Datacamp Machine Learning Scientist with Python Review

Overview

Datacamp Machine Learning Scientist with Python Review

Machine Learning is one of the most popular technologies today which is widely used for Image recognition, virtual personal assistants, online fraud detection, traffic prediction, speech recognition, etc. In this Datacamp Machine Learning Scientist with Python review, you will get all the details about the Datacamp Machine learning course which can help you to master the concepts of Machine learning in an easy way. 

Machine Learning is an application of artificial intelligence with the help of which various software and applications become more proficient in making smart decisions and predicting outcomes.

Machine learning is the technology of future and that’s the reason why various students and professional wants to learn it. 

There are various online learning platforms over the internet where you can learn the basic fundamental of Machine learning but today we will learn about Datacamp and its Machine learning courses. 

Datacamp is a popular online learning platform providing expertise in Data Science and Analytics. Datacamp believes that one can learn Data Science and relevant topics faster with the help of Datacamp’s proven learning methodology.

Datacamp has various courses out of which most of the courses are broadly divided into two main categories – 

  • Skill Track
  • Career Track

Skill track is a collection of courses designed by experts and is meant to provide domain-specific expertise. The courses in the skill track are curated by industry experts that can help you a lot in developing your data skills. 

Career track is a collection of courses that are slightly different from the Skill track. Courses and programs in the Career tracks are highly recommended to that student and professionals who want to advance in their career.

These courses are a bit detailed and lengthy focusing on mastering the specific concept.

Datacamp Machine Learning Scientist with Python Career Track is Datacamp’s Machine Learning course which we will explore in this post. This is the rising and one of the most detailed online courses on Machine Learning that is available over the internet.

This Datacamp Machine Learning course will provide you all the essential skills that are required to become a Machine Learning Scientist.

In this Datacamp Machine Learning Scientist with Python Career Track course, you will learn how to process data for features. You will learn how to train models, assess their performance, tune the required parameter for better performance. 

After this quick introduction about the Datacamp Machine Learning Scientist with Python course, now let’s move to the next section and check out the syllabus of this course in this Datacamp Machine Learning Scientist with Python review.

Syllabus

The syllabus of this Datacamp Machine Learning Scientist with Python career track is divided into 23 courses. These courses are selected by industry experts that can help you a lot in developing your data skills.

In the syllabus of this Machine Learning Scientist with Python course, you will learn about natural language processing, image processing, and some popular libraries such as Spark and Keras.

Let’s explore the courses included in Datacamp Machine Learning Scientist with Python course in this Datacamp Machine Learning Scientist with Python review. 

  1. Supervised Learning with Scikit-Learn

In this course, you will learn how to build and tune predictive models and evaluate their performance on unseen data. The major topics which will be covered in this course are  –

  1. Classification
  2. Regression
  3. Fine-tuning your model
  4. Preprocessing and pipelines

Hugo Bown-Anderson is a Data Scientist at DataCamp and will be your instructor for this course.

2. Unsupervised Learning in Python

In this course, you will learn to cluster, transform, visualize, extract insights from datasets using sci-kit-learn and scipy. The major topics which will be covered in this course are – 

  1. Clustering for dataset exploration
  2. Visualization with hierarchical clustering and t-SNE
  3. Decorrelating your data and dimension reduction
  4. Discovering interpretable features

Benjamin Wilson is Director of Research at lateral.io and will be your instructor for this course.

3. Linear Classifiers in Python

In this course, you will learn the details of linear classifiers like logistic regression and SVM. Major topics covering in this course are – 

  1. Applying logistic regression and SVM
  2. Loss functions
  3. Logistic regression
  4. Support Vector Machines

Mike Gelbart is the instructor at the University of British Columbia and will also be the instructor for this course.

4. Machine Learning with Tree-Based Models in Python

In this course, you will learn how to use tree-based models along with ensembling them for regression and classification using scikit-learn. Major topics covering in this course are – 

  1. Classification and Regression Trees
  2. The Bias-Variance Tradeoff
  3. Bagging and Random Forests
  4. Boosting
  5. Model Tuning

Elie Kawerk is a Data Scientist at Mirum Agency and will be your instructor for this course.

5. Extreme Gradient Boosting with XGBoost

In this course, you will learn the fundamentals of gradient boosting and build state of art machine learning models using XGBoost to solve classification and regression problems. Major topics covering in this course are – 

  1. Classification with XGBoost
  2. Regression with XGBoost
  3. Fine-tuning your XGBoost model
  4. Using XGBoost in pipelines

Sergey Fogelson is VP of Analytics at Viacom and will be your instructor for this course. 

6. Cluster Analysis in Python

In this course, you will learn unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library. Major topics covering in this course are – 

  1. Clustering
  2. Hierarchical Clustering
  3. K-Means Clustering
  4. Clustering in Real World

Shaumik Daityari is a business analyst at American Express and will be your instructor for this course. 

7. Dimensionality Reduction in Python

In this course, you will learn to reduce dimensionality in Python. Major topics covering in this course are – 

  1. Exploring high dimensional data
  2. Feature selection 1, selecting for feature information
  3. Feature selection 2, selecting for model accuracy
  4. Feature extraction 

Jeroen Boeye is a Machine Learning Engineer at Faktion and will be your instructor for this course.

8. Preprocessing for Machine Learning in Python

In this course, you will learn to clean your data for modeling. Major topics covering in this course are – 

  1. Introduction to Data Preprocessing
  2. Standardizing Data
  3. Feature Engineering
  4. Selecting features for modelling
  5. Putting it all together

9. Machine Learning for Time Series Data in Python

In this course, you will learn feature engineering and ML for time series data. Major topics covering in this course are – 

  1. Time Series and Machine Learning Primer
  2. Time Series as Inputs to a Model
  3. Predicting Time Series Data
  4. Validating and Inspecting Time Series Models

Chris Holdgraf is a Fellow at Berkeley Institute for Data Science and will be your instructor for this course. 

10. Feature Engineering for Machine Learning in Python

In this course, you will learn to create new features to improve the performance of your ML models. Major topics covering in this are – 

  1. Creating Features
  2. Dealing with Messy Data
  3. Conforming to Statistical Assumptions
  4. Dealing with Text Data

Robert O’Callaghan is the Director of Data Science at Ordergroove and will be your instructor for this course.

11. Model Validation in Python

In this course, you will learn the basics of model validation creating validated and high-performing models. Major topics covering in this course are – 

  1. Basic Modeling with Scikit-Learn
  2. Validation Basics
  3. Cross-Validation
  4. Selecting the best model with Hyperparameter tuning.

Kasey Jones is a Research Data Scientist and will be your instructor for this course.

12. Machine Learning Fundamentals in Python – Datacamp Signal

In this course, you will have to complete some assignments in order to develop your skills.

13. Introduction to Natural Language Processing in Python

In this course, you will learn the fundamentals of NLP techniques using Python and apply them to extract valuable insights. Major topics covering in this course are – 

  1. Regular expressions and word tokenization
  2. Simple topic identification
  3. Named-entity recognition
  4. Building a “fake news” classifier

Katharine Jarmul is the founder of Kjamistan and will be your instructor for this course. 

14. Feature engineering for NLP in Python

In this course, you will learn the techniques to extract useful insights from text and process them into the format for machine learning. Major topics covering in this course are – 

  1. Basic features and readability scores
  2. Text preprocessing, POS tagging, and NER
  3. N-Gram Models
  4. TF-IDF and similarity scores

Raunak Banik is a Data Scientist at Fractal Analytics and will be your instructor for this course. 

15. Introduction to TensorFlow in Python

In this course, you will learn the fundamentals of neural networks and how to build deep learning models using TensorFlow. Major topics covering in this course are – 

  1. Introduction to TensorFlow
  2. Linear models
  3. Neural Networks
  4. High-Level APIs

Isaiah Hull is an Economist and will be your instructor for this course.

16. Introduction to Deep Learning in Python

In this course, you will learn the fundamentals of neural networks and build deep learning models using Keras 2.0. Major topics covering in this course are – 

  1. Basics of Deep Learning and Neural Networks
  2. Optimizing a neural network with backward propagation
  3. Building deep learning models with Keras
  4. Fine-tuning Keras models

Dan Becker is a Data Scientist and will be your instructor for this course.

17. Introduction to Deep Learning with Keras

In this course, you will start developing deep learning models with Keras. Major topics covering in this course are – 

  1. Introducing Keras
  2. Going Deeper
  3. Improving Your Model Performance
  4. Advanced Model Architectures

Miguel Esteban is a Data Scientist and will be your instructor for this course.

18. Advanced Deep Learning with Keras

In this course, you will build multiple input and output deep learning models using Keras. Major topics covering in this course are – 

  1. The Keras Functional API
  2. Two Input Networks Using Categorical Embeddings, Shared Layers, and Merge Layers
  3. Multiple Inputs: 3 Inputs (and Beyond!)
  4. Multiple Outputs

Zachary Deane-Mayer is a VP, Data Science at DataRobot and will be your instructor in this course.

19. Image Processing in Python

In this course, you will learn to process, transform and manipulate images at your will. Major topics covering in this course are – 

  1. Introducing Image Processing and Scikit-Image
  2. Filters, Contrast, Transformation, and Morphology
  3. Image restoration, Noise, Segmentation, and Contours
  4. Advanced Operations, Detecting Faces and Features

Rebeca Gonzalez is a Data Engineer and will be your instructor in this course. 

20. Image Processing with Keras in Python

In this course, you will learn techniques for image analysis in Python using Deep Learning and convolutional neural networks in Keras. Major topics covering in this course are – 

  1. Image Processing with Neural Networks
  2. Using Convolutions
  3. Going Deeper
  4. Understanding and Improving Deep Convolutional Networks

Ariel Rokem is a Senior Data Scientist at the University of Washington and will be your instructor in this course.

21. Hyperparameter Tuning in Python

In this course, you will learn to tune hyperparameters in Python. Major topics covering in this course are – 

  1. Hyperparameters and Parameters
  2. Grid Search
  3. Random Search
  4. Informed Search

Alex Scriven is a Data Scientist at New South Wales Government and will be your instructor in this course. 

22. Introduction to PySpark

In this course, you will learn the implementation of Data management and machine learning in Spark using the PySpark package. Major topics covering in this course are – 

  1. Getting to know PySpark
  2. Manipulating Data
  3. Getting Started with ML Pipelines
  4. Model tuning and selection

Nick Solomon is a Data Scientist and Lore Dirick is the Director of Data Science Education at Flatiron School, they both will be your instructor for this course.

23. Machine Learning with PySpark

In this course, you will learn how to make predictions with Apache Spark. Major topics covering in this course are – 

  1. Introduction
  2. Classification
  3. Regression
  4. Ensembles and Pipelines

Andrew Collier is a Data Scientist at Exergetic Analytics and will be your instructor in this course.

Check this out -> Datacamp Machine Learning Review

Pricing and Duration

Datacamp Machine Learning Scientist with Python Pricing

Now, let’s move to the pricing and duration of this Datacamp Machine Learning Scientist with Python course in this Datacamp Machine Learning Scientist with Python review. 

This is not a single course, instead, it’s a career track and that’s the reason why it has 23 courses overall. These courses are self-paced which means one can finish them off at his own speed and consistency.

However, the total content duration of this Machine Learning Scientist with Python course is 93 hours. 

Datacamp follows a clear pricing plan for all of its courses, skill tracks, and career tracks. Pricing plans are broadly divided into two different types – 

  1. Personal Plans
  2. Business Plans

Personal Plans, are for students and professionals or individuals looking to learn and develop Data skills for any purpose or interest. Personal Plans are divided into three parts – 

  1. Standard
  2. Premium
  3. Free

Standard Plan is the most popular pricing plan in which you will have to pay $12.42 per month billed annually. This plan will provide you all the essentials to grow your Data Skills. 

Premium Plan is for the learners who want to access all the projects. In the Premium Plan, you will have to pay $33.25 per month billed annually.

A Free plan is also there for the student who wants to try and explore DataCamp while the business plans are for other business purposes.

Want to know more about Datacamp business plans, refer to -> Datacamp for business

Pros and Cons

Datacamp is a rising online platform for learning Data Science, Analytics, and Machine Learning. Their career track program is perfect for those who have basic knowledge but want to join the tech industry as a professional. 

Now, let’s check out the Pros and Cons of the Machine Learning Scientist with Python career track course in this Datacamp Machine Learning Scientist with Python review. 

Pros:

  1. Datacamp has over 250 experts from the Data Science and Analytics industry.
  2. Clear pricing plans.
  3. Variety of courses for Data Science and ML enthusiasts.
  4. Interactive learning videos along with a large number of exercises for good hands-on practice.
  5. Free plan for exploring the initial chapter and assessments.
  6. Skill Track and Career Track classification for better selection of the courses.
  7. “Statement of Accomplishment” credential is provided after successful completion of the course. 

Cons:

  1. They do not provide a recognized and validated certificate. 
  2. Relatively less content for free plan users.

Conclusion

Datacamp skill track and career track have one of the most detailed courses and a pretty easy format to understand. Datacamp is beginner-friendly yet it does not provide enough content free for beginners, you will have to subscribe to their personal plan in order to explore all the content available for beginner to advanced level. 

Datacamp is confident in its proven learning methodology of Assess, Learn, Apply, and Practice. They provide a hands-on learning experience in which you don’t need to install any software, instead, you can execute the code on your browser. 

Datacamp can help you a lot in launching your career as a professional Data Scientist, Machine Learning Scientist, Data Engineer, Data Analyst, Statistician, Programmer, etc. 

FAQ’s

  1. What is a “Statement of Accomplishment”?

Datacamp does not offer a validated certificate after the completion of the course, instead, they provide a “Statement of Accomplishment” non-accredited credential which shows that you have completed a specific track.

  1. What is “XP” in the Datacamp Courses?

XP’s are the point that you collect after successfully completing a video or an exercise. They get summed up as your progress through the course. Further, you can use them for various purposes such as getting a hint for a problem, etc. 

  1. Can I get a job using Datacamp courses?

Definitely, you can get a job using the knowledge and experience gained by the Datacamp courses. Remember Datacamp does not guarantee a job after the completion of their courses, skill, or career track.

  1. What comes with the free account?

With the help of the free plan, you can access and explore the first chapter of the courses.

  1. Is there an option to pause my account?

Ya, you can pause your account for a certain time period after that your subscription will automatically renew. Remember you can only pause your account only if you are a monthly subscriber.

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