Per Class (90 min)
Lecture Duration (60 min)
Q&A (30 min)

Python
1.Introduction to computing (1 Lecture)
2.First steps with python (1 Lecture)
3.Variables and data types (2 Lectures)
4.Branching and loops (2 Lectures)
5.Functions and scope (1 Lecture)
6.Modules and exceptions (1 Lecture)
7.os and file handling (1 Lecture)
8.Hands on project: working with excel using python
Advanced python:
9.Object oriented programming (1 Lectures)
10.Database (1 Lectures)
11.API (1 Lectures)
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in total: 13 classes for python.
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Data Analysis and Visualization:
12.Numpy (1 Lecture)
13.Pandas (2 Lectures)
14.Matplotlib and Seaborn (2 Lectures)
15. Case Study:EDA on Stack overflow survey data.
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in total: 6 classes for data analysis and visualization
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Machine Learning:
16.ML intro (1 Lecture)
17.Linear regression with one variable (2 lectures)
18.Linear regression with multiple variables (1 Lecture)
19.Hands on: coding linear regression (1 Lecture)
20.Tips and tools for building better linear regression models (1 lecture)
21.case study: Medical cost prediction (1 lecture)
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in total: 7 lectures for linear regression
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22.Logistic regression (1 Lecture)
23.Bias variance and regularization (1 lecture)
24.Hands on: coding logistic regression (1 Lecture)
25.case study:rain prediction using climate data (1 Lecture)
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in total: 4 lectures for logictic regression
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Deep Learning:
26. What are neural networks and how they work (1 Lecture)
27.Tensorflow implementation of neural networks (1 Lecture)
28.Training ANN and multiclass classification using softmax(1 Lecture)
29.case study: handwritten digit classification (1 Lecture)
30.Advice for applying machine learning (2 Lectures)
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in total: 6 lectures for deep learning
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31.Decision trees (2 Lectures)
32.Clustering (1 Lecture)
33.Anomaly detection (1 Lecture)
34.Recommender systems (2 Lectures)
35.Reinforcement learning (1 Lecture)
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in total: 7 lectures for advance learning algorithms.
36.End to End machine learning project development and deployment (2 Lectures)
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Total: 45 Lectures

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