[40% OFF]An Introductory Guide to DataScience and MachineLearning Coupon – Educative.io

An Introductory Guide to Data Science and Machine Learning

With An Introductory Guide to Data Science and Machine Learning you will get a 40% discount on yearly plans and a 20% monthly discount oneducative.io. It is one of the popular courses from educative.io

This course will get you familiar with the state of data science and the related fields such as machine learning and big data.

An Introductory Guide to Data Science and Machine Learning– Developer Discount

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Here is what you will Learn from An Introductory Guide to Data Science and Machine Learning

  1. 1. What is Data Science ?
  2. Data Science vs. Data Analysis vs. Data Engineering
  3. Descriptive and Predictive Analytics
  4. Data Science Life Cycle
  5. Structured vs. Semi-Structured vs. Unstructured Data
  6. The Good Traits of a Data Scientist
  7. 2. Applications of Data Science
  8. Applications in Healthcare and Recommender Systems
  9. Image Analysis
  10. 3. Overview of Libraries
  11. Beautiful Soup (Scraping Data from Simple HTML)
  12. Beautiful Soup (Scraping Data from Html Table)
  13. Scrapy
  14. Numpy Basics
  15. Numpy Array Creation
  16. Numpy Array Manipulation
  17. Sorting Numpy Arrays
  18. Basic Statistics on Numpy Arrays
  19. Broadcasting in Numpy Arrays
  20. Pandas
  21. Spacy Part 1
  22. Spacy Part 2
  23. Seaborn
  24. 4. Probability and Statistics
  25. Probability
  26. Statistics
  27. Joint Probability
  28. Conditional Probability and Bayes Theorem
  29. Measures of Locations
  30. Measures of Variability
  31. Probability Distributions (Binomial and Bernoulli Distributions)
  32. Gaussian Distribution
  33. Poisson Distribution
  34. Skewness and Kurtosis
  35. Sampling Methods
  36. Key Concepts in Statistics
  37. Statistical Hypothesis Testing
  38. 5. Machine Learning Part-1
  39. Machine Learning and its Types
  40. Deep Learning and Recommender Systems
  41. What is Regression ?
  42. Univariate Linear Regression
  43. Multivariate Linear Regression
  44. Feature Scaling
  45. Linear Regression in Scikit Learn
  46. Regularization (Lasso, Ridge, and ElasticNet Regression)
  47. Support Vector Regression
  48. Nearest Neighbour Regression
  49. Decision Tree Regression
  50. Feature Engineering and Categorical Variables Encoding
  51. Numerical Variables Transformation
  52. Feature Selection (Filter Methods)
  53. Feature Selection (Wrapper Methods)
  54. Feature Selection (Intrinsic Methods)
  55. Model Evaluation Measures (Explained Variance Score, MAE, MSE)
  56. Model Evaluation Measures (Median Absolute Error, R^2 Score)
  57. Dummy Regressors
  58. Cross Validation
  59. Case Study: House Prices Prediction using Advanced Regression
  60. 6. Machine Learning Part-2
  61. Types of Classification Problems
  62. Logistic Regression
  63. Support Vector Machines
  64. Decision Trees
  65. Naive bayes Part-1
  66. Naive bayes Part-2
  67. K-Nearest Neighbors
  68. Ensemble Learning Part 1
  69. Ensemble Learning Part 2
  70. XGBoost, Light GBM and CatBoost
  71. Learning Curves
  72. Model Evaluation Part 1
  73. Model Evaluation Part 2
  74. Dummy Estimators and Handling Imbalance Class Problem
  75. Hyper-Parameter Optimization and Kaggle Competition
  76. 7. Machine Learning Part-3
  77. Unsupervised Learning
  78. K-Means Clustering
  79. Hierarchical Clustering
  80. DBSCAN Clustering and Customer Segmentation
  81. Apriori Algorithm and Association Rules
  82. Principal Component Analysis for Dimensionality Reduction
  83. Semi-Supervised Learning Techniques
  84. 8. Deep Learning
  85. What is Deep Learning?
  86. Neural Networks
  87. Feedforward Neural Networks
  88. Backpropagation Part 1
  89. Backpropagation Part 2
  90. Convolutional Neural Network
  91. Recurrent Neural Network
  92. LSTM Networks
  93. 9. Machine Learning Tools and Libraries
  94. Automated Machine Learning
  95. Pandas Profiling and PyCaret
  96. RAPIDS (Using GPU for Fast Computations)
  97. 10. Big Data Tools and Technologies
  98. What is Big Data ?
  99. Hadoop Ecosystem
  100. Map Reduce Framework
  101. Apache Spark and it’s Components
  102. 11. Where to go next ?
  103. Starting Career on Kaggle (Tips)
  104. Recommended Courses from Educative
  105. References and Acknowledgements
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