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1. Setup Python environment
2. How to start jupyter notebook
3. Open Jupyter Notebook in Browser of your Choice
4. Install and check Packages
5. Arithmetic operations
6. Comparison or logical operations
7. Assignment and augmented assignment in Python
8. Variables naming conventions
9. Types of variables in Python and typecasting
10. Python Functions
11. Exception handling in Python
12. String manipulation and indexing
13. Conditional and loops in Python
14. Python data structure and containers
15. Introduction to Python Numpy
16. Introduction to Python SciPy
17. Conduct One Sample and Two Sample Equality of Means T Test in Python
18. Introduction to Python Pandas
19. Python pivot tables
20. Pandas join tables
21. Missing value treatment
22. Dummy coding of categorical variables 
23. Outliers, Missing Values, Dummy Coding
24. Exploratory Data Analysis using Pandas-Profiling Package
25. Basic statistics and visualization
26. Data standardization or normalization
27. Machine Learning & Data Science Intro and FAQs
28. Linear Regression with scikit- learn (Machine Learning library)
29. Lasso, Ridge and Elasticnet Regularization in GLM
30. Classification Algorithm Evaluation Metrics
31. Logistic Regression with scikit- learn (Machine Learning library)
32. Hierarchical clustering with Python
33. K-means clustering with Scikit Python
34. Decision trees using Scikit Python
35. Decision Trees Basics and Modeling
36. Regression Decision Trees with Scikit Python
37. Support Vector Machine using Scikit Python
38. Hyperparameters Optimization using Gridsearch and Cross Validations
39. Principal Component Analysis (PCA) using Scikit Python- Dimension Reduction
40. Linear Discriminant Analysis (LDA) using Scikit Python- Dimension Reduction and Classification
41. Market Basket Analysis or Association Rules or Affinity Analysis or Apriori Algorithm
42. Recommendation Engines using Scikit-Surprise
43. Price Elasticity of Demand using Log-Log Ordinary Least Square (OLS) Model
44. Timeseries Forecasting using Facebook Prophet Package
45. Timeseries Forecasting using Pyramid ARIMA Package
46. Model Persistence and Productionalization Using Python Pickle
47. Deep Learning- Introduction to deep learning and environment setup
48. Deep Learning- Multilayer perceptron (MLP) in Python
49. Deep Learning- Convolution Neural Network (CNN) in Python
50. Wordcloud using Python nltk library
51. How to install H2O.ai web UI or flow for Machine Learning and Deep Learning
52. Introduction to Ensemble Modeling and working example on Random Forest
53. Face Recognition using Python Open Source Libraries
54. Tweets Extraction and Sentiment Analysis using Tweepy and NLTK
55. Introduction to AutoGluon and Building a Classification Model 

Cheers!