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1. Introduction.
00:58
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2. What is Machine Learning.
10:53
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3. Types and Applications of ML.
25:46
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4. AI vs ML.
09:43
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5. Essential Math for ML and AI.
17:04
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1. Intro.
01:20
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2. Loading and Preprocessing the CIFAR10 Dataset.
25:57
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3. Building and Deploying the All-CNN Network Part 1
25:24
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4. Building and Deploying the All-CNN Network Part 2.
20:41
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1. Intro.
01:10
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2. Quality Metrics and Preprocessing Images.
34:08
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3. Image Super Resolution using Deep Learning.
47:23
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1. Intro.
01:02
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2. Feature Engineering.
48:07
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3. Deploying Sklearn Classifiers.
26:58
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1. Intro.
01:06
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2. Preprocessing Images for Clustering.
38:56
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3. Evaluation and Visualization.
28:34
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1. Intro.
00:53
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2. The Elbow Method.
00:00
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3. PCA Compression and Visualization.
00:00
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1. Introduction to Supervised Learning.
13:38
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2. Linear Methods for Classification.
00:00
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3. Linear Methods for Regression.
11:52
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4. Support Vector Machines.
15:42
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5. Basis Expansions.
11:00
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6. Model Selection Procedures.
13:58
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7. Bonus! Supervised Learning Project in Python Part 1.
15:24
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8. Bonus! Supervised Learning Project in Python Part 2.
15:23
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1. Introduction to Unsupervised Learning.
11:36
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2. Association Rules.
11:37
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3. Cluster Analysis.
14:14
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4. Reinforcement Learning.
13:19
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5. Bonus! KMeans Clustering Project.
16:34
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1. Introduction to Neural Networks
14:15
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2. The Perceptron.
12:26
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3. The Backpropagation Algorithm.
10:21
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4. Training Procedures.
12:19
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5. Convolutional Neural Networks.
13:37
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1. Introduction to Real World ML.
15:55
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2. Choosing an Algorithm.
10:34
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3. Design and Analysis of ML Experiments.
08:44
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4. Common Software for ML.
00:00
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1. Setting up OpenAI Gym.
10:47
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2. Building and Training the Network Part 1.
12:44
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3. Building and Training the Network Part 2.
16:14
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1. Intro.
21:54
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2. Board Game Review Prediction – Building the Dataset Part 1.
01:39
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3. Board Game Review Prediction – Building the Dataset Part 2.
16:41
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4. Board Game Review Prediction – Training the Models.
15:18
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1. Intro.
02:13
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2. Credit Card Fraud Detection – The Dataset.
22:23
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3. Credit Card Fraud Detection – The Algorithms.
20:41
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1. Intro.
01:27
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2. Tokenizing, Stop Words, and Stemming.
22:49
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3. Tagging, Chunking, and Named Entity Recognition.
31:55
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4. Text Classification.
23:57
