Learn Data Science Deep Learning, Machine Learning NLP & R (Udemy.com)
Learn Data Science, Deep Learning, Machine Learning, Natural Language Processing, R and Python Language with libraries
Created by: Cinnamon TechX
Produced in 2021
What you will learn
- Mathematics and Statistics behind Machine Learning
- Mathematics and Statistics behind Deep Learning
- Mathematics and Statistics behind Artificial Intelligence
- Python Programming Language from Scratch
- Python with it's Libraries
- Learn Numpy, Pandas, Matplotlib, Scikit-Learn
- Learn Natural Language Processing
- Learn R Language
- Learn Tokenization in Natural Language Processing
- Learn Implementation of R Packages and Libraries on Different Data Sets
- Learn Implementation of Python Libraries on Different Data Sets
- Algorithms and Models of Machine Learning
- Algorithms and Models of Deep Learning
- Learn Data Science
- k-Nearest Neighbors, Naive Bayes etc
- Supervised and Unsupervised Learning
- Clustering
- Different Theorems
Quality Score
Content Quality
/
Video Quality
/
Qualified Instructor
/
Course Pace
/
Course Depth & Coverage
/
Overall Score : 98 / 100
Course Description
||DATASCIENCE||Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. What Does a Data Scientist Do?
In the past decade, data scientists have become necessary assets and are present in almost all organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business.
Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.
Glassdoor ranked data scientist as the #1 Best Job in America in 2018 for the third year in a row. 4 As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. The growing demand for data science professionals across industries, big and small, is being challenged by a shortage of qualified candidates available to fill the open positions.
The need for data scientists shows no sign of slowing down in the coming years. LinkedIn listed data scientist as one of the most promising jobs in 2017 and 2018, along with multiple data-science-related skills as the most in-demand by companies. Where Do You Fit in Data Science?
Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data.
Data ScientistData scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.
Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learningData AnalystData analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.
Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualizationData EngineerData engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.
Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)Data Science Career Outlook and Salary OpportunitiesData science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. With over 4,500 open positions listed on Glassdoor, data science professionals with the appropriate experience and education have the opportunity to make their mark in some of the most forward-thinking companies in the world.
So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.
Predictive causal analytics If you want a model which can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model which can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not.
Prescriptive analytics: If you want a model which has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes.
The best example for this is Googles self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up.
Machine learning for making predictions If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.
Machine learning for pattern discovery If you dont have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you dont have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering.
Lets say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength.||DEEPLEARNING||Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free s
In the past decade, data scientists have become necessary assets and are present in almost all organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business.
Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.
Glassdoor ranked data scientist as the #1 Best Job in America in 2018 for the third year in a row. 4 As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. The growing demand for data science professionals across industries, big and small, is being challenged by a shortage of qualified candidates available to fill the open positions.
The need for data scientists shows no sign of slowing down in the coming years. LinkedIn listed data scientist as one of the most promising jobs in 2017 and 2018, along with multiple data-science-related skills as the most in-demand by companies. Where Do You Fit in Data Science?
Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data.
Data ScientistData scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.
Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learningData AnalystData analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.
Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualizationData EngineerData engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.
Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)Data Science Career Outlook and Salary OpportunitiesData science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. With over 4,500 open positions listed on Glassdoor, data science professionals with the appropriate experience and education have the opportunity to make their mark in some of the most forward-thinking companies in the world.
So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.
Predictive causal analytics If you want a model which can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model which can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not.
Prescriptive analytics: If you want a model which has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes.
The best example for this is Googles self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up.
Machine learning for making predictions If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.
Machine learning for pattern discovery If you dont have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you dont have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering.
Lets say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength.||DEEPLEARNING||Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free s
Instructor Details
- 4.9 Rating
184 Reviews
Cinnamon TechX
We are an emerging E-Learning company aiming to teach people advanced concepts starting from scratch. We offer real-time projects through real-world data., which help people to develop skills through on-hand one-to-one training. We are currently in the process of creating more online courses and publishing books.
We teach:
Data Science
Personal Development
Programming
Finance
Digital Marketing
Stay Tuned for more
More deep learning courses
Complete Tensorflow 2 and Keras Deep Learning Bootcamp (2021)
4.7 (312 Reviews)
Provider: Udemy
Time: 19h
$11.99
PyTorch for Deep Learning and Computer Vision (2021)
4.7 (179 Reviews)
- Provider: Udemy
- Time: 10.5h
$11.99
PyTorch: Deep Learning and Artificial Intelligence (2021)
4.7 (38 Reviews)
- Provider: Udemy
- Time: 22.5h
$11.99
![Deep Learning with TensorFlow 2.0 [2021]](/programming/deep-learning/images/1420956_f8d8_4.jpg?v=1789153475)
The Complete Self-Driving Car Course - Applied Deep Learning (2021)
4.6 (252 Reviews)
- Provider: Udemy
- Time: 18h
$11.99
PyTorch for Deep Learning with Python Bootcamp (2019)
4.6 (176 Reviews)
- Provider: Udemy
- Time: 17h
$11.99
Reviews
- Video Duration:
70.5h - Price: 11.99 Track Price 11.99 Track Price
- Provider: Udemy
- Year:
2021 - Language:
English - Certificate:
Yes
30-Day Money-Back Guarantee
Udemy Coupon Price Tracker
