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ÂHere are some of the must try list of Data Science/Machine Learning courses on Udemy this 2022.

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# Machine Learning A-Zâ„¢: Hands-On Python & R In Data Science:Â

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This is one of the best and popular courses out there to learn and understand Machine Learning from front to back, it is evident that with over 700k+ students enrolled and with over 130k+ ratings it is the highest rated course with rating of 4.5/5 as of end 2020.Â

Definitely, this course would be the good choice to look out for in 2022.Â

**What's In For You:**

1. Machine Learning in Python & R.Â

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2. Making accurate predictions.

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3. Making robust Machine Learning models.

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4. Using Machine Learning for personal purpose.

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5. Handling advanced techniques like Dimensionality Reduction.

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6. Building an army of powerful Machine Learning models and know how to combine them to solve any problem.

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7. Having a great intuition of many Machine Learning models.

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8. Making Powerful analysis.

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9. Creating the strong added value to your business.

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10. Handling specific topics like Reinforcement Learning, NLP and Deep Learning.

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11. Knowing which Machine Learning Model to choose for each type of problem.

# The Data Science Course 2020: Complete Data Science Bootcamp:

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This is one of the biggest courses on Data Science, from "Why Data Science" to "Advanced Case Studies". Along with the practical approach, this course is packed with lots and lots of math required to understand the intuition behind the algorithms.Â

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With over 300k+ students enrolled and 83k ratings it has a rating of 4.5/5 in the platform.

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# What's in for you:

1. This course provides the entire toolkit you need to become a data scientist.Â Â

2. It's your turn to impress interviewers by showing an understanding of the data science field after this course.

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3. Understanding the mathematics behind Machine Learning (an absolute must which other courses donâ€™t teach!).

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4. Learning how to pre-process data.

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5. Start coding in Python and learn how to use it for statistical analysis.

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4. Performing linear and logistic regressions in Python.

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5. Performing cluster and factor analysis.

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6. Being able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn.

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7. UnfoldingÂ the power of deep neural networks.

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8. Using state-of-the-art
Deep Learning frameworks such as Googleâ€™s TensorFlow and Develop a business
intuition while coding and solving tasks with big data.

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9. Improving Machine
Learning algorithms by studying underfitting, overfitting, training,
validation, n-fold cross validation, testing, and how hyperparameters
could improve performance.

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# Complete Machine Learning & Data Science Bootcamp 2022:

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This is the new course on Machine Learning and Data Science which is taught by one of the most popular udemy instructors Andrie Neagoie, his other courses are quite popular in software development domain mainly in web development.Â

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With over 30k+ students and 5k + ratings this course is rated at 4.6/5 with some great reviews.

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# What's In For You:

1. Understanding the Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0.

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2. Use modern tools that big tech companies like Google, Apple, Amazon and Facebook use.

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3. Learning which Machine Learning model to choose for each type of problem.

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4. Real life case studies and projects to understand how things are done in the real world.

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5. Learning best practices when it comes to Data Science Workflow.

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6. Implementing Machine Learning algorithms.

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7. Knowing to improve your Machine Learning Models. Â

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8. Supervised and Unsupervised Learning.

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9. Exploring large datasets using data visualization tools like Matplotlib and Seaborn.

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10. Learning NumPy and how it is used in Machine Learning.

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11. Exploring large datasets and wrangle data using Pandas.

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12. Learning to perform Classification and Regression modelling.

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13. Learning about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry and much more....Â Â

# Python For Data Science And Machine Learning Bootcamp:

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This
is the second most enrolled course on Data Science Bootcamp. Along with the practical approach, this
course is more inclined to practical approach on Machine Learning techniques with a python crash course.Â

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With over 400k+ students enrolled and 90k+ ratings it has a rating of 4.6/5 in the platform. And, the instructor Jose Portilla is famous for his python courses.

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# What's In For You:

1. Using Python for Data Science and Machine Learning.

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2. Using Spark for Big Data Analysis.

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3. Implementing Machine Learning Algorithms.

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4. Learning to use NumPy for Numerical Data.

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5. Learning to use Pandas for Data Analysis.

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6. Learning to use Matplotlib for Python Plotting.

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7. Learning to use Seaborn for statistical plots.

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8. Using Plotly for interactive dynamic visualizations.

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9. Using SciKit-Learn for Machine Learning Tasks.

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10. K-Means Clustering and Support Vector Machines.

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11. Linear and Logistic Regression.

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12. Natural Language Processing and Spam Filters.

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13. Neural Networks.

# Machine Learning, Data Science and Deep Learning with Python:

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This course is a complete hands-on machine learning tutorial with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks with one major captone project. Instructor starts with installing and some refreshers in statistics, probability and python to machine learning real world models.Â

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This course is rated at 4.5/5 with 140k+ students enrolled.

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# What's In For You:

1. Building artificial neural networks with Tensorflow and Keras.

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2. Classifying images, data, and sentiments using deep learning.

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3. Making predictions using linear regression, polynomial regression, and multivariate regression.

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4. Data Visualization Techniques with MatPlotLib and Seaborn.

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5. Implementing machine learning at massive scale with Apache Spark's MLLib.

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6. Understanding reinforcement learning - and how to build a Pac-Man bot.

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7. Classifying data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA.

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8. Using train/test and K-Fold cross validation to choose and tune your models.

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9. Building a movie recommender system using item-based and user-based collaborative filtering.

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10. Cleaning your input data to remove outliers.

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11. Designing and evaluating A/B tests using T-Tests and P-Values. Â

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