Career Opportunities in Data Science & AI Specialist
Duration:
6 Months
About Advance AI
The Advanced AI course provides in-depth knowledge of cutting-edge AI techniques, including deep learning, reinforcement learning, natural language processing, and computer vision. Students explore advanced topics such as generative adversarial networks (GANs), transformer models, and ethical considerations in AI. Hands-on projects and real-world applications empower students to master complex AI concepts and contribute to the forefront of AI research and development.
Course Curriculum:
Data Science & AI Specialist
- Introduction to Data Science
- Introduction to Data Science
- Discussion on Course Curriculum
- Introduction to Programming
- Python Basics
- Introduction to Python: Installation and Running (Jupyter Notebook, .py file from terminal, Google Colab)
- Data Types and Type Conversion
- Variables
- Operators
- Flow Control: If, Elif, Else
- Loops
- Python Identifier
- Building Functions (print, type, id, sys, len)
- Python - Data Types & Utilities
- List, List of Lists and List Comprehension
- List Creation
- Create a List with Variable
- List Mutable Concept
- len(), append(), pop()
- insert(), remove(), sort(), reverse()
- Forward Indexing
- Backward Indexing
- Forward Slicing
- Backward Slicing
- Step Slicing
- Set
- Set Creation with Variable
- len(), add(), remove(), pop()
- union(), intersection(), difference()
- Tuple
- Tuple Creation
- Create Tuple with Variable
- Tuple Immutable Concept
- len(), count(), index()
- Forward Indexing
- Backward Indexing
- Dictionary and Dictionary Comprehension
- Create a Dictionary Using Variable
- Keys: Values Concept
- len(), keys(), values(), items()
- get(), pop(), update()
- Comparison of Data Structures
- Introduction to range()
- Pass range() in the List
- range() Arguments
- For Loop Introduction Using range()
- Functions
- Inbuilt vs User Defined
- User Defined Function
- Function Argument
- Types of Function Arguments
- Actual Argument
- Global Variable vs Local Variable
- Anonymous Function (Lambda)
- Packages
- Map Reduce
- OOPs
- Class & Object
- What is an Inbuilt Class
- How to Create a User Class
- Create a Class & Object
- __init__ Method
- Python Constructor
- Constructor, Self & Comparing Objects
- Instance Variable & Class Variable
- Methods
- What is Instance Method
- What is Class Method
- What is Static Method
- Accessor & Mutator
- Python Decorator
- How to Use Decorator
- Inner Class, Outer Class
- Inheritance
- Polymorphism
- Duck Typing
- Operator Overloading
- Method Overloading
- Method Overriding
- Magic Method
- Abstract Class & Abstract Method
- Iterator
- Generators in Python
- Python - Production Level
- Error / Exception Handling
- File Handling
- Docstrings
- Modularization
- Pickling & Unpickling
- Pandas
- Introduction, Fundamentals, Importing Pandas, Aliasing, DataFrame
- Series – Introduction & Creation
- NaN Value
- Series Attributes
- Series Methods
- DataFrame
- Loading Different Files
- DataFrame Attributes
- DataFrame Methods
- Rename Column & Index
- Inplace Parameter
- Handling Missing or NaN Values
- iLoc and Loc
- Filtering
- Sorting
- GroupBy
- Merging or Joining
- Concat
- Adding/Dropping Columns & Rows
- Date and Time
- Concatenate Multiple CSV Files
- NumPy
- Introduction & Installation
- Create NumPy Arrays
- Array Manipulation
- Mathematical Operations
- Indexing & Slicing
- NumPy Attributes
- Important Methods
- Adding Values to Arrays
- Diagonal of a Matrix
- Trace of a Matrix
- Matrix Operations
- Statistical Functions
- Filtering in NumPy
- Matplotlib
- Introduction
- Pyplot
- Figure Class
- Axes Class
- Setting Limits and Tick Labels
- Multiple Plots
- Legend
- Different Types of Plots
- Line Graph
- Bar Chart
- Histograms
- Scatter Plot
- Pie Chart
- 3D Plots
- Working with Images
- Customizing Plots
- Seaborn
- catplot()
- stripplot()
- boxplot()
- violinplot()
- pointplot()
- barplot()
- relplot()
- scatterplot()
- regplot()
- lmplot()
- FacetGrid()
- Multi-plot Grids
- Statistical Plots
- Color Palettes
- Faceting
- Regression Plots
- Distribution Plots
- Categorical Plots
- Pair Plots
- SciPy
- Signal and Image Processing
- Linear Algebra
- Integration
- Statistics
- Spatial Distance and Clustering
- Statsmodels
- Linear Regression
- Time Series Analysis
- Statistical Tests
- ANOVA
- Set Theory
- Data Representation & Database Operations
- Combinatorics
- Feature Selection
- Permutations and Combinations for Sampling
- Hyperparameter Tuning
- Experiment Design
- Data Partitioning and Cross-Validation
- Probability
- Basics
- Theoretical Probability
- Empirical Probability
- Addition Rule
- Multiplication Rule
- Conditional Probability
- Total Probability
- Probability Decision Tree
- Bayes Theorem
- Sensitivity & Specificity in Probability
- Bernoulli Naïve Bayes
- Gaussian Naïve Bayes
- Multinomial Naïve Bayes
- Distributions
- Binomial Distribution
- Poisson Distribution
- Normal Distribution
- Standard Normal Distribution
- Gaussian Distribution
- Uniform Distribution
- Z Score
- Skewness
- Kurtosis
- Geometric Distribution
- Hypergeometric Distribution
- Markov Chain
- Linear Algebra
- Linear Equations
-
Matrices
- Matrix Algebra
- Vector Matrix Multiplication
- Matrix Matrix Multiplication
- Determinant
- Eigen Value and Eigen Vector
- Euclidean Distance & Manhattan Distance
- Calculus
- Differentiation
- Partial Differentiation
- Maximum & Minimum (Max & Min)
- Indices & Logarithms
- Introduction
- Population & Sample
- Reference & Sampling Technique
- Types of Data
- Qualitative or Categorical – Nominal & Ordinal
- Quantitative or Numerical – Discrete & Continuous
- Cross Sectional Data & Time Series Data
- Measures of Central Tendency
- Mean
- Mode
- Median
- Frequency Distribution
- Descriptive Statistics – Measures of Symmetry
- Skewness
- Positive Skew
- Negative Skew
- Zero Skew
- Kurtosis
- Leptokurtic
- Mesokurtic
- Platykurtic
- Skewness
- Measurement of Spread
- Range
- Variance
- Standard Deviation
- Measures of Variability
- Interquartile Range (IQR)
- Mean Absolute Deviation (MAD)
- Coefficient of Variation
- Covariance
- Levels of Data Measurement
- Nominal
- Ordinal
- Interval
- Ratio
- Variable
- Types of Variables
- Categorical Variables
- Nominal Variable
- Ordinal Variable
- Numerical Variables
- Discrete
- Continuous
- Dependent Variable
- Independent Variable
- Control, Moderating & Mediating Variables
- Frequency Distribution Table
- Nominal
- Ordinal
- Interval
- Ratio
- Relative Frequency
- Cumulative Frequency
- Histogram
- Scatter Plots
- Range
- Calculate Class Width
- Create Intervals
- Count Frequencies
- Construct the Table
- Correlation, Regression & Collinearity
- Pearson Correlation Method
- Spearman Correlation Method
- Regression Error Metrics
- Others
- Percentiles
- Quartiles
- Interquartile Range (IQR)
- Different Types of Plots for Continuous & Categorical Variables
- Box Plot
- Outliers
- Confidence Intervals
- Central Limit Theorem
- Degree of Freedom
- Bias and Variance in Machine Learning
- Entropy in Machine Learning
- Information Gain
- Surprise in Machine Learning
- Loss Function & Cost Function
- Mean Squared Error (MSE)
- Mean Absolute Error (MAE)
- Huber Loss Function
- Cross Entropy Loss Function
- Inferential Statistics
- Hypothesis Testing
- One-Tailed Test
- Two-Tailed Test
- P-Value
- Formulation of Null & Alternate Hypothesis
- Type-I Error
- Type-II Error
- Statistical Tests
- Sample Test
- ANOVA Test
- Chi-Square Test
- Z-Test
- T-Test
- Hypothesis Testing
- Introduction
- DBMS vs RDBMS
- Introduction to SQL
- SQL vs NoSQL
- MySQL Installation
- Keys
- Primary Key
- Foreign Key
- Constraints
- Unique
- NOT NULL
- CHECK
- DEFAULT
- AUTO INCREMENT
- CRUD Operations
- Create
- Retrieve
- Update
- Delete
- SQL Languages
- Data Definition Language (DDL)
- Data Query Language (DQL)
- Data Manipulation Language (DML)
- Data Control Language (DCL)
- Transaction Control Language (TCL)
- SQL Commands
- Create
- Insert
- Alter
- Modify
- Rename
- Update
- Delete
- Truncate
- Drop
- Grant
- Revoke
- Commit
- Rollback
- Select
- SQL Clauses
- WHERE
- DISTINCT
- ORDER BY
- GROUP BY
- HAVING
- LIMIT
- Operators
- Comparison Operators
- Logical Operators
- Membership Operators
- Identity Operators
- Wildcards
- Aggregate Functions
- COUNT()
- SUM()
- AVG()
- MIN()
- MAX()
- SQL Joins
- Inner Join
- Outer Join
- Left Join
- Right Join
- Self Join
- Cross Join
- Natural Join
- EDA (Exploratory Data Analysis)
- Univariate Analysis
- Bivariate Analysis
- Multivariate Analysis
- Data Visualization
- Various Plots on Different Data Types
- Plots for Continuous Variables
- Plots for Discrete Variables
- Plots for Time Series Variables
- Machine Learning Introduction
- What is Machine Learning?
- Types of Machine Learning Methods
- Classification Problems
- Validation Techniques (Cross Validation, OOB)
- Classification Metrics
- Curse of Dimensionality
- Feature Transformations
- Feature Selection
- Imbalanced Dataset and Its Effect on Classification
- Bias-Variance Tradeoff
- Important Elements of Machine Learning
- Multiclass Classification
- One-vs-All (OvA)
- Overfitting and Underfitting
- Error Measures
- PCA Learning
- Statistical Learning Approaches
- Introduction to Scikit-Learn Framework
- Data Processing
- Creating Training and Test Sets
- Data Scaling and Normalization
- Feature Engineering
- Data Cleaning (Missing Values & Outliers)
- Data Wrangling
- Encoding Techniques
- Feature Transformations
- Feature Scaling
- Feature Selection
- Filter Methods
- Wrapper Methods
- Embedded Methods
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- Sparse PCA
- Kernel PCA
- Singular Value Decomposition (SVD)
- Non-Negative Matrix Factorization (NMF)
- Regression
- Introduction to Regression
- Mathematics Involved in Regression
- Regression Algorithms
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Lasso Regression
- Ridge Regression
- Elastic Net Regression
- Evaluation Metrics for Regression
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R² Score
- Adjusted R² Score
- Classification
- Introduction
- K-Nearest Neighbors (KNN)
- Logistic Regression
- Support Vector Machines (Linear SVM)
- Linear Classification
- Kernel-Based Classification
- Non-Linear Examples
- 2 Features Form a Straight Line
- 3 Features Form a Plane
- Hyperplane and Support Vectors
- Controlled Support Vector Machines
- Support Vector Regression (SVR)
- Kernel SVM (Non-Linear SVM)
- Naïve Bayes
- Decision Trees
- Random Forest / Bagging
- AdaBoost
- Gradient Boosting
- XGBoost
- Evaluation Metrics for Classification
- Clustering
- Introduction
- K-Means Clustering
- Finding the Optimal Number of Clusters
- Optimizing the Inertia
- Cluster Instability
- Elbow Method
- Hierarchical Clustering
- Agglomerative Clustering
- DBSCAN Clustering
- Association Rules
- Market Basket Analysis
- Apriori Algorithm
- Recommendation Engines
- Collaborative Filtering
- User-Based Collaborative Filtering
- Item-Based Collaborative Filtering
- Recommendation Systems
- Time Series & Forecasting
- What is Time Series Data?
- Components of Time Series Data
- Stationarity of Time Series Data
- ACF (Auto Correlation Function)
- PACF (Partial Auto Correlation Function)
- Time Series Models
- AR (Auto Regression)
- ARMA
- ARIMA
- SARIMAX
- Model Selection & Evaluation
- Overfitting & Underfitting
- Bias-Variance Tradeoff
- Hyperparameter Tuning
- Joblib and Pickling
- Others
- Dummy Variables
- One-Hot Encoding
- GridSearchCV vs RandomizedSearchCV
- Machine Learning Pipeline
- Machine Learning Model Deployment using Flask
- Installation of Python
- Variables
- Input
- Output
- Data Types
- Data Structures
- Operators
- Condition Statements
- Loops
- Functions
- oops
- Advance Module
- Advance Functions
- File Handling
- Errors
- Exception Handling
- Introduction
- Power BI for Data Scientist
- Types of Reports
- Data Source Types
- Installation
- Basic Report Design
- Data Sources and Visual Types
- Canvas and Fields
- Table and Tree Map
- Format Button and Data Labels
- Legend, Category and Grid
- CSV and PDF Exports
- Visual Sync, Grouping
- Slicer Visual
- Orientation and Selection Process
- Slicer: Number, Text, Slicer List
- Bin Count and Binning
- Hierarchies, Filters
- Creating Hierarchies
- Drill Down Options
- Expand and Show
- Visual Filter, Page Filter, Report Filter
- Drill Thru Reports
- Power Query
- Power Query Transformations
- Table and Column Transformations
- Text and Time Transformations
- Power Query Functions
- Merge and Append Transformations
- DAX Functions
- DAX Architecture and Entity Sets
- DAX Data Types and Syntax Rules
- DAX Measures and Calculations
- Creating Measures
- Creating Calculated Columns
- Deep Learning at a Glance
- Introduction to Neural Networks
- Biological and Artificial Neuron
- Introduction to Perceptron
- Perceptron, Learning Rule, and Drawbacks
- Multilayer Perceptron (MLP)
- Loss Function
- Neural Network Activation Functions
- Training MLP: Backpropagation
- Cost Function
- Gradient Descent & Backpropagation
- Vanishing Gradient Problem
- Exploding Gradient Problem
- Introduction to PyTorch
- Regularization
- Optimizers
- Hyperparameters and Hyperparameter Tuning
- TensorFlow Framework
- Introduction to TensorFlow
- TensorFlow Basic Syntax
- TensorFlow Graphs
- Variables and Placeholders
- TensorFlow Playground
- ANN (Artificial Neural Network)
- ANN Architecture
- Forward Propagation
- Backward Propagation
- Epoch Concept
- Introduction to TensorFlow & Keras
- Vanishing Gradient Descent
- Fine-Tuning Neural Network Hyperparameters
- Number of Hidden Layers
- Number of Neurons per Hidden Layer
- Activation Functions
- Installation of YOLOv8
- Installation of Keras
- Installation of Theano
- PyTorch Library
- RNN (Recurrent Neural Network)
- Introduction to RNN
- Backpropagation Through Time (BPTT)
- Input and Output Sequences
- RNN vs ANN
- LSTM (Long Short-Term Memory)
- GRU (Gated Recurrent Unit)
- Bidirectional RNN
- Sequence-to-Sequence Architecture (Encoder-Decoder)
- BERT Transformers
- Text Generation using Deep Learning
- Text Classification using Deep Learning
- Generative AI (ChatGPT)
- Basics of Image Processing
- Histogram of Images
- Basic Image Filters
- Convolutional Neural Networks (CNN)
- Introduction to CNN
- ImageNet Dataset
- Project: Image Classification
- Different Types of CNN Architectures
- Recurrent Neural Network (RNN) Overview
- Transfer Learning using Pre-trained Models
- Natural Language Processing (NLP)
- Text Cleaning
- Texts and Tokens
- Basic Text Classification using Bag of Words
- Document Vectorization
- Bag of Words (BoW)
- TF-IDF Vectorizer
- N-Gram Models
- Unigram
- Bigram
- Word Vectorizer Basics
- One-Hot Encoding
- Count Vectorizer
- Word Cloud and Gensim
- Word2Vec
- GloVe
- Text Classification using Word2Vec and GloVe
- Parts of Speech (POS) Tagging
- Topic Modeling using LDA (Latent Dirichlet Allocation)
- Sentiment Analysis
- Twitter Sentiment Analysis using TextBlob
- Introduction to TextBlob
- Installing TextBlob Library
- Simple TextBlob Sentiment Analysis Example
- Using NLTK's Twitter Corpus
- spaCy Library
- Introduction to spaCy
- What is a Token?
- Tokenization
- Stop Words in spaCy
- Stemming
- Lemmatization
- Lemmatization through NLTK
- Lemmatization using spaCy
- Word Frequency Analysis
- Counter
- Part of Speech (POS)
- Part of Speech Tagging
- POS Tagging using spaCy and NLTK
- Dependency Parsing
- Named Entity Recognition (NER)
- NER with NLTK
- NER with spaCy
- Human Vision vs Computer Vision
- CNN Architecture
- Convolution Layer
- Max Pooling Layer
- Flatten Layer
- Fully Connected Layer
- Striding and Padding
- Max Pooling
- Data Augmentation
- Introduction to OpenCV
- Introduction to YOLOv3 Algorithm
- Image Processing with OpenCV
- Image Basics with OpenCV
- Opening Image Files with OpenCV
- Drawing on Images using OpenCV
- Image Manipulation with OpenCV
- Face Detection with OpenCV
- Video Processing with OpenCV
- Introduction to Video Basics
- Object Detection
- Object Detection with OpenCV
- Reinforcement Learning
- Introduction to Reinforcement Learning
- Architecture of Reinforcement Learning
- Reinforcement Learning with OpenAI
- Policy Gradient Theory
- OpenAI & Generative AI
- Introduction to OpenAI
- Generative AI
- ChatGPT (3.5)
- Large Language Models (LLMs)
- Classification Tasks with Generative AI
- Content Generation using Generative AI
- Text Summarization with Generative AI
- Information Retrieval and Synthesis Workflow with Generative AI
- Time Series and Forecasting
- Time Series Forecasting using Deep Learning
- Seasonal-Trend Decomposition using LOESS (STL)
- Bayesian Time Series Analysis
- MakerSuite Google
- PaLM API
- MUM Models
- Azure Machine Learning (Azure ML)
