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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