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Instructor: Applied AI Course Duration: 5 mins

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MIDI music generation.
High Level Design of the Solution
1.1
Human Activity Recognition Problem definition
9 min
1.2
Dataset understanding
22 min
1.3
Data cleaning & preprocessing
4 min
1.4
EDA:Univariate analysis.
5 min
1.5
EDA:Data visualization using t-SNE
5 min
1.6
Classical ML models.
13 min
1.7
Deep-learning Model.
15 min
2.1
Self Driving Car :Problem definition.

14 min
2.2
Datasets.
9 min
2.3
Data understanding & Analysis :Files and folders.
5 min
2.4
Dash-cam images and steering angles.
6 min
2.5
Split the dataset: Train vs Test
3 min
2.6
EDA: Steering angles
6 min
2.7
Mean Baseline model: simple
5 min
2.8
Deep-learning model:Deep Learning for regression: CNN, CNN+RNN
10 min
2.9
Batch load the dataset.
6 min
2.10
NVIDIA’s end to end CNN model.
18 min
2.11
Train the model.
13 min
2.12
Test and visualize the output.
11 min
2.13
Extensions.
5 min
4.1
High Level Design of the Solution

4.2
Sentence Vectors and Docker Containerisation

4.3
Indexing using ElasticSearch

4.4
Deployment using Flask APIs, Docker and ElasticSearch

5.1
Building a Smart Gym Assistant from scratch- Part 1
130 min
5.2
Building a Smart Gym Assistant from scratch- Part II
121 min
6.1
Revision Questions
30 min
6.2
External resources for Interview Questions
30 min
7.1
High-level design of a self driving system

7.2
Music Recommendation System
7.3
Speaker identification using audio + Siamese Networks
7.4
Data Quality and Verification @ Uber, Netflix and Amazon
7.5
Data Quality and Verification @ Uber, Netflix and Amazon -- Part-2