Break into AI: I took an online course, what's next?

DeepLearningAI · Beginner ·📐 ML Fundamentals ·5y ago
Welcome to the virtual Learner Community Event hosted by DeepLearning.AI. We have assembled a panel of machine learning practitioners who have gotten into the field from different paths. They will be sharing their first-hand experience and suggestions on how to transition from online courses to landing your first ML job! Topics that you can expect include: - How they started in AI and what they are working on now - Besides taking courses, what else they did as part of building the portfolio - How to know when someone's ready to work in ML industry - The hardest part of learning ML - How to pr…
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1 Forward and Backward Propagation (C1W4L06)
Forward and Backward Propagation (C1W4L06)
DeepLearningAI
 2 deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
DeepLearningAI
 3 deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
DeepLearningAI
 4 deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
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 5 deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
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 6 deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
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 7 deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
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 8 Using an Appropriate Scale (C2W3L02)
Using an Appropriate Scale (C2W3L02)
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 9 Gradient Checking (C2W1L13)
Gradient Checking (C2W1L13)
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 10 Gradient Checking Implementation Notes (C2W1L14)
Gradient Checking Implementation Notes (C2W1L14)
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 11 Learning Rate Decay (C2W2L09)
Learning Rate Decay (C2W2L09)
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 12 Understanding Mini-Batch Gradient Dexcent (C2W2L02)
Understanding Mini-Batch Gradient Dexcent (C2W2L02)
DeepLearningAI
 13 Mini Batch Gradient Descent (C2W2L01)
Mini Batch Gradient Descent (C2W2L01)
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 14 The Problem of Local Optima (C2W3L10)
The Problem of Local Optima (C2W3L10)
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 15 Exponentially Weighted Averages (C2W2L03)
Exponentially Weighted Averages (C2W2L03)
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 16 Tuning Process (C2W3L01)
Tuning Process (C2W3L01)
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 17 Understanding Exponentially Weighted Averages (C2W2L04)
Understanding Exponentially Weighted Averages (C2W2L04)
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 18 Bias Correction of Exponentially Weighted Averages (C2W2L05)
Bias Correction of Exponentially Weighted Averages (C2W2L05)
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 19 Gradient Descent With Momentum (C2W2L06)
Gradient Descent With Momentum (C2W2L06)
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 20 Normalizing Activations in a Network (C2W3L04)
Normalizing Activations in a Network (C2W3L04)
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 21 Hyperparameter Tuning in Practice (C2W3L03)
Hyperparameter Tuning in Practice (C2W3L03)
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 22 Adam Optimization Algorithm (C2W2L08)
Adam Optimization Algorithm (C2W2L08)
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 23 RMSProp (C2W2L07)
RMSProp (C2W2L07)
DeepLearningAI
 24 Fitting Batch Norm Into Neural Networks (C2W3L05)
Fitting Batch Norm Into Neural Networks (C2W3L05)
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 25 Why Does Batch Norm Work? (C2W3L06)
Why Does Batch Norm Work? (C2W3L06)
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 26 Batch Norm At Test Time (C2W3L07)
Batch Norm At Test Time (C2W3L07)
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 27 Softmax Regression (C2W3L08)
Softmax Regression (C2W3L08)
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 28 Deep Learning Frameworks (C2W3L10)
Deep Learning Frameworks (C2W3L10)
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 29 Neural Network Overview (C1W3L01)
Neural Network Overview (C1W3L01)
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 30 Training Softmax Classifier (C2W3L09)
Training Softmax Classifier (C2W3L09)
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 31 Why Deep Representations? (C1W4L04)
Why Deep Representations? (C1W4L04)
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 32 Gradient Descent For Neural Networks (C1W3L09)
Gradient Descent For Neural Networks (C1W3L09)
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 33 Neural Network Representations (C1W3L02)
Neural Network Representations (C1W3L02)
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 34 TensorFlow (C2W3L11)
TensorFlow (C2W3L11)
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 35 Activation Functions (C1W3L06)
Activation Functions (C1W3L06)
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 36 Explanation For Vectorized Implementation (C1W3L05)
Explanation For Vectorized Implementation (C1W3L05)
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 37 Getting Matrix Dimensions Right (C1W4L03)
Getting Matrix Dimensions Right (C1W4L03)
DeepLearningAI
 38 Understanding Dropout (C2W1L07)
Understanding Dropout (C2W1L07)
DeepLearningAI
 39 Building Blocks of a Deep Neural Network (C1W4L05)
Building Blocks of a Deep Neural Network (C1W4L05)
DeepLearningAI
 40 Why Non-linear Activation Functions (C1W3L07)
Why Non-linear Activation Functions (C1W3L07)
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 41 Computing Neural Network Output (C1W3L03)
Computing Neural Network Output (C1W3L03)
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 42 Backpropagation Intuition (C1W3L10)
Backpropagation Intuition (C1W3L10)
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 43 Train/Dev/Test Sets (C2W1L01)
Train/Dev/Test Sets (C2W1L01)
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 44 Deep L-Layer Neural Network (C1W4L01)
Deep L-Layer Neural Network (C1W4L01)
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 45 Random Initialization (C1W3L11)
Random Initialization (C1W3L11)
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 46 Other Regularization Methods (C2W1L08)
Other Regularization Methods (C2W1L08)
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 47 Normalizing Inputs (C2W1L09)
Normalizing Inputs (C2W1L09)
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 48 Derivatives Of Activation Functions (C1W3L08)
Derivatives Of Activation Functions (C1W3L08)
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 49 Parameters vs Hyperparameters (C1W4L07)
Parameters vs Hyperparameters (C1W4L07)
DeepLearningAI
 50 Vectorizing Across Multiple Examples (C1W3L04)
Vectorizing Across Multiple Examples (C1W3L04)
DeepLearningAI
 51 What does this have to do with the brain? (C1W4L08)
What does this have to do with the brain? (C1W4L08)
DeepLearningAI
 52 Dropout Regularization (C2W1L06)
Dropout Regularization (C2W1L06)
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 53 Vanishing/Exploding Gradients (C2W1L10)
Vanishing/Exploding Gradients (C2W1L10)
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 54 Basic Recipe for Machine Learning (C2W1L03)
Basic Recipe for Machine Learning (C2W1L03)
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 55 Bias/Variance (C2W1L02)
Bias/Variance (C2W1L02)
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 56 Forward Propagation in a Deep Network (C1W4L02)
Forward Propagation in a Deep Network (C1W4L02)
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 57 Weight Initialization in a Deep Network (C2W1L11)
Weight Initialization in a Deep Network (C2W1L11)
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 58 Numerical Approximations of Gradients (C2W1L12)
Numerical Approximations of Gradients (C2W1L12)
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 59 Regularization (C2W1L04)
Regularization (C2W1L04)
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 60 Why Regularization Reduces Overfitting (C2W1L05)
Why Regularization Reduces Overfitting (C2W1L05)
DeepLearningAI
⚡ AI Lesson Summary ✦ V3 skills 🛠 Hands-on

The video discusses the journey of breaking into the AI field, with panelists sharing their experiences and suggestions on transitioning from online courses to landing a job in machine learning. They cover topics such as building a portfolio, participating in Kaggle competitions, and fine-tuning interview rounds based on skill set. The video provides practical advice and insights for individuals looking to start a career in machine learning.

Key Takeaways
  1. Build a portfolio by participating in Kaggle competitions and working on personal projects
  2. Fine-tune interview rounds based on skill set and research the company and role
  3. Apply machine learning knowledge to real-world problems and showcase skills on a resume and LinkedIn profile
  4. Read research papers and apply knowledge to real-world problems
  5. Take online courses and participate in data science internships and hackathons to gain practical experience
  6. Network with professionals in the field and attend industry events to learn about new opportunities
  7. Highlight strengths and showcase good knowledge in one area to increase chances of getting hired
  8. Persevere and follow online courses to lead to job opportunities, even without a formal degree
💡 Building a strong portfolio and showcasing skills on a resume and LinkedIn profile can increase chances of getting hired in the machine learning field

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