SkillAI · The Beam

Python Machine Learning Mastery 2026

0/65 concepts·~27 h left

1
Phase 010/5 · ~2 h left

Python Fundamentals

Basic syntax and data types25 min
Control structures: if, for, while25 min
Functions and modules25 min
Data structures: list, dict, set, tuple25 min
File handling: reading and writing files25 min
2
Phase 020/5 · ~2 h left

Data Manipulation with pandas

DataFrames and Series basics25 min
Data cleaning and preprocessing25 min
Data aggregation and grouping25 min
Merging and joining datasets25 min
Time series data handling25 min
3
Phase 030/5 · ~2 h left

Data Visualization with matplotlib and seaborn

Basic plotting with matplotlib25 min
Statistical plots with seaborn25 min
Customizing plots and styles25 min
Creating dashboards with plotly25 min
Visualizing time series data25 min
4
Phase 040/5 · ~2 h left

NumPy for Numerical Computing

Arrays and array operations25 min
Mathematical functions and broadcasting25 min
Linear algebra with numpy.linalg25 min
Random number generation25 min
Performance optimization with NumPy25 min
5
Phase 050/5 · ~2 h left

Statistics and Probability Basics

Descriptive statistics: mean, median, mode25 min
Probability distributions: normal, binomial25 min
Hypothesis testing and p-values25 min
Correlation and covariance25 min
Basic inferential statistics25 min
6
Phase 060/5 · ~2 h left

Machine Learning Basics with scikit-learn

Supervised vs. unsupervised learning25 min
Model evaluation metrics: accuracy, precision25 min
Basic algorithms: linear regression, decision trees25 min
Overfitting and underfitting concepts25 min
Train-test split and cross-validation25 min
7
Phase 070/5 · ~2 h left

Feature Engineering Techniques

Feature selection methods25 min
Creating new features from existing data25 min
Handling categorical variables with OneHotEncoder25 min
Scaling features with StandardScaler25 min
Dimensionality reduction with PCA25 min
8
Phase 080/5 · ~2 h left

Deep Learning Fundamentals with TensorFlow and Keras

Neural network basics and architecture25 min
Building models with Keras API25 min
Training and evaluating deep learning models25 min
Convolutional Neural Networks (CNNs) basics25 min
Recurrent Neural Networks (RNNs) basics25 min
9
Phase 090/5 · ~2 h left

Natural Language Processing with NLTK and spaCy

Text preprocessing techniques25 min
Tokenization and stemming/lemmatization25 min
Named Entity Recognition (NER) with spaCy25 min
Sentiment analysis basics25 min
Text classification with scikit-learn25 min
10
Phase 100/5 · ~2 h left

Model Deployment Techniques

Exporting models with joblib or pickle25 min
Creating REST APIs with Flask or FastAPI25 min
Containerization with Docker25 min
Cloud deployment options (AWS, GCP, Azure)25 min
Monitoring and maintaining deployed models25 min
11
Phase 110/5 · ~2 h left

Advanced Machine Learning Techniques

Ensemble methods: Random Forest, Gradient Boosting25 min
Hyperparameter tuning with GridSearchCV25 min
Anomaly detection techniques25 min
Reinforcement learning basics25 min
Transfer learning in deep learning25 min
12
Phase 120/5 · ~2 h left

Ethics in AI and Machine Learning

Understanding bias in data and models25 min
Fairness and accountability in AI25 min
Privacy concerns and data protection25 min
Ethical implications of AI applications25 min
Regulatory frameworks and compliance25 min
13
Phase 130/5 · ~2 h left

Capstone Project Development

Choosing a real-world problem to solve25 min
Data collection and preprocessing25 min
Model selection and evaluation25 min
Deployment and presentation of results25 min
Gathering feedback and iterating on the project25 min