Master Machine Learning & Data Science

PART 1: A Foundation For Machine Learning and Data Science

• Black-box ML concepts
• A high-level understanding of the 11 stages involved in developing and implementing ML projects
• Python for Machine Learning and Data Science
• Python data types and structures, NumPy data structures, and Pandas data structures
• Pandas data indexing and selection, Operating on Pandas data, Handling missing data, Hierarchical indexing/ multi-indexing
• Combining datasets, aggregation, and grouping
• Working with strings, list-set-dictionary comprehensions, functions, unpacking sequences, and so on
• How to use NumPy for numerical computing, vectorization, broadcasting, data transformation, and so on
• How to use Pandas for data analysis and data manipulation
• Jupyter Notebook commands and markdown codes
• Linear algebra, including the types of linear regression problems and the types of classification problems, and so on
• Statistics, including Why do we need to learn statistics? What are statistical models? What are the different types of statistics available?
• What are mean, median, mode, quartiles, and percentiles? What are range, variance, and standard deviation? What are skewness and kurtosis?
• What are the different types of variables we will be dealing with?
• How are statistics used in various stages of machine learning? and so on
• Probability theory, including the language of Probability theory, Probability Tree, Types of probability, why we need to learn Probability, and so on
• Object-Oriented Programming
• An overview of important libraries used in ML and DS for data processing, data analysis, data manipulation, visualization, and other supporting libraries
• And, much more

PART 2: Machine Learning Project Guidelines™

• A deeper understanding of the 11 stages involved in developing and implementing ML projects
• Best practices to be followed while doing ML projects
• Building a template that you can use for your future ML projects
• Guidelines to Select Evaluation Metrics
• Guidelines to choose ML algorithms to solve specific problem(s)
• How can you visually compare the performances of ML models and select the best-performing model?
• What is data leakage, and how to detect, prevent, and minimize it?
• Importance of converting business problems into analytical problems before building ML models
• How to understand datasets using Exploratory Data Analysis using various tools?
• Detailed approach to Data preprocessing
• How do various Regression and Classification algorithms (Linear, Non-linear, and Ensembles) and Clustering algorithms (K-Means and RFM Analysis) work?
• How to use various ML algorithms such as Linear Regression, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbors, and Support Vector Machines?
• How to use Decision Trees, Random Forest, Gradient Boosting, Extreme Gradient Boosting, K-Means, and so on
• How to apply ML algorithms in Python using Scikit-learn, XGBoost, and other ML libraries?
• How to perform Error Analysis and Troubleshoot Prediction Errors?
• How to tune Hyperparameters to improve Model Performance?
• How to build an appealing visualization using Matplotlib, Seaborn, and Plotly?
• And, much more

Part 3: Model deployment in AWS Cloud

• How to use SageMaker Notebooks for any machine learning task in AWS
• Deploy a production-ready robust Machine Learning application in AWS
• Use Matplotlib, Numpy, Pandas, and Seaborn in SageMaker
• Hyperparameter tuning in AWS
• Overview of MLOps in AWS

Course Description:

Who this course is for:

• Beginners with little programming experience and basic mathematics
• Experienced programmers who want to pursue a career in ML/ Data Science/ AI
• People who have already taken other Machine Learning courses who want to strengthen their skills further and use a well-defined methodology in ML projects with best practices using a standardized project template