Beginner friendly Data Science/ML/AI syllabus

If you search for an online course on Data Science, ML and AI , the course content/syllabus that is covered varies from a one day workshop to 4 year long B.Tech/B.E specialization in AI. In this article I have tried to create a 3-4 week long curriculum for beginners in data science/ML. The focus will be on the theoretical aspects that are helpful for Interviews, publishing and understanding the mechanism behind algorithms.

The curriculum is spread over 3 weeks (week 3 can be extended to 4 as per the comfort level of the student) , the weekly content is balanced when it comes to breadth and depth of topics. Although it depends a lot on the reader as to what extent one is planning to spend time on a single topic.

The field is vast and one might find few topics missing, but then a 3 week duration would not be a justified. The aim of this curriculum stands to make you ready for at least 2-3 capstone projects and introduce you to the field in the most detailed manner possible.

Week 1

1. Need of automation, introduction to machine intelligence.
2. What is a dataset? Balanced and imbalanced dataset, static vs temporal data
3. Types of variables/features in a data set.
4. Distributions, need of identifying distributions.
5. Types of distributions
6. Training, cv, testing data, difference in train and test distribution
7. Gaussian distribution, standard normal variate, Chebyshev’s law
8. Real life examples of various distributions (log-normal, power-law etc.)
9. Mean, median, quantiles, variance, Kurtosis, skewness (moments around mean)
10. PDF, CDF
11. Central limit theorem
12. Probability and hypothesis testing
13. Comparing distributions, KS testing
14. QQ plots
15. Transforming distributions
16. Covariance, correlations, Pearson correlation coefficient, spearman rank CC, box-cox transforms
17. Correlation vs causation
18. Matrix factorization, cosine similarity

Topics students need to cover: confidence interval code part: data preprocessing, eda on above topics

19. Supervised, unsupervised and reinforcement learning definitions
20. Feature scaling, handling missing values
21. Outliers, RANSAC
22. Preprocessing categorical values, label encoding, one hot encoding
23. Regression vs classification
24. Bias variance trade-off
25. MSE, log-loss, performance metrics (accuracy, AUC-ROC, TPR, FPR), need for cost-function, differentiability requirements
26. Basics of 3d geometry, hyper-planes, hypercubes, generalization to n dimensions
27. What is a model? Interpretability of a model? Business requirements
28. Domain Knowledge
29. Intro to Logistic regression, sigmoid function and probability interpretation need for regularization formulation of regularization in logistic regression types of regularization, feature sparsity in L1, Hyper-parameter tuning, (manual, grid-search, random-search)

Week 2

1. Linear regression
2. Assumptions of linear regression
3. MAPE, R^2
4. Distance metrics, KNN, problems with KNN, kd trees, LSH (locality sensitive hashing)
5. Clustering algorithms, performance metrics for un-labelled data
6. K means, kmeans++
7. DBSCAN
8. Reachability distance, LOF (Local outlier factor)
9. Revisiting conditional probability
10. Bayes theorem, basics of NLP (STEMMING, STOP WORDS, BOW, TF-IDF)
11. Naive bayes, assumptions, LOG probabilities
12. Laplace smoothing (outlier handling in naive bayes)
13. Naive bayes for continuous variables
14. Dimensionality reduction
15. Curse of dimensionality
16. PCA
17. Eigen vectors, eigen values, linear transformations
18. Langrange Multipliers
19. Solving PCA objective function
20. SNE, T-SNE, KL-Divergence
21. TSNE limitations
22. Intro to Decision Trees
23. Entropy, Gini-impurity, Pruning of trees
24. Splitting nodes for continuous variables

Deep Learning

Week 3

1. Neuron structure, Neural networks
2. Perceptron
3. MLP, weight matrices, hidden layers
4. Gradients, learning rate, saddle points, local and global minimas,
5. Forward propagation and backpropagation
6. GD vs SGD
7. Activation functions, vanishing gradient problems
8. Parameters vs Hyper-parameters of a network
9. Weight initialization techniques
10. Symmetric initialization
11. Random initialization
12. Math behind Xavier/Glorot initialization
13. He weights initialization techniques
14. Contour plots, Batch-Normalization
15. Optimizers
16. Momentum, NAG, Ada-delta, Ada-grad, rmsprop, Adam
17. Soft-max in multi-class classification
18. CNN feature extraction, different layers used in cnns
19. Channels, padding, strides
20. Filters, kernels max, min, average pooling
21. Transfer learning
22. Residual networks
23. Image segmentation (basics)
24. Object Detection (basics), brief discussion on GANS
25. Rnns, sequential information
26. Vanishing gradients
27. Sharing weights (comparison with CNN)
28. Lstms, grus
29. Gates in lstms
30. Encoder-decoder models, context vector
31. Bidirectional networks
32. BLEU score
33. Disadvantages of one hot, bows model, Space efficiency
34. Semantic relation of words
35. Representation of words as vectors-Word embeddings
36. Word2VEC model
37. C-BOW
38. Skip-Gram
39. Embedding matrix
40. Glove vectors
41. Attention mechanism (NLP)
42. Local vs global attention
43. Transformer’s architecture, self-Attention
44. Query, key and value matrices
45. Multi-head and masked attention Intro to BERT (Encoder only stacks) GPT-2, GPT-3 (Decoder-only stacks)