Machine Learning tutorials for beginners in hindi + english
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Course content
1 modules • 100 lessons • 18 hours of video
Machine Learning tutorials for beginners in hindi + english
100 lessons
• 18 hours
Machine Learning tutorials for beginners in hindi + english
100 lessons
• 18 hours
- Introduction of Machine Learning - lecture 1/ Machine Learning 11:04
- Scope and limitations of machine learning - lecture 2/ machine learning 11:37
- Types of Machine Learning - Supervised Learning - lecture 3/ machine learning 09:07
- Types of Supervised Learning - lecture 4/ machine learning 08:56
- Types of Machine Learning - Unsupervised Learning - lecture 5/machine learning 09:45
- Types of unsupervised learning - lecture 6/ machine learning 08:20
- Types of Machine Learning - Reinforcement Learning - lecture 7/machine learning 12:46
- Difference between Supervised , Unsupervised and Reinforcement Learning - lecture 8/ machine learnin 12:26
- Linear Regression - lecture 9/ machine learning 16:51
- Numericals on Linear Regression - lecture 10/machine learning 09:22
- Data preparation for machine learning - Data collection - lecture 11/ machine learning 10:45
- Data Preprocessing - lecture 12/ machine learning 14:15
- Data transformation - lecture 13/ machine learning 11:16
- Data Visualization and types of charts used - lecture 14/ machine learning 18:27
- Biological neural network ( BNN)- lecture 15/ machine learning 11:07
- Artificial neural network (ANN) - lecture 16/ machine learning 14:31
- Comparison between biological neural network and artificial neural network - lecture 17/ machine lea 05:57
- Activation function and it's types - lecture 18/ machine learning 07:18
- Binary step / Threshold activation function - lecture 19/ machine learning 04:22
- Sigmoid / logistic activation function - lecture 20/ machine learning 04:55
- Tanh / Hyperbolic Tangent activation function - lecture 21/ machine learning 02:29
- ReLU and Leaky ReLU activation function - lecture 22/ machine learning 06:52
- Terms used in Artificial Neural Network - lecture 23/ machine learning 12:48
- Multilayer artificial neural network - lecture 24/ machine learning 07:27
- Representation of weight and bias in multilayer artificial neural network - lecture 25/machine learn 14:42
- Loss function in machine learning - lecture 26/ machine learning 08:35
- Square Error Loss / Mean Square Error ( MSE). - lecture 27/ machine learning 07:59
- Absolute Error Loss/ Mean Absolute Error ( MAE) - lecture 28/ machine learning 04:03
- Huber loss function - lecture 29/ machine learning 04:21
- Hinge loss/ Multiclass SVM loss function - lecture 30/machine learning 08:07
- Cross entropy loss function - lecture 31 / machine learning 10:28
- Gradient Descent - lecture 32/ machine learning 14:59
- Types of gradient descent - lecture 33/ machine learning 02:33
- Algorithm of gradient descent and use of hyper parameter eta - lecture 34/ machine learning 08:57
- Backpropagation - lecture 35/ machine learning 12:10
- Steps involved in backpropagation - lecture 36/ machine learning 12:51
- Vanishing gradient problem - lecture 37/ machine learning 18:03
- Exploding gradients problem - lecture 38/ machine learning 04:20
- Weight initialization and it's techniques -lecture 39/ machine learning 12:21
- Training and Testing in machine learning - lecture 40/machine learning 09:47
- Overfitting and Underfitting problem in machine learning - lecture 41/ machine learning 09:25
- Regularization - lecture 42/ machine learning 16:08
- L1 Regularization / Lasso Regularization/ Lasso Regression - lecture 43/machine learning 08:15
- L2 Regularization/ Ridge Regularization/ Ridge Regression - lecture 44/ machine learning 03:41
- Dropout - lecture 45/ machine learning 08:57
- Auto Encoders - lecture 46/ machine learning 11:57
- Batch Normalization - lecture 47/ machine learning 21:48
- Momentum - lecture 48/ machine learning 14:26
- Difference between model parameters and hyper parameters - lecture 49/ machine learning 08:22
- Hyperparameter Tuning Techniques - lecture 50/ machine learning 12:49
- Softmax activation function - lecture 51/ machine learning 09:31
- CNN - Convolution Neural Network - lecture 52/ machine learning 13:43
- Convolution layers Operation in CNN - lecture 53/ machine learning 19:27
- Use of Padding and ReLu activation function in CNN - lecture 54/ machine learning 12:03
- Pooling Layer operation in CNN - lecture 55/ machine learning 10:17
- Flattening and Fully connected layer in CNN - lecture 56/ machine learning 08:35
- Working and training of convolution neural network ( CNN) -lecture 57/ machine learning 06:31
- Example1 of CNN using 3D image and without Max Pooling - lecture 58/ machine learning 06:57
- Example 2 of CNN using 3 D image with Max Pooling - lecture 59/ machine learning 05:26
- Advantages of CNN - lecture 60/ machine learning 02:29
- Vertical and Horizontal Edge Detection using CNN - lecture 61/machine learning 09:58
- Problem in CNN - lecture 62/ machine learning 06:19
- 1x1 Convolution - lecture 63/ machine learning 21:58
- Inception network - lecture 64/ machine learning 18:28
- Difference between machine learning and transfer learning - lecture 65/ machine learning 10:58
- Transfer learning - lecture 66/ machine learning 07:23
- One shot learning - lecture 67/ machine learning 20:37
- Need of Recurrent Neural Network (RNN) - lecture 68/ machine learning 10:00
- Difference between Feed Forward Neural Network and Recurrent Neural Network - lecture 69/ machine le 14:57
- Working of RNN - lecture 70/ machine learning 14:07
- Applications of RNN - lecture 71/ machine learning 08:39
- Various architecture of RNN - lecture 72/ machine learning 03:28
- Backpropagation Through Time( BPTT) algorithm in RNN - lecture 73/ machine learning 14:47
- Limitations of RNN - lecture 74/ machine learning 04:31
- Need of Long Short Term Memory (LSTM) - lecture 75/ machine learning 09:02
- General introduction of LSTM - lecture 76/ machine learning 10:17
- Working of LSTM ( part 1) - lecture 77/ machine learning 14:37
- Working of LSTM ( part 2) - lecture 78/machine learning 07:01
- Gated Recurrent Unit (GRU) - lecture 79/ machine learning 09:15
- Machine translation using Encoder-Decoder Model - lecture 80/ machine learning 13:59
- Beam Search and Width - lecture 81/ machine learning 19:37
- BLEU Score ( part 1) -lecture 82/ machine learning 12:17
- BLEU Score ( part 2) - lecture 83/ machine learning 18:36
- Attention Model ( part 1) - lecture 84/ machine learning 14:22
- Attention Model ( part 2) - lecture 85/ machine learning 12:30
- Overview of Reinforcement learning - lecture 86/ machine learning 12:22
- Terms used in reinforcement learning - lecture 87/ machine learning 06:53
- Markov Decision Process and Markov property - lecture 88/ machine learning 10:58
- Parts of Markov Decision Process model - lecture 89/ machine learning 05:09
- Value Function - lecture 90/ machine learning 18:31
- Bellman Equation - lecture 91/ machine learning 07:42
- Dynamic programming and it's algorithms - lecture 92/ machine learning 11:09
- Value iteration algorithm - lecture 93/ machine learning 17:55
- Policy iteration algorithm - lecture 94/ machine learning 15:29
- Difference between model based and model free reinforcement learning - lecture 95/ machine learning 10:21
- Actor - Critic Model - lecture 96/ machine learning 11:02
- Q Learning ( part 1) - lecture 97/ machine learning 12:28
- Q Learning ( part 2) - lecture 98/machine learning 16:52
- SARSA - lecture 99/machine learning 12:23
- Difference between Q learning and SARSA - lecture 100/machine learning 09:55
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