# 雷火电竞赛程

- 76 Courses | 71h 49m 43s
- 4 Labs | 30h
- Includes Test Prep

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Track 1: ML Programmer
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In this track of the machine learning Skillsoft Aspire journey, the focus is linear regression, computational theory, and training sets.

- 25 Courses | 21h 55m 15s
- 1 Lab | 8h

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Track 2: DL Programmer
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In this track of the machine learning Skillsoft Aspire journey, the focus is neural networks, CNNs, RNNs, and ML algorithms.

- 22 Courses | 21h 19m 49s
- 1 Lab | 8h

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Track 3: ML Engineer
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In this track of the machine learning Skillsoft Aspire journey, the focus is predictive modeling and analytics, ml modeling, and ml architecting.

- 17 Courses | 16h 13m 15s
- 1 Lab | 8h

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Track 4: ML Architect
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In this track of the machine learning Skillsoft Aspire journey, the focus is applied predictive modeling, CNNs and RNNs, and ML algorithms.

- 12 Courses | 12h 21m 24s
- 1 Lab | 6h

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COURSES INCLUDED
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**NLP for ML with Python: NLP Using Python & NLTK**

**NLP for ML with Python: Advanced NLP Using spaCy & Scikit-learn**

**Linear Algebra and Probability: Fundamentals of Linear Algebra**

**Linear Algebra & Probability: Advanced Linear Algebra**

**Linear Regression Models: Introduction**

**Linear Regression Models: Building Models with Scikit Learn & Keras**

**Linear Regression Models: Multiple & Parsimonious**

**Linear Regression Models: Introduction to Logistic Regression**

**Simplifying Regression and Classification with Estimators**

**Computational Theory: Language Principle & Finite Automata Theory**

**Computational Theory: Using Turing, Transducers, & Complexity Classes**

**Model Management: Building Machine Learning Models & Pipelines**

**Model Management: Building & Deploying Machine Learning Models in Production**

**Bayesian Methods: Bayesian Concepts & Core Components**

**Implementing Bayesian Model and Computation with PyMC**

**Bayesian Methods: Advanced Bayesian Computation Model**

**Reinforcement Learning: Essentials**

**Reinforcement Learning: Tools & Frameworks**

**Math for Data Science & Machine Learning**

**Building ML Training Sets: Introduction**

**Building ML Training Sets: Preprocessing Datasets for Linear Regression**

**Building ML Training Sets: Preprocessing Datasets for Classification**

**Linear Models & Gradient Descent: Managing Linear Models**

**Linear Models & Gradient Descent: Gradient Descent and Regularization**

**Final Exam: ML Programmer**

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COURSES INCLUDED
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**Getting Started with Neural Networks: Biological & Artificial Neural Networks**

**Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms**

**Building Neural Networks: Development Principles**

**Building Neural Networks: Artificial Neural Networks Using Frameworks**

**Training Neural Networks: Implementing the Learning Process**

**Training Neural Networks: Advanced Learning Algorithms**

**Improving Neural Networks: Neural Network Performance Management**

**Improving Neural Networks: Loss Function & Optimization**

**Improving Neural Networks: Data Scaling & Regularization**

**ConvNets: Introduction to Convolutional Neural Networks**

**ConvNets: Working with Convolutional Neural Networks**

**Convolutional Neural Networks: Fundamentals**

**Convolutional Neural Networks: Implementing & Training**

**Convo Nets for Visual Recognition: Filters and Feature Mapping in CNN**

**Convo Nets for Visual Recognition: Computer Vision & CNN Architectures**

**Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling**

**Fundamentals of Sequence Model: Language Model & Modeling Algorithms**

**Build & Train RNNs: Neural Network Components**

**Build & Train RNNs: Implementing Recurrent Neural Networks**

**ML Algorithms: Multivariate Calculation & Algorithms**

**ML Algorithms: Machine Learning Implementation Using Calculus & Probability**

**Final Exam: DL Programmer**

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COURSES INCLUDED
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**Predictive Modeling: Predictive Analytics & Exploratory Data Analysis**

**Predictive Modeling: Implementing Predictive Models Using Visualizations**

**Applying Predictive Analytics**

**Planning AI Implementation**

**Automation Design & Robotics**

**ML/DL in the Enterprise: Machine Learning Modeling, Development, & Deployment**

**ML/DL in the Enterprise: Pipelines & Infrastructure**

**Enterprise Services: Enterprise Machine Learning with AWS**

**Enterprise Services: Machine Learning Implementation on Microsoft Azure**

**Enterprise Services: Machine Learning Implementation on Google Cloud Platform**

**Enterprise Architecture: Architectural Principles & Patterns**

**Enterprise Architecture: Design Architecture for Machine Learning Applications**

**Architecting Balance: Designing Hybrid Cloud Solutions**

**Architecting Balance: Hybrid Cloud Implementation with AWS & Azure**

**Refactoring ML/DL Algorithms: Techniques & Principles**

**Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms**

**Final Exam: ML Engineer**

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COURSES INCLUDED
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**Applied Predictive Modeling**

**Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools**

**Implementing Deep Learning: Optimized Deep Learning Applications**

**Applied Deep Learning: Unsupervised Data**

**Applied Deep Learning: Generative Adversarial Networks and Q-Learning**

**Advanced Reinforcement Learning: Principles**

**Advanced Reinforcement Learning: Implementation**

**ML/DL Best Practices: Machine Learning Workflow Best Practices**

**ML/DL Best Practices: Building Pipelines with Applied Rules**

**Research Topics in ML & DL**

**Deep Learning with Keras**

**Final Exam: ML Architect**

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EARN A DIGITAL BADGE WHEN YOU COMPLETE THESE TRACKS
**

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**Digital badges are yours to keep, forever.**