Full Stack Data Science with Generative AI Training

Python Fundamentals

• Installation, Python Scripts, Jupyter Notebook and Google Colab
• Data Types, Variables, Operators and Type Conversion
• Conditional Statements, Loops and Built-in Functions
• Lists, Tuples, Sets and Dictionaries
• Indexing, Slicing and Comprehensions
• Functions, Scope, Lambda, Map and Reduce

Object-Oriented and Production Python

• Classes, Objects, Constructors and Self
• Instance, Class and Static Methods
• Inheritance, Polymorphism and Abstract Classes
• Decorators, Iterators and Generators
• Exception Handling, File Handling, Modules and Packages
• Pickling and Unpickling

NumPy and Pandas

• NumPy Arrays, Indexing, Slicing and Reshaping
• Array Operations and Statistical Functions
• Pandas Series and DataFrames
• Importing CSV Data and Managing Missing Values
• Loc, Iloc, Filtering, GroupBy, Merge, Join and Concatenation
• Sorting, Date-Time Operations and Data Transformation

Data Visualization and Scientific Libraries

• Matplotlib Figures, Axes and Chart Types
• Seaborn Categorical, Regression and Distribution Plots
• Pair Plots, FacetGrid and Color Palettes
• SciPy: Linear Algebra, Statistics, Integration and Clustering
• Statsmodels: Regression, Statistical Tests, ANOVA and Time Series

Foundations

• Set Theory and Data Representation
• Combinatorics, Permutations and Combinations
• Sampling, Experiment Design and Cross-Validation

Probability and Distributions

• Theoretical and Empirical Probability
• Conditional and Total Probability
• Bayes' Theorem and Naive Bayes
• Binomial, Poisson, Normal, Uniform and Geometric Distributions
• Z-Scores, Skewness, Kurtosis and Markov Chains

Linear Algebra and Calculus

• Vectors, Matrices and Matrix Multiplication
• Determinants, Eigenvalues and Eigenvectors
• Euclidean and Manhattan Distance
• Differentiation, Partial Derivatives and Optimization
• Indices and Logarithms

Descriptive Statistics

• Population, Samples and Sampling Techniques
• Qualitative, Quantitative, Cross-Sectional and Time Series Data
• Mean, Median, Mode and Frequency Distributions
• Skewness and Kurtosis
• Range, Variance, Standard Deviation, IQR, MAD and Covariance

Variables and Visualization

• Nominal, Ordinal, Interval and Ratio Measurement
• Categorical, Numerical, Dependent and Independent Variables
• Control, Moderating and Mediating Variables
• Frequency Tables, Histograms, Scatter Plots and Box Plots

Statistical Learning

• Pearson and Spearman Correlation
• Regression Metrics and Collinearity
• Percentiles, Confidence Intervals and Central Limit Theorem
• Bias-Variance, Entropy, Information Gain and Surprise
• MSE, MAE, Huber and Cross-Entropy Loss

Inferential Statistics

• Null and Alternative Hypotheses
• One- and Two-Tailed Tests, P-Values
• Type I and II Errors
• ANOVA, Chi-Square, Z-Test and T-Test

Introduction to Databases

• DBMS vs RDBMS and SQL vs NoSQL
• MySQL Installation
• Primary and Foreign Keys
• SQL Constraints and CRUD Operations

SQL Querying

• DDL, DQL, DML, DCL and TCL
• CREATE, INSERT, ALTER, UPDATE, DELETE and SELECT
• WHERE, DISTINCT, ORDER BY, GROUP BY, HAVING and LIMIT
• Comparison and Logical Operators
• Wildcards and Aggregate Functions
• INNER, OUTER, LEFT, RIGHT, SELF and CROSS Joins

Exploratory Data Analysis

• Univariate, Bivariate and Multivariate Analysis
• Continuous, Discrete and Time Series Visualization
• Missing Values, Outliers, Scaling and Encoding
• Feature Engineering and Feature Selection

Machine Learning Foundations

• Supervised and Unsupervised Learning
• Train-Test Splitting and Cross-Validation
• Overfitting, Underfitting and Bias-Variance Tradeoff
• PCA, Sparse PCA, Kernel PCA and SVD
• Scikit-learn Fundamentals

Regression and Classification

• Simple, Multiple and Polynomial Regression
• Lasso, Ridge and Elastic Net
• MAE, MSE, RMSE, R-Squared and Adjusted R-Squared
• KNN, Logistic Regression, SVM and Naive Bayes
• Decision Trees and Random Forest
• AdaBoost, Gradient Boosting and XGBoost
• Classification Evaluation Metrics

Unsupervised Learning

• K-Means and Elbow Method
• Hierarchical Clustering and DBSCAN
• Market Basket Analysis and Apriori Algorithm
• Collaborative Recommendation Systems

Forecasting, Optimization and Deployment

• Stationarity, ACF and PACF
• AR, ARMA, ARIMA and SARIMAX
• GridSearchCV and RandomizedSearchCV
• Model Pipelines, Joblib and Pickling
• Flask Model Deployment

Report Design and Visualization

• Power BI Introduction, Installation and Data Sources
• Canvas, Fields, Tables, Treemaps and Charts
• Formatting, Legends, Labels and Exporting
• Slicers, Grouping, Binning and Filtering
• Hierarchies, Drill Down and Drill-Through Reports

Power Query and DAX

• Data and Column Transformations
• Text and Date-Time Transformations
• Merge and Append Queries
• DAX Data Types and Syntax
• Measures and Calculated Columns

Neural Network Fundamentals

• Artificial Neurons and Perceptrons
• Multilayer Perceptrons and Activation Functions
• Loss Functions, Gradient Descent and Backpropagation
• Regularization, Optimizers and Hyperparameter Tuning

Frameworks and Architectures

• PyTorch Fundamentals
• TensorFlow and Keras
• ANN and Forward/Backward Propagation
• RNN, LSTM, GRU and Bidirectional RNN
• Encoder-Decoder Architectures
• BERT and Transformers
• Text Classification and Generation
• Generative AI and ChatGPT Concepts

Image Learning

• Image Histograms and Filters
• CNN Architectures and ImageNet
• Transfer Learning and Image Classification
• YOLOv8

Text Processing

• Text Cleaning, Tokens and Bag of Words
• TF-IDF, Unigrams and Bigrams
• One-Hot Encoding and CountVectorizer
• Word Clouds, Word2Vec and GloVe
• Gensim and Topic Modeling

Language Analysis

• Text Classification and Sentiment Analysis
• TextBlob and NLTK
• Tokenization, Stemming and Lemmatization
• Part-of-Speech Tagging
• Dependency Parsing and Named Entity Recognition
• spaCy Libraries

Computer Vision Fundamentals

• Human and Computer Vision
• CNN Architecture
• Convolution and Max Pooling
• Flattening and Fully Connected Layers
• Striding and Padding
• Data Augmentation
• OpenCV and Image Processing
• YOLOv3 Concepts