0]) df['Mileage']=df['Mileage'].astype('int') df['Engine volume'].unique() df['Turbo']=df['Engine volume'].apply(lambda x:1 if 'Turbo' in str(x) else 0) df['Engine volume']=df['Engine volume'].apply(lambda x:str(x).replace('Turbo','')) df['Engine volume']=df['Engine volume'].astype(float) cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[0]]); cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[1]]); cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[2]]); cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[3]]); cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[4]]); def find_outliers_limit(df,col): print(col) print('-'*50) #removing outliers q25, q75 = np.percentile(df[col], 25), np.percentile(df[col], 75) iqr = q75 - q25 print('Percentiles: 25th=%.3f, 75th=%.3f, IQR=%.3f' % (q25, q75, iqr)) # calculate the outlier cutoff cut_off = iqr * 1.5 lower, upper = q25 - cut_off, q75 + cut_off print('Lower:',lower,' Upper:',upper) return lower,upper def remove_outlier(df,col,upper,lower): # identify outliers outliers = [x for x in df[col] if x upper] print('Identified outliers: %d' % len(outliers)) # remove outliers print('Non-outlier observations: %d' % len(outliers_removed)) return final outlier_cols=['Levy','Engine volume','Mileage','Cylinders'] for col in outlier_cols: lower,upper=find_outliers_limit(df,col) df[col]=remove_outlier(df,col,upper,lower) #boxplot - to see outliers plt.figure(figsize=(20,10)) df[outlier_cols].boxplot() df['Doors'].unique() df['Doors']=df['Doors'].astype(str) #Creating Additional Features labels=[0,1,2,3,4,5,6,7,8,9] df['Mileage_bin']=pd.cut(df['Mileage'],len(labels),labels=labels) df['Mileage_bin']=df['Mileage_bin'].astype(float) labels=[0,1,2,3,4] df['EV_bin']=pd.cut(df['Engine volume'],len(labels),labels=labels) df['EV_bin']=df['EV_bin'].astype(float) #Handling Categorical features num_df=df.select_dtypes(include=np.number) cat_df=df.select_dtypes(include=object) encoding=OrdinalEncoder() cat_cols=cat_df.columns.tolist() encoding.fit(cat_df[cat_cols]) cat_oe=encoding.transform(cat_df[cat_cols]) cat_oe=pd.DataFrame(cat_oe,columns=cat_cols) cat_df.reset_index(inplace=True,drop=True) cat_oe.head() num_df.reset_index(inplace=True,drop=True) cat_oe.reset_index(inplace=True,drop=True) final_all_df=pd.concat([num_df,cat_oe],axis=1) #Checking correlation final_all_df['price_log']=np.log(final_all_df['Price']) plt.figure(figsize=(20,10)) sns.heatmap(round(final_all_df.corr(),2),annot=True); cols_drop=['Price','price_log','Cylinders'] final_all_df.columns X=final_all_df.drop(cols_drop,axis=1) y=final_all_df['Price'] # Data Splitting and Scaling X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=25) scaler=StandardScaler() X_train_scaled=scaler.fit_transform(X_train) X_test_scaled=scaler.transform(X_test) # Model Building def train_ml_model(x,y,model_type): if model_type=='lr': model=LinearRegression() elif model_type=='xgb': model=XGBRegressor() elif model_type=='rf': model=RandomForestRegressor() model.fit(X_train_scaled,np.log(y)) return model def model_evaluate(model,x,y): predictions=model.predict(x) predictions=np.exp(predictions) mse=mean_squared_error(y,predictions) mae=mean_absolute_error(y,predictions) mape=mean_absolute_percentage_error(y,predictions) msle=mean_squared_log_error(y,predictions) mse=round(mse,2) mae=round(mae,2) mape=round(mape,2) msle=round(msle,2) return [mse,mae,mape,msle] model_lr=train_ml_model(X_train_scaled,y_train,'lr') model_xgb=train_ml_model(X_train_scaled,y_train,'xgb') model_rf=train_ml_model(X_train_scaled,y_train,'rf') ## Deep Learning ### Small Network model_dl_small=Sequential() model_dl_small.add(Dense(16,input_dim=X_train_scaled.shape[1],activation='relu')) model_dl_small.add(Dense(8,activation='relu')) model_dl_small.add(Dense(4,activation='relu')) model_dl_small.add(Dense(1,activation='linear')) model_dl_small.summary() epochs=20 batch_size=10 model_dl_small.fit(X_train_scaled,np.log(y_train),verbose=0,validation_data=(X_test_scaled,np.log(y_test)),epochs=epochs,batch_size=batch_size) #plot the loss and validation loss of the dataset history_df = pd.DataFrame(model_dl_small.history.history) plt.figure(figsize=(20,10)) plt.plot(history_df['loss'], label='loss') plt.plot(history_df['val_loss'], label='val_loss') plt.xticks(np.arange(1,epochs+1,2)) plt.yticks(np.arange(1,max(history_df['loss']),0.5)) plt.legend() plt.grid() ### Large Network model_dl_large=Sequential() model_dl_large.add(Dense(64,input_dim=X_train_scaled.shape[1],activation='relu')) model_dl_large.add(Dense(32,activation='relu')) model_dl_large.add(Dense(16,activation='relu')) model_dl_large.add(Dense(1,activation='linear')) model_dl_large.summary() epochs=20 batch_size=10 model_dl_large.fit(X_train_scaled,np.log(y_train),verbose=0,validation_data=(X_test_scaled,np.log(y_test)),epochs=epochs,batch_size=batch_size) #plot the loss and validation loss of the dataset history_df = pd.DataFrame(model_dl_large.history.history) plt.figure(figsize=(20,10)) plt.plot(history_df['loss'], label='loss') plt.plot(history_df['val_loss'], label='val_loss') plt.xticks(np.arange(1,epochs+1,2)) plt.yticks(np.arange(1,max(history_df['loss']),0.5)) plt.legend() plt.grid() summary=PrettyTable(['Model','MSE','MAE','MAPE','MSLE']) summary.add_row(['LR']+model_evaluate(model_lr,X_test_scaled,y_test)) summary.add_row(['XGB']+model_evaluate(model_xgb,X_test_scaled,y_test)) summary.add_row(['RF']+model_evaluate(model_rf,X_test_scaled,y_test)) summary.add_row(['DL_SMALL']+model_evaluate(model_dl_small,X_test_scaled,y_test)) summary.add_row(['DL_LARGE']+model_evaluate(model_dl_large,X_test_scaled,y_test)) print(summary) y_pred=np.exp(model_rf.predict(X_test_scaled)) number_of_observations=20 x_ax = range(len(y_test[:number_of_observations])) plt.figure(figsize=(20,10)) plt.plot(x_ax, y_test[:number_of_observations], label="True") plt.plot(x_ax, y_pred[:number_of_observations], label="Predicted") plt.title("Car Price - True vs Predicted data") plt.xlabel('Observation Number') plt.ylabel('Price') plt.xticks(np.arange(number_of_observations)) plt.legend() plt.grid() plt.show() 9.ConclusionIn this article, we tried predicting the car price using the various parameters that were provided in the data about the car. We build machine learning and deep learning models to predict car prices and saw that machine learning-based models performed well at this data than deep learning-based models.
10. About the AuthorHi, I am Kajal Kumari. I have completed my Master’s from IIT(ISM) Dhanbad in Computer Science & Engineering. As of now, I am working as Machine Learning Engineer in Hyderabad. You can also check out few other blogs that I have written here.
The media shown in this article on LSTM for Human Activity Recognition are not owned by Analytics Vidhya and are used at the Author’s discretion.
Related
Difference Between Cognitive Computing And Machine Learning
Introduction
Cognitive computing and machine learning are two buzzwords that are frequently used interchangeably in the field of artificial intelligence (AI). Yet, there are important differences between the two, and businesses and organisations looking to use AI to achieve a competitive edge must comprehend these differences. We shall thoroughly examine the distinctions between cognitive computing and machine learning in this article.
Differences What is Cognitive Computing?Cognitive computing systems can decipher unstructured data, such as pictures, text, and speech, and draw valuable conclusions from it. These systems are also capable of reasoning, decision-making, and experience-based learning. They can communicate with people in natural language, comprehend situations, and give personalized answers.
Natural language processing, sentiment analysis, speech recognition, and image recognition are a few typical cognitive computing applications. Systems that use cognitive computing include Google Assistant and IBM Watson, for instance.
What is Machine Learning?Machine learning, a branch of artificial intelligence, allows computers to learn from data without explicit programming. In other words, machine learning algorithms enable computers to continuously improve their performance by autonomously learning from data.
The three types of machine learning algorithms are supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the machine learning algorithm learns from labelled data to make predictions about new, new dataset. Unsupervised learning is the process through which a machine learning system finds patterns and relationships in unlabeled data. Reinforcement learning uses rewards or penalties to provide feedback to the machine learning algorithm as it learns through trial and error.
Applications for machine learning include fraud detection, natural language processing, picture identification, and recommendation systems. Decision trees, logistic regression, neural networks, and linear regression are a few common machine learning algorithms.
Differences between Cognitive Computing and Machine LearningAlthough machine learning and cognitive computing are both subsets of artificial intelligence, they differ greatly from one another. The following are some significant variations −
PurposeMaking devices that can copy human thought processes, communicate with people in natural language, and offer customized responses is the goal of cognitive computing. The goal of machine learning is to give machines the ability to learn from data and statistically improve their performance.
ApproachNatural language processing, machine learning, and computer vision are just a few of the methods used in cognitive computing to mimic human thought processes. On the other hand, machine learning heavily relies on algorithms that learn from data.
Data TypesCognitive computing systems are capable of comprehending and analysing unstructured data, including speech, images, and text. Both organized and unstructured data can be used by machine learning algorithms, but structured data is often more productive.
ApplicationsCognitive computing is often used in applications that require natural language processing, sentiment analysis, and personalized responses. Machine learning is used in a wide range of applications, including image recognition, fraud detection, recommendation systems, and predictive analytics.
User InteractionCognitive computing systems are intended to communicate with users in natural language, comprehend context, and offer tailored responses. Often, user-facing interactions between machine learning systems and users are limited.
Training DataLarge amounts of training data are necessary for cognitive computing systems to learn from. While machine learning algorithms still need training data, they frequently have a smaller learning curve.
Here are the differences briefed in tabular form −
Cognitive Computing
Machine Learning
It Mimics the human thinking
It mainly Learns from the data
Uses natural language processing, and machine learning with computer vision
It Relies primarily on ML algorithms
It Can analyzes unstructured data
Can analyze with structured and unstructured data
Used for natural language processing, sentiment analysis, personalized responses
Used for image recognition, fraud detection, recommendation systems, predictive analytics
Interacts with users in a natural language
Does not interact with users directly
Requires large amounts of training data
Can learn from smaller amounts of data
less interpretable
More interpretable than Cognitive
Focuses on cognition and perception
Focuses on prediction and optimization
This Emulates human reasoning processes
It Automates decision-making processes
Combines one or more AI techniques
Primarily depends on statistical models
Designed for complex tasks that require contextual understanding
Designed for specific tasks based on predefined criteria
Involves higher levels of human-machine interaction
Involves lower levels of human-machine interaction
Examples of cognitive: IBM Watson, Google Assistant
Examples of ML: TensorFlow, Keras, Scikit-learn
ConclusionTo conclude, cognitive computing and machine learning are two potent AI methodologies that are applied to a range of challenging issues. Machine learning uses statistical models to make predictions and optimize results, whereas cognitive computing aims to replicate human thought and perception. For companies and organisations looking to integrate AI solutions into their operations, it is essential to comprehend the distinctions between these two methodologies. Organizations may utilise the full potential of AI to achieve their goals and spur innovation in their particular industries by selecting the best AI technique based on their unique demands.
Announcing The Machine Learning Starter Program!
Ideal Time to Start your Machine Learning Journey!
Picture this – you want to learn all about machine learning but just can’t find the time. There’s too much to do, whether that’s our professional work or your exams are around the corner. Suddenly, you have a lot of time on your hands and a once-in-a-lifetime opportunity to learn machine learning and apply it!
That’s exactly the opportunity in front of you right now. We are living in unprecedented times with half the world in complete lockdown and following social distancing protocols. There are two types of people emerging during this lockdown:
Those who are watching movies and surfing the internet to pass the time
Those who are eager to pick up a new skill, learn a new programming language, or apply your machine learning knowledge
If you’re in the latter category – we are thrilled to announce the:
You can use the code ‘LOCKDOWN’ to enroll in the Machine Learning Starter Program for FREE! You will have access to the course for 14 days from the day of your enrollment. Post this, the fee of the Program will be Rs. 4,999 (or $80).
What is the Machine Learning Starter Program?The Machine Learning Starter Program is a step-by-step online starter program to learn the basics of Machine Learning, hear from industry experts and data science professionals, and apply your learning in machine learning hackathons!
This is the perfect starting point to ignite your fledging machine learning career and take a HUGE step towards your dream data scientist role.
The aim of the Machine Learning Starter Program is to:
Help you understand how this field is transforming and disrupting industries
Acquaint you with the core machine learning algorithms
Enhance and complement your learning through competition and hackathon exposure
We believe in a holistic learning approach and that’s how we’ve curated the Machine Learning Starter Program.
What does the Machine Learning Starter Program include?There are several components in the Machine Learning Starter Program:
Machine Learning Basics Course
Expert Talks on various machine learning topics by industry practitioners
2 awesome machine learning hackathons
E-book on “Machine Learning Simplified”
Let’s explore each offering in a bit more detail.
Machine Learning Basics CourseThis course provides you all the tools and techniques you need to apply machine learning to solve business problems. Here’s what you’ll learn in the Machine Learning Basics course:
Understand how Machine Learning and Data Science are disrupting multiple industries today
Linear, Logistic Regression, Decision Tree and Random Forest algorithms for building machine learning models
Understand how to solve Classification and Regression problems in machine learning
How to evaluate your machine learning models and improve them through Feature Engineering
Improve and enhance your machine learning model’s accuracy through feature engineering
Expert TalksThere is no substitute for experience.
This course is an amalgamation of various talks by machine learning experts, practitioners, professionals and leaders who have decades upon decades of learning experience with them. They have already gone through the entire learning process and they showcase their work and thought process in these talks.
This course features rockstar data science experts like Sudalai Rajkumar (SRK), Professor Balaraman Ravindran, Dipanjan Sarkar, Kiran R and many more!
Machine Learning HackathonsThe Machine Learning Starter Program features two awesome hackathons to augment your learning:
JanataHack
Machine Learning Starter Program hackathon
Come, interact with the community, apply your machine learning knowledge, hack, have fun and stay safe.
E-Book on “Machine Learning Simplified”This e-book aims to provide an overview of machine learning, recent developments and current challenges in Machine Learning. Here’s a quick summary of what’s included:
What is Machine Learning?
Applications of Machine Learning
How do Machines Learn?
Why is Machine Learning getting so much attention?
Steps required to Build a Machine Learning Model
How can one build a career in Machine learning?
and much more!
Who is the Machine Learning Starter Program for?The Machine Learning Starter Program is for anyone who:
Is a beginner in machine learning
Wants to kick start their machine learning journey
Wants to learn about core machine learning algorithms
Is interested in a practical learning environment
Wants to practice and enhance their existing machine learning knowledge
So, what are you waiting for? Enroll in the Machine Learning Starter Program for FREE using the code ‘LOCKDOWN’ and begin your learning journey today!
Related
Top Machine Learning Trends For 2023 And Beyond
What’s next in machine learning development?
Machine learning is one of the branches of artificial intelligence that creates algorithms to help machines understand and make decisions based on data. The process of automation of software testing is connected to the development of machine learning. Owing to that, there is a fast pace of development in the IT industry. Machine learning is being incorporated in several companies, including tech giants like Google, Apple, Facebook, Netflix, and eBay. Analysts predict that
machine learning will continue to grow in popularity until 2024, with the most growth in 2023 and 2023
.
For the next three years, these are the major trends and developments we can expect in the field of machine learning.
1. Machine learning and IoTThis is the trend that is most awaited by tech professionals. Its development will impact the usage of 5G, which will become the base for IoT. As 5G comes with high speeds, devices will react quickly and transfer and receive more information. IoT devices enable multiple devices to connect across a network via the internet. Year by year, the amount of devices that are being connected is increasing, and the amount of information transferred is being increased as well. The use of IoT devices will leverage many fields like environment, healthcare, education, and the IT field. This combination will also ensure there are fewer errors and data leaks on the internet.
2. Automated machine learningAutomated machine learning will help specialists to develop efficient models for higher productivity. Because of this, all the developments will be focused on giving out the most accurate task solving. AutoML is used to sustain high-quality custom models, to improve the efficiency of work without much knowledge of programming. Additionally, AutoML will be useful by subject matter experts. This technology will provide training without spending much time and sacrificing the quality of work.
3. Better cybersecurityMost of our appliances and apps have become smart, with a high level of tech progress.
They are constantly connected to the internet which raises the need to increase the level of security. By using machine learning, professionals can create innovative anti-virus models that can ward off cyber-crime, hackers, and minimize attacks by helping the model identify different kinds of threats, like the behavior of malware, code difference, and new viruses.
4. AI EthicsWith the development of AI and ML, ethics need to be established for these technologies. As technology is becoming modern, ethics to need to become modern, otherwise, machines will not be able to work and make wrong decisions, like
what is happening with self-driving cars
. Failure of artificial intelligence to perform as desired is the main reason for self-driving car failures. The programming in autonomous cars is driving biased conclusions by separating groups of people. These are two reasons for this:
• Developers are choosing data with bais, to begin with. For example, they can use the information where the majority of the factors can cause the machine to favor the other.
• Lack of data moderation can also make machine learning models learn from the wrong type of data. This can lead to prejudice in the neural network of the machine.
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