Machine Learning With PYTHON – ML Programmer To ML Architect

Machine Learning Architects interpret real-time analysis of data to automate and increase efficiency across all business domains, setting the stage for meaningful AI that moves from reactive to predictive. This Journey will guide you in the transition from becoming an ML Programmer to an ML/DL Architect Master through mechanisms such as computational theory.

This learning path, with more than 100 hours of online content, is divided into the following four tracks:

• ML Track 1: Machine Learning Programmer
• ML Track 2: Deep Learning Programmer
• ML Track 3: Machine Learning Engineer
• ML Track 4: Machine Learning Architect

Track 1: Machine Learning Programmer

In this track of the machine learning journey, the focus is linear regression, computational theory, and training sets.

Content:

E-learning courses

• NLP for ML with Python
• Linear Algebra and Probability
• Linear Regression Models
• Computational Theory
• Model Management
• Bayesian Methods
• Reinforcement Learning
• Math for Data Science & Machine Learning
• Building ML Training Sets
• Linear Models & Gradient Descent

Online Mentor

• You can reach your Mentor 24/7 by entering chats or submitting an email.

Final Exam assessment

• Estimated duration: 90 minutes

Practice Labs: Machine Learning Programming with Python (estimated duration: 8 hours)

• Perform ML programming tasks with Python, such as splitting data and standardizing data, and classification using nearest neighbors and ridge regression. Then, test your skills by answering assessment questions after performing principal component analysis, visualizing correlations, training a naive Bayes model and a support vector machine model. This lab provides access to several tools commonly used in ML, including:
  • Microsoft Excel 2016, Visual Studio Code, Anaconda, Jupyter Notebook + JupyterHub, Pandas, NumPy, SiPy, Seaborn Library, Spyder IDE

Track 2: Deep Learning Programmer

In this track of the machine learning journey, the focus is neural networks, CNNs, RNNs, and ML algorithms.

Content:

E-learning courses

• Getting Started with Neural Networks
• Building Neural Networks
• Training Neural Networks
• Improving Neural Networks
• ConvNets
• Convolutional Neural Networks
• Convo Nets for Visual Recognition
• Fundamentals of Sequence Model
• Build & Train RNNs
• ML Algorithms

Online Mentor

• You can reach your Mentor 24/7 by entering chats or submitting an email.

Final Exam assessment

• Estimated duration: 90 minutes

Practice Labs: Deep Learning Programming with Python (estimated duration: 8 hours)

• Perform DL programming tasks with Python, such as performing series expansion and calculus, and work with TensorFlow and scikit-image. Then, test your skills by answering assessment questions after loading a data set for hierarchical clustering and k-means clustering, and train a model using random forests and gradient boosting.

Track 3: Machine Learning Engineer

In this track of the machine learning journey, the focus is predictive modeling and analytics, ml modeling, and ml architecting.

Content:

E-learning collections

• Predictive Modeling
• Planning AI Implementation
• ML/DL in the Enterprise
• Enterprise Services
• Architecting Balance
• Enterprise Architecture
• Refactoring ML/DL Algorithms

Online Mentor

• You can reach your Mentor 24/7 by entering chats or submitting an email.

Final Exam assessment

• Estimated duration: 90 minutes

Practice Labs: Architecting ML/DL Apps with Python (estimated duration: 8 hours)

• Perform architecting tasks such as binning data, imputing values, performing cross validation, and evaluating a classification model. Then, test your skills by answering assessment questions after validating a model, tuning parameters, refactoring a machine learning model, and saving and loading models using Python.

Track 4: Machine Learning Architect

In this track of the machine learning journey, the focus is applied predictive modeling, CNNs and RNNs, and ML algorithms.

Content:

E-learning collections

• Applied Predictive Modeling
• Implementing Deep Learning
• Applied Deep Learning
• Advanced Reinforcement Learning
• ML/DL Best Practices
• Research Topics in ML and DL
• Deep Learning with Keras

Online Mentor

• You can reach your Mentor 24/7 by entering chats or submitting an email.

Final Exam assessment

• Estimated duration: 90 minutes

Practice Labs: Architecting Advanced ML/DL Apps with Python (estimated duration: 8 hours)

• Perform advanced ML/DL app architecture tasks using Python, such as loading a data set to train a simple multilayer perceptron (MLP), a Convolutional Neural Network (CNN) and an LSTM model. Then, test your skills by answering assessment questions after performing image and text classification using CNN.