Machine Learning and Data Analysis Course Description
1) Data and EDA
a. types of data
b. EDA Best Practices
2) Preprocessing
a. Missing Values and Imputation → Exercise Se02_01 , Exercise Se02_02
b. Scaling Data → Exercise Se02_03
c. Categorical Encoding
Project 1
3) Probability
a. Some Key Concepts, Descriptive statistics, Distribution, Central tendency, Variability
b. Inferential statistics, Descriptive statistics
c. Basic Probability
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i. Axiomatic Probability → Exercise cho1_1
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¡i. Conditional Probability, Bayes' Theorem or Rule → Exercise cho1_2
d. Random Variables and Distribution
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i. Random variable
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ii. Cumulative distribution function (cdf)
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iii. Probability mass function (pmf)
e. Distribution
f. Goodness of fit and Hypothesis test (dist_fitter) → Exercise Ses_04_1
g. Expectation of a Random Variable → Exercise Ses_04_2
h. Variance and Standard Deviation
i. Quantile
j. Skewness and Kurtosis
4) ML Fundamentals
a. Defining
b. Generalized Model
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i. Overfitting
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ii. Underfitting
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iii. Bias, Variance
5) Validation Strategy
a. Hold-Out
b. K-fold
c. Startified
6) Metrics
a. Accuracy
b. Precision
c. Recall
d. ROC_AUC score
e. R' (R-Squared)
f. Mean Squared Error (MSE)
g. Root Mean Squared Error (RMSE)
h. Mean Absolute Error (MAE)
Practice
7) Linear Regression
a. The equation of a line
b. Simple Linear Regression
c. Measures of Variation
d. A simple Example → Jupyter Notebook → Exercise S05_E01
8) Polynomial Regression
a. Nonlinear functions
b. Higher-order polynomials
c. Overfitting and complexity
Practice
9) Logistic Regression
a. The Logistic Function
b. Dilemma using OLS
c. Odds ratio
d. Probability Thresholds
e. Cost Function
f. Optimization Process → Exercise S06_01
10) Regularization
a. Overfitting, Underfitting
b. Occam's razor
c. Bias-Variance Tradeoff
d. Model Selection
e. Regularization (Explain)
f. Ridge & Lasso Regression → Excersice S06_02
11) Classification Metrics
a. accuracy_score
b. confusion_matrix
c. recall_score
d. precision_score
e. fi_score
f. roc_curve
g. roc_auc_score
h. precision_recall_curve
i. average_precision_score(PR AUC)
j. log loss
Project 2:
Full Pipeline Practice (Covid-19 Hospital los)
12) Tree Based Algorithms
a. Decision Trees (CART)
b. Random Forest
c. AdaBoost
d. GBM
e. XGBoost
13) Neural Networks
a. Biological Inspirations
b. Introduction
c. Elements
d. Matrix Operation
e. Activation Functions
f. Training
g. Gradient Descent Algorithm
h. Adam
i. Loss Functions
j. Generalization
k. Batch Normalization
l. Keras → Exercise_ MNIST
m. Convolutional Neural Networks
n. Recurrent Neural Networks
o. LSTM Networks
14) Unsupervised Learning
a. Cluster Analysis
b. DBSCAN
c. Esp and Min Point
d. K-Means
e. PCA
15) Reinforcement Learning (Optional)
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