Best Machine Learning Courses to Learn in December 2024
Looking to enhance your skills? We’ve curated a list of the top-rated machine learning courses available this month. These courses are highly rated by students and offer comprehensive learning experiences.
10. Data Science and Machine Learning Fundamentals [2024]
Instructor: Henrik Johansson
Learn to master Data Science and Machine Learning Fundamentals with Python and Pandas
Course Highlights:
- Rating: 4.85 ⭐ (322 reviews)
- Students Enrolled: 1107
- Course Length: 193363 hours
- Number of Lectures: 103
- Number of Quizzes: 0
![Data Science and Machine Learning Fundamentals [2024]](https://img-c.udemycdn.com/course/480x270/5160648_d59a_4.jpg)
Data Science and Machine Learning Fundamentals [2024], has an average rating of 4.85, with 103 lectures, based on 322 reviews, and has 1107 subscribers.
You will learn about Knowledge about Data Science and Machine Learning theory, algorithms, methods, best practices, and tasks Deep hands-on knowledge about Data Science and Machine Learning, and know how to do common Data Science and Machine Learning tasks The ability to handle common Data Science and Machine Learning tasks with confidence Master Python for Data Handling Master Pandas for Data Handling Knowledge and practical hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and many other Python libraries Detailed and deep, Master knowledge of Regression, Regression Analysis, Prediction, Classification, and Cluster analysis Advanced knowledge of A.I. prediction models and automatic model creation Advanced Knowledge of Text Mining, Text Mining Tasks, and Emotion Mining Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources This course is ideal for individuals who are This course is for you, regardless if you are a beginner or experienced Data Scientist, regardless if you have a Ph.D., or no education or experience at all. It is particularly useful for This course is for you, regardless if you are a beginner or experienced Data Scientist, regardless if you have a Ph.D., or no education or experience at all.
Learn More About Data Science and Machine Learning Fundamentals [2024]
What You Will Learn
- Knowledge about Data Science and Machine Learning theory, algorithms, methods, best practices, and tasks
- Deep hands-on knowledge about Data Science and Machine Learning, and know how to do common Data Science and Machine Learning tasks
- The ability to handle common Data Science and Machine Learning tasks with confidence
- Master Python for Data Handling
- Master Pandas for Data Handling
- Knowledge and practical hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and many other Python libraries
- Detailed and deep, Master knowledge of Regression, Regression Analysis, Prediction, Classification, and Cluster analysis
- Advanced knowledge of A.I. prediction models and automatic model creation
- Advanced Knowledge of Text Mining, Text Mining Tasks, and Emotion Mining
- Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources
9. TensorFlow for Deep Learning Bootcamp
Instructor: Andrei Neagoie
Learn TensorFlow by Google. Become an AI, Machine Learning, and Deep Learning expert!
Course Highlights:
- Rating: 4.7 ⭐ (11237 reviews)
- Students Enrolled: 77798
- Course Length: 224839 hours
- Number of Lectures: 425
- Number of Quizzes: 2
TensorFlow for Deep Learning Bootcamp, has an average rating of 4.7, with 425 lectures, 2 quizzes, based on 11237 reviews, and has 77798 subscribers.
You will learn about Build TensorFlow models using Computer Vision, Convolutional Neural Networks and Natural Language Processing Complete access to ALL interactive notebooks and ALL course slides as downloadable guides Increase your skills in Machine Learning, Artificial Intelligence, and Deep Learning Understand how to integrate Machine Learning into tools and applications Learn to build all types of Machine Learning Models using the latest TensorFlow 2 Build image recognition, text recognition algorithms with deep neural networks and convolutional neural networks Using real world images to visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy Applying Deep Learning for Time Series Forecasting Gain the skills you need to become a TensorFlow Developer Be recognized as a top candidate for recruiters seeking TensorFlow developers This course is ideal for individuals who are Anyone who wants to become a top 10% TensorFlow Developer and be at the forefront of Artificial Intelligence, Machine Learning, and Deep Learning or Students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow or Anyone looking to expand their knowledge when it comes to AI, Machine Learning and Deep Learning or Anyone looking to master building ML models with the latest version of TensorFlow It is particularly useful for Anyone who wants to become a top 10% TensorFlow Developer and be at the forefront of Artificial Intelligence, Machine Learning, and Deep Learning or Students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow or Anyone looking to expand their knowledge when it comes to AI, Machine Learning and Deep Learning or Anyone looking to master building ML models with the latest version of TensorFlow.
Learn More About TensorFlow for Deep Learning Bootcamp
What You Will Learn
- Build TensorFlow models using Computer Vision, Convolutional Neural Networks and Natural Language Processing
- Complete access to ALL interactive notebooks and ALL course slides as downloadable guides
- Increase your skills in Machine Learning, Artificial Intelligence, and Deep Learning
- Understand how to integrate Machine Learning into tools and applications
- Learn to build all types of Machine Learning Models using the latest TensorFlow 2
- Build image recognition, text recognition algorithms with deep neural networks and convolutional neural networks
- Using real world images to visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy
- Applying Deep Learning for Time Series Forecasting
- Gain the skills you need to become a TensorFlow Developer
- Be recognized as a top candidate for recruiters seeking TensorFlow developers
8. AWS Certified Machine Learning Engineer Associate: Hands On!
Instructor: Sundog Education by Frank Kane
Practice exam included! Master MLA-C01 / ME1-C01 AWS Machine Learning Engineer Exam: SageMaker, Bedrock, and AI Skills.
Course Highlights:
- Rating: 4.62 ⭐ (729 reviews)
- Students Enrolled: 11082
- Course Length: 85792 hours
- Number of Lectures: 298
- Number of Quizzes: 12
AWS Certified Machine Learning Engineer Associate: Hands On!, has an average rating of 4.62, with 298 lectures, 12 quizzes, based on 729 reviews, and has 11082 subscribers.
You will learn about Prepare confidently for the AWS Certified Machine Learning Engineer Associate exam. Understand and apply key AWS machine learning services like SageMaker, Bedrock, and more. Perform data preparation, feature engineering, and data validation for ML models. Master hyperparameter tuning, model training, and deployment strategies on AWS. Implement CI/CD pipelines and automation for scalable machine learning workflows. Secure, monitor, and optimize AWS ML infrastructure for performance and cost-efficiency. This course is ideal for individuals who are Data engineers, data scientists, DevOps professionals, and software developers who are looking to advance their careers by obtaining the AWS Certified Machine Learning Engineer Associate certification or IT professionals who have experience working with AWS services and want to deepen their understanding of machine learning solutions on the AWS platform. It is particularly useful for Data engineers, data scientists, DevOps professionals, and software developers who are looking to advance their careers by obtaining the AWS Certified Machine Learning Engineer Associate certification or IT professionals who have experience working with AWS services and want to deepen their understanding of machine learning solutions on the AWS platform.
Learn More About AWS Certified Machine Learning Engineer Associate: Hands On!
What You Will Learn
- Prepare confidently for the AWS Certified Machine Learning Engineer Associate exam.
- Understand and apply key AWS machine learning services like SageMaker, Bedrock, and more.
- Perform data preparation, feature engineering, and data validation for ML models.
- Master hyperparameter tuning, model training, and deployment strategies on AWS.
- Implement CI/CD pipelines and automation for scalable machine learning workflows.
- Secure, monitor, and optimize AWS ML infrastructure for performance and cost-efficiency.
7. 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 ⭐ (147453 reviews)
- Students Enrolled: 727832
- 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 147453 reviews, and has 727832 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
6. 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: 4.68 ⭐ (16534 reviews)
- Students Enrolled: 116548
- Course Length: 158496 hours
- Number of Lectures: 232
- Number of Quizzes: 4
Python for Machine Learning & Data Science Masterclass, has an average rating of 4.68, with 232 lectures, 4 quizzes, based on 16534 reviews, and has 116548 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.
5. Machine Learning, Data Science and Generative AI with Python
Instructor: Sundog Education by Frank Kane
Complete hands-on machine learning and GenAI tutorial with data science, Tensorflow, GPT, OpenAI, and neural networks
Course Highlights:
- Rating: 4.63 ⭐ (33597 reviews)
- Students Enrolled: 219962
- Course Length: 72852 hours
- Number of Lectures: 156
- Number of Quizzes: 0
Machine Learning, Data Science and Generative AI with Python, has an average rating of 4.63, with 156 lectures, based on 33597 reviews, and has 219962 subscribers.
You will learn about Build generative AI systems with OpenAI, RAG, and LLM Agents Build artificial neural networks with Tensorflow and Keras Implement machine learning at massive scale with Apache Spark's MLLib Classify images, data, and sentiments using deep learning Make predictions using linear regression, polynomial regression, and multivariate regression Data Visualization with MatPlotLib and Seaborn Understand reinforcement learning – and how to build a Pac-Man bot Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA Use train/test and K-Fold cross validation to choose and tune your models Build a movie recommender system using item-based and user-based collaborative filtering Clean your input data to remove outliers Design and evaluate A/B tests using T-Tests and P-Values This course is ideal for individuals who are Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course. or Technologists curious about how deep learning really works or Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful. or If you have no prior coding or scripting experience, you should NOT take this course – yet. Go take an introductory Python course first. It is particularly useful for Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course. or Technologists curious about how deep learning really works or Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful. or If you have no prior coding or scripting experience, you should NOT take this course – yet. Go take an introductory Python course first.
Learn More About Machine Learning, Data Science and Generative AI with Python
What You Will Learn
- Build generative AI systems with OpenAI, RAG, and LLM Agents
- Build artificial neural networks with Tensorflow and Keras
- Implement machine learning at massive scale with Apache Spark's MLLib
- Classify images, data, and sentiments using deep learning
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Data Visualization with MatPlotLib and Seaborn
- Understand reinforcement learning – and how to build a Pac-Man bot
- Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
- Use train/test and K-Fold cross validation to choose and tune your models
- Build a movie recommender system using item-based and user-based collaborative filtering
- Clean your input data to remove outliers
- Design and evaluate A/B tests using T-Tests and P-Values
4. Mathematical Foundations of Machine Learning
Instructor: Dr Jon Krohn
Essential Linear Algebra and Calculus Hands-On in NumPy, TensorFlow, and PyTorch
Course Highlights:
- Rating: 4.59 ⭐ (6728 reviews)
- Students Enrolled: 128683
- Course Length: 59006 hours
- Number of Lectures: 122
- Number of Quizzes: 1
Mathematical Foundations of Machine Learning, has an average rating of 4.59, with 122 lectures, 1 quizzes, based on 6728 reviews, and has 128683 subscribers.
You will learn about Understand the fundamentals of linear algebra and calculus, critical mathematical subjects underlying all of machine learning and data science Manipulate tensors using all three of the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch How to apply all of the essential vector and matrix operations for machine learning and data science Reduce the dimensionality of complex data to the most informative elements with eigenvectors, SVD, and PCA Solve for unknowns with both simple techniques (e.g., elimination) and advanced techniques (e.g., pseudoinversion) Appreciate how calculus works, from first principles, via interactive code demos in Python Intimately understand advanced differentiation rules like the chain rule Compute the partial derivatives of machine-learning cost functions by hand as well as with TensorFlow and PyTorch Grasp exactly what gradients are and appreciate why they are essential for enabling ML via gradient descent Use integral calculus to determine the area under any given curve Be able to more intimately grasp the details of cutting-edge machine learning papers Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning This course is ideal for individuals who are You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities or You’re a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems or You’re a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline or You’re a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!) It is particularly useful for You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities or You’re a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems or You’re a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline or You’re a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!).
Learn More About Mathematical Foundations of Machine Learning
What You Will Learn
- Understand the fundamentals of linear algebra and calculus, critical mathematical subjects underlying all of machine learning and data science
- Manipulate tensors using all three of the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch
- How to apply all of the essential vector and matrix operations for machine learning and data science
- Reduce the dimensionality of complex data to the most informative elements with eigenvectors, SVD, and PCA
- Solve for unknowns with both simple techniques (e.g., elimination) and advanced techniques (e.g., pseudoinversion)
- Appreciate how calculus works, from first principles, via interactive code demos in Python
- Intimately understand advanced differentiation rules like the chain rule
- Compute the partial derivatives of machine-learning cost functions by hand as well as with TensorFlow and PyTorch
- Grasp exactly what gradients are and appreciate why they are essential for enabling ML via gradient descent
- Use integral calculus to determine the area under any given curve
- Be able to more intimately grasp the details of cutting-edge machine learning papers
- Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning
3. 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: 4.6 ⭐ (24806 reviews)
- Students Enrolled: 134715
- 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 4.6, with 384 lectures, 2 quizzes, based on 24806 reviews, and has 134715 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
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 ⭐ (6966 reviews)
- Students Enrolled: 45683
- 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 6966 reviews, and has 45683 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. 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 ⭐ (191976 reviews)
- Students Enrolled: 1102079
- 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 191976 reviews, and has 1102079 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
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