Enroll in DeepLearning.AI's Data Analytics Professional Certificate!
Learn more: https://bit.ly/4oFATRE
Data-informed decision-making is now an essential skill for everyone, from everyday consumer choices to business decisions at all levels. As reliance on data grows, so does the need for professionals who can analyze and interpret it effectively. The Data Analytics Professional Certificate, led by industry leader Sean Barnes, equips you with the skills to manage the entire data lifecycle, from defining problems to delivering insights.
The skills you’ll gain are in high demand, and with data science roles projected to grow 36% from 2023 to 2033 according to the U.S. Bureau of Labor Statistics, developing these skills puts you at the forefront of a data-centric world.
In Data Analytics Foundations, course one of this five-course program, you’ll gain proficiency in the essentials of data analysis, visualization, and communication. You’ll learn to calculate and interpret descriptive statistics, create compelling visualizations, and leverage large language models to assist in your analysis.
Course two, Applied Statistics for Data Analytics, explores the statistical underpinnings of rigorous data analytics. Practical exercises, like developing forest fire prevention strategies, will help you demonstrate impacts in the real world.
Python for Data Analytics, the third course in this series, you’ll move beyond spreadsheets to use Python—the most widely used programming language among data professionals. You’ll develop coding skills that enable you to automate repetitive tasks and analyze datasets of virtually any size, dramatically expanding your analytical capabilities.
Unique to this program is its integration of new AI tools into the analytics workflow. You’ll learn to use large language models as a thought partner, accelerating tasks like simulation modeling, formula debugging, and data visualization. Each of the course examples comes from real-world use cases, building practical and immediately useful skills.
Whether you’re a sof
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Forward and Backward Propagation (C1W4L06)
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deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
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deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
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deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
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deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
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deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
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deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
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Using an Appropriate Scale (C2W3L02)
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Gradient Checking (C2W1L13)
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Gradient Checking Implementation Notes (C2W1L14)
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Learning Rate Decay (C2W2L09)
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Understanding Mini-Batch Gradient Dexcent (C2W2L02)
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Mini Batch Gradient Descent (C2W2L01)
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The Problem of Local Optima (C2W3L10)
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Exponentially Weighted Averages (C2W2L03)
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Tuning Process (C2W3L01)
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Understanding Exponentially Weighted Averages (C2W2L04)
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Bias Correction of Exponentially Weighted Averages (C2W2L05)
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Gradient Descent With Momentum (C2W2L06)
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Normalizing Activations in a Network (C2W3L04)
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Hyperparameter Tuning in Practice (C2W3L03)
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Adam Optimization Algorithm (C2W2L08)
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RMSProp (C2W2L07)
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Fitting Batch Norm Into Neural Networks (C2W3L05)
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Why Does Batch Norm Work? (C2W3L06)
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Batch Norm At Test Time (C2W3L07)
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Softmax Regression (C2W3L08)
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Deep Learning Frameworks (C2W3L10)
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Neural Network Overview (C1W3L01)
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Training Softmax Classifier (C2W3L09)
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Why Deep Representations? (C1W4L04)
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Gradient Descent For Neural Networks (C1W3L09)
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Neural Network Representations (C1W3L02)
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TensorFlow (C2W3L11)
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Activation Functions (C1W3L06)
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Explanation For Vectorized Implementation (C1W3L05)
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Getting Matrix Dimensions Right (C1W4L03)
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Understanding Dropout (C2W1L07)
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Building Blocks of a Deep Neural Network (C1W4L05)
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Why Non-linear Activation Functions (C1W3L07)
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Computing Neural Network Output (C1W3L03)
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Backpropagation Intuition (C1W3L10)
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Train/Dev/Test Sets (C2W1L01)
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Deep L-Layer Neural Network (C1W4L01)
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Random Initialization (C1W3L11)
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Other Regularization Methods (C2W1L08)
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Normalizing Inputs (C2W1L09)
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Derivatives Of Activation Functions (C1W3L08)
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Parameters vs Hyperparameters (C1W4L07)
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Vectorizing Across Multiple Examples (C1W3L04)
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What does this have to do with the brain? (C1W4L08)
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Dropout Regularization (C2W1L06)
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Vanishing/Exploding Gradients (C2W1L10)
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Basic Recipe for Machine Learning (C2W1L03)
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Bias/Variance (C2W1L02)
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Forward Propagation in a Deep Network (C1W4L02)
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Weight Initialization in a Deep Network (C2W1L11)
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Numerical Approximations of Gradients (C2W1L12)
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Regularization (C2W1L04)
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Why Regularization Reduces Overfitting (C2W1L05)
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