Top 10 Data Science Courses to Learn in December 2024
Looking to enhance your skills? We’ve curated a list of the top-rated data science courses available this month. These courses are highly rated by students and offer comprehensive learning experiences.
10. Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2024]
Instructor: Kirill Eremenko
Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!
Course Highlights:
- Rating: 4.61 ⭐ (34258 reviews)
- Students Enrolled: 220222
- Course Length: 76077 hours
- Number of Lectures: 235
- Number of Quizzes: 1
![Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2024]](https://img-c.udemycdn.com/course/480x270/513244_b831_4.jpg)
Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2024], has an average rating of 4.61, with 235 lectures, 1 quizzes, based on 34258 reviews, and has 220222 subscribers.
You will learn about Successfully perform all steps in a complex Data Science project Create Basic Tableau Visualisations Perform Data Mining in Tableau Understand how to apply the Chi-Squared statistical test Apply Ordinary Least Squares method to Create Linear Regressions Assess R-Squared for all types of models Assess the Adjusted R-Squared for all types of models Create a Simple Linear Regression (SLR) Create a Multiple Linear Regression (MLR) Create Dummy Variables Interpret coefficients of an MLR Read statistical software output for created models Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models Create a Logistic Regression Intuitively understand a Logistic Regression Operate with False Positives and False Negatives and know the difference Read a Confusion Matrix Create a Robust Geodemographic Segmentation Model Transform independent variables for modelling purposes Derive new independent variables for modelling purposes Check for multicollinearity using VIF and the correlation matrix Understand the intuition of multicollinearity Apply the Cumulative Accuracy Profile (CAP) to assess models Build the CAP curve in Excel Use Training and Test data to build robust models Derive insights from the CAP curve Understand the Odds Ratio Derive business insights from the coefficients of a logistic regression Understand what model deterioration actually looks like Apply three levels of model maintenance to prevent model deterioration Install and navigate SQL Server Install and navigate Microsoft Visual Studio Shell Clean data and look for anomalies Use SQL Server Integration Services (SSIS) to upload data into a database Create Conditional Splits in SSIS Deal with Text Qualifier errors in RAW data Create Scripts in SQL Apply SQL to Data Science projects Create stored procedures in SQL Present Data Science projects to stakeholders This course is ideal for individuals who are Anybody with an interest in Data Science or Anybody who wants to improve their data mining skills or Anybody who wants to improve their statistical modelling skills or Anybody who wants to improve their data preparation skills or Anybody who wants to improve their Data Science presentation skills It is particularly useful for Anybody with an interest in Data Science or Anybody who wants to improve their data mining skills or Anybody who wants to improve their statistical modelling skills or Anybody who wants to improve their data preparation skills or Anybody who wants to improve their Data Science presentation skills.
Learn More About Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2024]
What You Will Learn
- Successfully perform all steps in a complex Data Science project
- Create Basic Tableau Visualisations
- Perform Data Mining in Tableau
- Understand how to apply the Chi-Squared statistical test
- Apply Ordinary Least Squares method to Create Linear Regressions
- Assess R-Squared for all types of models
- Assess the Adjusted R-Squared for all types of models
- Create a Simple Linear Regression (SLR)
- Create a Multiple Linear Regression (MLR)
- Create Dummy Variables
- Interpret coefficients of an MLR
- Read statistical software output for created models
- Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
- Create a Logistic Regression
- Intuitively understand a Logistic Regression
- Operate with False Positives and False Negatives and know the difference
- Read a Confusion Matrix
- Create a Robust Geodemographic Segmentation Model
- Transform independent variables for modelling purposes
- Derive new independent variables for modelling purposes
- Check for multicollinearity using VIF and the correlation matrix
- Understand the intuition of multicollinearity
- Apply the Cumulative Accuracy Profile (CAP) to assess models
- Build the CAP curve in Excel
- Use Training and Test data to build robust models
- Derive insights from the CAP curve
- Understand the Odds Ratio
- Derive business insights from the coefficients of a logistic regression
- Understand what model deterioration actually looks like
- Apply three levels of model maintenance to prevent model deterioration
- Install and navigate SQL Server
- Install and navigate Microsoft Visual Studio Shell
- Clean data and look for anomalies
- Use SQL Server Integration Services (SSIS) to upload data into a database
- Create Conditional Splits in SSIS
- Deal with Text Qualifier errors in RAW data
- Create Scripts in SQL
- Apply SQL to Data Science projects
- Create stored procedures in SQL
- Present Data Science projects to stakeholders
9. Python Data Science: Data Prep & EDA with Python
Instructor: Maven Analytics
Learn Python + Pandas for data cleaning, profiling & EDA, and prep data for machine learning & data science with Python
Course Highlights:
- Rating: 4.73 ⭐ (1023 reviews)
- Students Enrolled: 6555
- Course Length: 31031 hours
- Number of Lectures: 180
- Number of Quizzes: 7
Python Data Science: Data Prep & EDA with Python, has an average rating of 4.73, with 180 lectures, 7 quizzes, based on 1023 reviews, and has 6555 subscribers.
You will learn about Master the core building blocks of Python for data science BEFORE applying machine learning algorithms Scope data science projects by clearly defining the goals, techniques, and data sources needed for your analysis Import and export flat files, Excel workbooks, and SQL database tables using Pandas Clean data by converting data types, handling common data issues, and creating new columns for analysis Perform exploratory data analysis (EDA) by sorting, filtering, grouping, and visualizing data to discover patterns and insights Prepare data for machine learning models by joining tables, aggregating rows, and applying feature engineering techniques This course is ideal for individuals who are Data scientists looking to learn core techniques and best practices for data prep and exploratory data analysis or Python users who want to build the core skills required before applying AI and machine learning models or Data analysts or BI experts looking to transition into a data science role or Anyone interested in learning one of the most popular open source programming languages in the world It is particularly useful for Data scientists looking to learn core techniques and best practices for data prep and exploratory data analysis or Python users who want to build the core skills required before applying AI and machine learning models or Data analysts or BI experts looking to transition into a data science role or Anyone interested in learning one of the most popular open source programming languages in the world.
Learn More About Python Data Science: Data Prep & EDA with Python
What You Will Learn
- Master the core building blocks of Python for data science BEFORE applying machine learning algorithms
- Scope data science projects by clearly defining the goals, techniques, and data sources needed for your analysis
- Import and export flat files, Excel workbooks, and SQL database tables using Pandas
- Clean data by converting data types, handling common data issues, and creating new columns for analysis
- Perform exploratory data analysis (EDA) by sorting, filtering, grouping, and visualizing data to discover patterns and insights
- Prepare data for machine learning models by joining tables, aggregating rows, and applying feature engineering techniques
8. Python for Machine Learning & Data Science Masterclass
Instructor: Jose Portilla
Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more!
Course Highlights:
- Rating: 4.69 ⭐ (16490 reviews)
- Students Enrolled: 116193
- Course Length: 158496 hours
- Number of Lectures: 232
- Number of Quizzes: 4
Python for Machine Learning & Data Science Masterclass, has an average rating of 4.69, with 232 lectures, 4 quizzes, based on 16490 reviews, and has 116193 subscribers.
You will learn about You will learn how to use data science and machine learning with Python. You will create data pipeline workflows to analyze, visualize, and gain insights from data. You will build a portfolio of data science projects with real world data. You will be able to analyze your own data sets and gain insights through data science. Master critical data science skills. Understand Machine Learning from top to bottom. Replicate real-world situations and data reports. Learn NumPy for numerical processing with Python. Conduct feature engineering on real world case studies. Learn Pandas for data manipulation with Python. Create supervised machine learning algorithms to predict classes. Learn Matplotlib to create fully customized data visualizations with Python. Create regression machine learning algorithms for predicting continuous values. Learn Seaborn to create beautiful statistical plots with Python. Construct a modern portfolio of data science and machine learning resume projects. Learn how to use Scikit-learn to apply powerful machine learning algorithms. Get set-up quickly with the Anaconda data science stack environment. Learn best practices for real-world data sets. Understand the full product workflow for the machine learning lifecycle. Explore how to deploy your machine learning models as interactive APIs. This course is ideal for individuals who are Beginner Python developers curious about Machine Learning and Data Science with Python It is particularly useful for Beginner Python developers curious about Machine Learning and Data Science with Python.
Learn More About Python for Machine Learning & Data Science Masterclass
What You Will Learn
- You will learn how to use data science and machine learning with Python.
- You will create data pipeline workflows to analyze, visualize, and gain insights from data.
- You will build a portfolio of data science projects with real world data.
- You will be able to analyze your own data sets and gain insights through data science.
- Master critical data science skills.
- Understand Machine Learning from top to bottom.
- Replicate real-world situations and data reports.
- Learn NumPy for numerical processing with Python.
- Conduct feature engineering on real world case studies.
- Learn Pandas for data manipulation with Python.
- Create supervised machine learning algorithms to predict classes.
- Learn Matplotlib to create fully customized data visualizations with Python.
- Create regression machine learning algorithms for predicting continuous values.
- Learn Seaborn to create beautiful statistical plots with Python.
- Construct a modern portfolio of data science and machine learning resume projects.
- Learn how to use Scikit-learn to apply powerful machine learning algorithms.
- Get set-up quickly with the Anaconda data science stack environment.
- Learn best practices for real-world data sets.
- Understand the full product workflow for the machine learning lifecycle.
- Explore how to deploy your machine learning models as interactive APIs.
7. Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]
Instructor: Kirill Eremenko
Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
Course Highlights:
- Rating: 4.54 ⭐ (191747 reviews)
- Students Enrolled: 1100065
- Course Length: 152569 hours
- Number of Lectures: 472
- Number of Quizzes: 37
![Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]](https://img-c.udemycdn.com/course/480x270/950390_270f_3.jpg)
Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024], has an average rating of 4.54, with 472 lectures, 37 quizzes, based on 191747 reviews, and has 1100065 subscribers.
You will learn about Master Machine Learning on Python & R Have a great intuition of many Machine Learning models Make accurate predictions Make powerful analysis Make robust Machine Learning models Create strong added value to your business Use Machine Learning for personal purpose Handle specific topics like Reinforcement Learning, NLP and Deep Learning Handle advanced techniques like Dimensionality Reduction Know which Machine Learning model to choose for each type of problem Build an army of powerful Machine Learning models and know how to combine them to solve any problem This course is ideal for individuals who are Anyone interested in Machine Learning. or Students who have at least high school knowledge in math and who want to start learning Machine Learning. or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. or Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science. or Any data analysts who want to level up in Machine Learning. or Any people who are not satisfied with their job and who want to become a Data Scientist. or Any people who want to create added value to their business by using powerful Machine Learning tools. It is particularly useful for Anyone interested in Machine Learning. or Students who have at least high school knowledge in math and who want to start learning Machine Learning. or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. or Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science. or Any data analysts who want to level up in Machine Learning. or Any people who are not satisfied with their job and who want to become a Data Scientist. or Any people who want to create added value to their business by using powerful Machine Learning tools.
Learn More About Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]
What You Will Learn
- Master Machine Learning on Python & R
- Have a great intuition of many Machine Learning models
- Make accurate predictions
- Make powerful analysis
- Make robust Machine Learning models
- Create strong added value to your business
- Use Machine Learning for personal purpose
- Handle specific topics like Reinforcement Learning, NLP and Deep Learning
- Handle advanced techniques like Dimensionality Reduction
- Know which Machine Learning model to choose for each type of problem
- Build an army of powerful Machine Learning models and know how to combine them to solve any problem
6. Mathematics-Basics to Advanced for Data Science And GenAI
Instructor: Krish Naik
Build Strong math foundation with linear algebra,stats,probability,differential calculus for mastering data science
Course Highlights:
- Rating: 4.6 ⭐ (1026 reviews)
- Students Enrolled: 10729
- Course Length: 82548 hours
- Number of Lectures: 97
- Number of Quizzes: 0
Mathematics-Basics to Advanced for Data Science And GenAI, has an average rating of 4.6, with 97 lectures, based on 1026 reviews, and has 10729 subscribers.
You will learn about Master Calculus: Understand derivatives and integrals, and apply them in optimizing machine learning algorithms and data analysis tasks. Learn Linear Algebra: Grasp vectors, matrices, and eigenvalues, essential for building and understanding advanced data science models. Understand Probability: Dive into probability theory, crucial for making informed predictions and working with uncertainty in data. Apply Statistics: Gain practical skills in statistical analysis, helping you make data-driven decisions and interpret results effectively. This course is ideal for individuals who are Aspiring Data Scientists: Individuals looking to build a strong mathematical foundation essential for mastering data science and machine learning. or Data Science Beginners: Those who are new to data science and want to understand the core mathematical concepts that drive data science algorithms. or Professionals Transitioning into Data Science: Engineers, analysts, or professionals from other fields seeking to acquire the mathematical skills necessary for a career shift into data science. or Students and Academics: Students pursuing studies in data science, computer science, or related fields who need a comprehensive understanding of mathematics for data science applications. or Lifelong Learners: Anyone with a passion for learning and a desire to understand how mathematics powers the world of data science, even without prior experience in the field. or This course is tailored to equip learners with the essential mathematical tools needed to excel in data science, regardless of their current level of expertise. It is particularly useful for Aspiring Data Scientists: Individuals looking to build a strong mathematical foundation essential for mastering data science and machine learning. or Data Science Beginners: Those who are new to data science and want to understand the core mathematical concepts that drive data science algorithms. or Professionals Transitioning into Data Science: Engineers, analysts, or professionals from other fields seeking to acquire the mathematical skills necessary for a career shift into data science. or Students and Academics: Students pursuing studies in data science, computer science, or related fields who need a comprehensive understanding of mathematics for data science applications. or Lifelong Learners: Anyone with a passion for learning and a desire to understand how mathematics powers the world of data science, even without prior experience in the field. or This course is tailored to equip learners with the essential mathematical tools needed to excel in data science, regardless of their current level of expertise.
Learn More About Mathematics-Basics to Advanced for Data Science And GenAI
What You Will Learn
- Master Calculus: Understand derivatives and integrals, and apply them in optimizing machine learning algorithms and data analysis tasks.
- Learn Linear Algebra: Grasp vectors, matrices, and eigenvalues, essential for building and understanding advanced data science models.
- Understand Probability: Dive into probability theory, crucial for making informed predictions and working with uncertainty in data.
- Apply Statistics: Gain practical skills in statistical analysis, helping you make data-driven decisions and interpret results effectively.
5. Python for Data Science and Machine Learning Bootcamp
Instructor: Jose Portilla
Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!
Course Highlights:
- Rating: 4.59 ⭐ (147362 reviews)
- Students Enrolled: 742077
- Course Length: 89200 hours
- Number of Lectures: 184
- Number of Quizzes: 1
Python for Data Science and Machine Learning Bootcamp, has an average rating of 4.59, with 184 lectures, 1 quizzes, based on 147362 reviews, and has 742077 subscribers.
You will learn about Use Python for Data Science and Machine Learning Use Spark for Big Data Analysis Implement Machine Learning Algorithms Learn to use NumPy for Numerical Data Learn to use Pandas for Data Analysis Learn to use Matplotlib for Python Plotting Learn to use Seaborn for statistical plots Use Plotly for interactive dynamic visualizations Use SciKit-Learn for Machine Learning Tasks K-Means Clustering Logistic Regression Linear Regression Random Forest and Decision Trees Natural Language Processing and Spam Filters Neural Networks Support Vector Machines This course is ideal for individuals who are This course is meant for people with at least some programming experience It is particularly useful for This course is meant for people with at least some programming experience.
Learn More About Python for Data Science and Machine Learning Bootcamp
What You Will Learn
- Use Python for Data Science and Machine Learning
- Use Spark for Big Data Analysis
- Implement Machine Learning Algorithms
- Learn to use NumPy for Numerical Data
- Learn to use Pandas for Data Analysis
- Learn to use Matplotlib for Python Plotting
- Learn to use Seaborn for statistical plots
- Use Plotly for interactive dynamic visualizations
- Use SciKit-Learn for Machine Learning Tasks
- K-Means Clustering
- Logistic Regression
- Linear Regression
- Random Forest and Decision Trees
- Natural Language Processing and Spam Filters
- Neural Networks
- Support Vector Machines
4. Complete A.I. & Machine Learning, Data Science Bootcamp
Instructor: Andrei Neagoie
Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!
Course Highlights:
- Rating: 4.61 ⭐ (24713 reviews)
- Students Enrolled: 134102
- Course Length: 155778 hours
- Number of Lectures: 384
- Number of Quizzes: 2
Complete A.I. & Machine Learning, Data Science Bootcamp, has an average rating of 4.61, with 384 lectures, 2 quizzes, based on 24713 reviews, and has 134102 subscribers.
You will learn about Become a Data Scientist and get hired Master Machine Learning and use it on the job Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0 Use modern tools that big tech companies like Google, Apple, Amazon and Meta use Present Data Science projects to management and stakeholders Learn which Machine Learning model to choose for each type of problem Real life case studies and projects to understand how things are done in the real world Learn best practices when it comes to Data Science Workflow Implement Machine Learning algorithms Learn how to program in Python using the latest Python 3 How to improve your Machine Learning Models Learn to pre process data, clean data, and analyze large data. Build a portfolio of work to have on your resume Developer Environment setup for Data Science and Machine Learning Supervised and Unsupervised Learning Machine Learning on Time Series data Explore large datasets using data visualization tools like Matplotlib and Seaborn Explore large datasets and wrangle data using Pandas Learn NumPy and how it is used in Machine Learning A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided Learn to use the popular library Scikit-learn in your projects Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry Learn to perform Classification and Regression modelling Learn how to apply Transfer Learning This course is ideal for individuals who are Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science and Python or You are a programmer that wants to extend their skills into Data Science and Machine Learning to make yourself more valuable or Anyone who wants to learn these topics from industry experts that don’t only teach, but have actually worked in the field or You’re looking for one single course to teach you about Machine learning and Data Science and get you caught up to speed with the industry or You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really “getting it” or You want to learn to use Deep learning and Neural Networks with your projects or You want to add value to your own business or company you work for, by using powerful Machine Learning tools. It is particularly useful for Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science and Python or You are a programmer that wants to extend their skills into Data Science and Machine Learning to make yourself more valuable or Anyone who wants to learn these topics from industry experts that don’t only teach, but have actually worked in the field or You’re looking for one single course to teach you about Machine learning and Data Science and get you caught up to speed with the industry or You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really “getting it” or You want to learn to use Deep learning and Neural Networks with your projects or You want to add value to your own business or company you work for, by using powerful Machine Learning tools.
Learn More About Complete A.I. & Machine Learning, Data Science Bootcamp
What You Will Learn
- Become a Data Scientist and get hired
- Master Machine Learning and use it on the job
- Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
- Use modern tools that big tech companies like Google, Apple, Amazon and Meta use
- Present Data Science projects to management and stakeholders
- Learn which Machine Learning model to choose for each type of problem
- Real life case studies and projects to understand how things are done in the real world
- Learn best practices when it comes to Data Science Workflow
- Implement Machine Learning algorithms
- Learn how to program in Python using the latest Python 3
- How to improve your Machine Learning Models
- Learn to pre process data, clean data, and analyze large data.
- Build a portfolio of work to have on your resume
- Developer Environment setup for Data Science and Machine Learning
- Supervised and Unsupervised Learning
- Machine Learning on Time Series data
- Explore large datasets using data visualization tools like Matplotlib and Seaborn
- Explore large datasets and wrangle data using Pandas
- Learn NumPy and how it is used in Machine Learning
- A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
- Learn to use the popular library Scikit-learn in your projects
- Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
- Learn to perform Classification and Regression modelling
- Learn how to apply Transfer Learning
3. 100 Days of Code: The Complete Python Pro Bootcamp
Instructor: Dr. Angela Yu, Developer and Lead Instructor
Master Python by building 100 projects in 100 days. Learn data science, automation, build websites, games and apps!
Course Highlights:
- Rating: 4.71 ⭐ (336663 reviews)
- Students Enrolled: 1443403
- Course Length: 187169 hours
- Number of Lectures: 654
- Number of Quizzes: 43
100 Days of Code: The Complete Python Pro Bootcamp, has an average rating of 4.71, with 654 lectures, 43 quizzes, based on 336663 reviews, and has 1443403 subscribers.
You will learn about You will master the Python programming language by building 100 unique projects over 100 days. You will learn automation, game, app and web development, data science and machine learning all using Python. You will be able to program in Python professionally You will learn Selenium, Beautiful Soup, Request, Flask, Pandas, NumPy, Scikit Learn, Plotly, and Matplotlib. Create a portfolio of 100 Python projects to apply for developer jobs Be able to build fully fledged websites and web apps with Python Be able to use Python for data science and machine learning Build games like Blackjack, Pong and Snake using Python Build GUIs and Desktop applications with Python This course is ideal for individuals who are If you want to learn to code from scratch through building fun and useful projects, then take this course. or If you want to start your own startup by building your own websites and web apps. or If you are a complete beginner then this course will be everything you need to become a Python professional or If you are a seasoned programmer wanting to switch to Python then this is the quickest way. Learn through coding projects. or If you are an intermediate Python programmer then you know 100 days of code challenges will help you level up. It is particularly useful for If you want to learn to code from scratch through building fun and useful projects, then take this course. or If you want to start your own startup by building your own websites and web apps. or If you are a complete beginner then this course will be everything you need to become a Python professional or If you are a seasoned programmer wanting to switch to Python then this is the quickest way. Learn through coding projects. or If you are an intermediate Python programmer then you know 100 days of code challenges will help you level up.
Learn More About 100 Days of Code: The Complete Python Pro Bootcamp
What You Will Learn
- You will master the Python programming language by building 100 unique projects over 100 days.
- You will learn automation, game, app and web development, data science and machine learning all using Python.
- You will be able to program in Python professionally
- You will learn Selenium, Beautiful Soup, Request, Flask, Pandas, NumPy, Scikit Learn, Plotly, and Matplotlib.
- Create a portfolio of 100 Python projects to apply for developer jobs
- Be able to build fully fledged websites and web apps with Python
- Be able to use Python for data science and machine learning
- Build games like Blackjack, Pong and Snake using Python
- Build GUIs and Desktop applications with Python
2. Complete Data Science,Machine Learning,DL,NLP Bootcamp
Instructor: Krish Naik
Master the theory, practice,and math behind Data Science,Machine Learning,Deep Learning,NLP with end to end projects
Course Highlights:
- Rating: 4.6 ⭐ (6713 reviews)
- Students Enrolled: 44034
- Course Length: 329254 hours
- Number of Lectures: 442
- Number of Quizzes: 20
Complete Data Science,Machine Learning,DL,NLP Bootcamp, has an average rating of 4.6, with 442 lectures, 20 quizzes, based on 6713 reviews, and has 44034 subscribers.
You will learn about Master foundational and advanced Machine Learning and NLP concepts. Apply theoretical and practical knowledge to real-world projects using Machine learning,NLP And MLOPS Understand and implement mathematical principles behind ML algorithms. Develop and optimize ML models using industry-standard tools and techniques. Understand The Core intuition of Deep Learning such as optimizers,loss functions,neural networks and cnn This course is ideal for individuals who are Aspiring data scientists and machine learning enthusiasts. or Students and professionals looking to enhance their ML and NLP skills. or Beginners with a basic understanding of programming and mathematics. or Anyone interested in understanding and applying machine learning and NLP techniques from scratch to advanced levels. or Beginners Python Developer who wants to get into the Data Science field It is particularly useful for Aspiring data scientists and machine learning enthusiasts. or Students and professionals looking to enhance their ML and NLP skills. or Beginners with a basic understanding of programming and mathematics. or Anyone interested in understanding and applying machine learning and NLP techniques from scratch to advanced levels. or Beginners Python Developer who wants to get into the Data Science field.
Learn More About Complete Data Science,Machine Learning,DL,NLP Bootcamp
What You Will Learn
- Master foundational and advanced Machine Learning and NLP concepts.
- Apply theoretical and practical knowledge to real-world projects using Machine learning,NLP And MLOPS
- Understand and implement mathematical principles behind ML algorithms.
- Develop and optimize ML models using industry-standard tools and techniques.
- Understand The Core intuition of Deep Learning such as optimizers,loss functions,neural networks and cnn
1. The Data Science Course: Complete Data Science Bootcamp 2024
Instructor: 365 Careers
Complete Data Science Training: Math, Statistics, Python, Advanced Statistics in Python, Machine and Deep Learning
Course Highlights:
- Rating: 4.59 ⭐ (147187 reviews)
- Students Enrolled: 725963
- Course Length: 111737 hours
- Number of Lectures: 550
- Number of Quizzes: 281
The Data Science Course: Complete Data Science Bootcamp 2024, has an average rating of 4.59, with 550 lectures, 281 quizzes, based on 147187 reviews, and has 725963 subscribers.
You will learn about The course provides the entire toolbox you need to become a data scientist Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow Impress interviewers by showing an understanding of the data science field Learn how to pre-process data Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!) Start coding in Python and learn how to use it for statistical analysis Perform linear and logistic regressions in Python Carry out cluster and factor analysis Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn Apply your skills to real-life business cases Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data Unfold the power of deep neural networks Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations This course is ideal for individuals who are You should take this course if you want to become a Data Scientist or if you want to learn about the field or This course is for you if you want a great career or The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills It is particularly useful for You should take this course if you want to become a Data Scientist or if you want to learn about the field or This course is for you if you want a great career or The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills.
Learn More About The Data Science Course: Complete Data Science Bootcamp 2024
What You Will Learn
- The course provides the entire toolbox you need to become a data scientist
- Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
- Impress interviewers by showing an understanding of the data science field
- Learn how to pre-process data
- Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
- Start coding in Python and learn how to use it for statistical analysis
- Perform linear and logistic regressions in Python
- Carry out cluster and factor analysis
- Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
- Apply your skills to real-life business cases
- Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
- Unfold the power of deep neural networks
- Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
- Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
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