Python and Machine Learning Syllabus

August 27, 2021 4 minute read

Fall 2021, 9:00-11:00 am, 2 sessions per week, days TBD

Materials

We will be using Udemy as our learning materials:

Tentative Course Schedule

Course 1: Python A-Z

Week 01 (Sep 06 - Sep 10)
- 1: Welcome To The Course; Core Programming Principles - 1 hr 27 min
  - Udemy: Section 1, 1-6; Section 2, 7-16
  - Topics: Installing Python, variables, loop
  - Homework 1: Law of Large Numbers
  - Quiz 1: Core programming Principles
- 2: Fundamentals of Python - 1 hr 27 min
  - Udemy: Section 7, 66; Section 3, 17-27
  - Topics: Homework 1 solution; Lists, functions, packages, arrays
  - Homework 2: Financial Statement Analysis
  - Quiz 2: Fundamentals of Python
Week 02 (Sep 13 - Sep 17)
- 3: Matrices 1/2 - 1 hr 38 min
  - Udemy: Section 7, 67-68; Section 4, 28-34
  - Topics: Homework 2 solution; Matrices, dictionaries, visualization
- 4: Matrices 2/2; Data Frames 1/2 - 1 hr 35 min
  - Udemy: Section 4, 35-39; Section 5, 40-44
  - Topics: Creating functions, import data, rename, subset, operations
  - Homework 3: Basketball free throws
  - Quiz 3: Matrices
Week 03 (Sep 20 - Sep 24)
- 5: Data Frames 2/2 - 1 hr 29 min
  - Udemy: Section 7, 69; Section 5, 45-51
  - Topics: Homework 3 solution; Filter, Seaborn, keyword arguments
  - Homework 4: World Trends
  - Quiz 4: Data frames
- 6: Advanced Visualization 1/2 - 1 hr 46 min
  - Udemy: Section 7, 70-71; Section 6, 52-58
  - Topics: Homework 4 solution; Category data, JointPlots, common plots
Week 04 (Sep 27 - Oct 01)
- 7: Advanced Visualization 2/2 - 1 hr 52 min
  - Udemy: Section 6, 59-65; Section 7, 72-74; Section 8, 75
  - Topics: Facet grid, build dashboards, Homework 5 solution, bonus lectures
  - Homework 5: Movie Domestic % Gross
  - Quiz 5: Advanced Visualization

Course 2: Machine Learning A-Z

Week 04 (Sep 27 - Oct 01)
- 1: Welcome to the course! - 42 min
  - Udemy: Section 1, 1-14
  - Topics: Intro, resources, data, code
Week 05 (Oct 04 - Oct 08)
- 2: Data Preprocessing - 2 hr 17 min
  - Udemy: Section 2, 15; Section 3, 16-24; Section 4, 25-34
- 3: Simple Linear Regression; Multiple Linear Regression 1/2 - 2 hr 02 min
  - Udemy: Section 5, 35; Section 6, 36-47; Section 7, 48-54
  - Quiz 1: Simple Linear Regression
Week 06 (Oct 11 - Oct 15)
- 4: Multiple Linear Regression 2/2 - 1 hr 35 min
  - Udemy: Section 7, 55-67
  - Quiz 2: Multiple Linear Regression
- 5: Polynomial Regression - 1 hr 52 min
  - Udemy: Section 8, 68-78
Week 07 (Oct 18 - Oct 22)
- 6: Support Vector Regression (SVR) - 1 hr 19 min
  - Udemy: Section 9, 79-87
- 7: Decision Tree Regression; Random Forest Regression - 1 hr 36 min
  - Udemy: Section 10, 88-94; Section 11, 95-98
Week 08 (Oct 25 - Oct 29)
- 8: Evaluating Regression Models Performance; Regression Model Selection in Python and R; Logistic Regression 1/2 - 1 hr 22 min
  - Udemy: Section 12, 99-100; Section 13, 101-104; Section 14, 105-107; Section 15, 108; Section 16, 109
- 9: Logistic Regression 2/2 - 1 hr 53 min
  - Udemy: Section 16, 110-126
  - Quiz 3: Logistic Regression
Week 09 (Nov 01 - Nov 05)
- 10: K-Nearest Neighbors (K-NN); Support Vector Machine (SVM) - 1 hr 18 min
  - Udemy: Section 17, 127-130; Section 18, 131-134
  - Quiz 4: K-Nearest Neighbor
- 11: Kernel SVM; Naive Bayes 1/2 - 1 hr 58 min
  - Udemy: Section 19, 135-142; Section 20, 143-146
Week 10 (Nov 08 - Nov 12)
- 12: Naive Bayes 2/2; Decision Tree Classification; Random Forest Classification - 1 hr 50 min
  - Udemy: Section 20, 147-149; Section 21, 150-153; Section 22, 154-157
- 13: Classification Model Selection; Evaluating Classification Models Performance; K-Means Clustering 1/2 - 1 hr 33 min
  - Udemy: Section 23, 158-159; Section 24, 160-165; Section 25, 166; Section 26, 167-169
Week 11 (Nov 15 - Nov 19)
- 14: K-Means Clustering 2/2; Hierarchical Clustering 1/2 - 1 hr 44 min
  - Udemy: Section 26, 170-176; Section 27, 177-179
  - Quiz 5: K-Means Clustering
- 15: Hierarchical Clustering 2/2; Apriori 1/2 - 1 hr 14 min
  - Udemy: Section 27, 180-189; Section 28, 190; Section 29, 191
  - Quiz 6: Hierarchical Clustering
Week 12 (Nov 22 - Nov 26)
- 16: Apriori 2/2 - 1 hr 53 min
  - Udemy: Section 29, 192-199
- 17: Eclat; Upper Confidence Bound (UCB) 1/2 - 2 hr 03 min
  - Udemy: Section 30, 200-203; Section 31, 204; Section 32, 205-214
Week 13 (Nov 29 - Dec 03)
- 18: Upper Confidence Bound (UCB) 2/2; Thompson Sampling 1/2 - 1 hr 59 min
  - Udemy: Section 32, 215-218; Section 33, 219-226
- 19: Thompson Sampling 2/2; Natural Language Processing 1/2 - 2 hr 5 min
  - Udemy: Section 33, 227-228; Section 34, 229-241
Week 14 (Dec 06 - Dec 10)
- 20: Natural Language Processing 2/2 - 1 hr 24 min
  - Udemy: Section 34, 242-253
- 21: Deep Learning; Artificial Neural Networks 1/2 - 1 hr 35 min
  - Udemy: Section 35, 254-255; Section 36, 256-264
Week 15 (Dec 13 - Dec 17)
- 22: Artificial Neural Networks 2/2 - 2 hr 3 min
  - Udemy: Section 36, 265-276
- 23: Convolutional Neural Networks 1/2 - 1 hr 41 min
  - Udemy: Section 37, 277-285
Week 16 (Dec 20 - Dec 24)
- 24: Convolutional Neural Networks 2/2 - 1 hr 26 min
  - Udemy: Section 37, 286-293
- 25: Principle Component Analysis (PCA); Linear Discriminant Analysis (LDA) - 1 hr 44 min
  - Udemy: Section 38, 294; Section 39, 295-301; Section 40, 302-305
Week 17 (Dec 27 - Dec 31)
- 26: Kernel PCA; Model Selection - 1 hr 47 min
  - Udemy: Section 41, 306-308; Section 42, 309; Section 43, 310-314
- 27: XGBoost - 39 min
  - Udemy: Section 44, 315-319; Section 45, 320

Twitter Facebook LinkedIn