Full Stack Data Science & AI: From Python Basics to Generative AI and Prompt Engineering-Day 2

Full Stack Data Science & AI: From Python Basics to Generative AI and Prompt Engineering – Live Training

Dive into the world of Artificial Intelligence, Data Science, and Machine Learning with this comprehensive, beginner-friendly course designed for career changers and job seekers.
Start with Python programming fundamentals, then explore essential data structures and libraries like NumPy and Pandas for data analysis. Learn the core concepts of statistics and mathematics that power machine learning algorithms, followed by hands-on training in Supervised and Unsupervised ML techniques.
Advance into Deep Learning fundamentals, understanding how neural networks work, and get an introduction to Generative AI—the technology behind AI-powered content generation. Learn the basics of Prompt Engineering, a critical skill for effectively interacting with AI models like ChatGPT.
By the end of this course, you’ll have a solid understanding of Python, Machine Learning, Deep Learning, and Generative AI, equipping you with practical skills for the evolving job market.
Perfect for beginners, career switchers, and professionals looking to build a strong foundation in AI and Data Science without getting lost in complex theories.

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Live Sessions Price:

🔥 Exclusive One-Time Offer – Offer price after discount is 290 USD 200 USD 179 USD Or 25000 INR 19000 INR 13900 Rupees.

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Free Day 2 Session:

27th March @ 7 AM – 8 AM (IST) (Indian Timings)

26th March @ 9:30 PM – 10:30 PM (EST) (U.S Timings)

27th March @ 1:30 AM– 2:30 AM (BST) (UK Timings)

Class Schedule:

For Participants in India: Monday to Friday @ 7 AM – 8:30 AM (IST)

For Participants in US: Sunday to Thusday @9:30 PM – 11 PM (EST)

For Participants in UK: Monday to Friday @ 1:30 AM– 3 AM (BST)

What student’s have to say about the Trainer:

What will I Learn by end of this course?:

• Python Programming: Master the basics of Python, including data types, loops, and functions.
• Data Structures: Understand and work with lists, dictionaries, tuples, sets, and strings.
• Data Manipulation: Use libraries like NumPy and Pandas for efficient data handling and analysis.
• Data Visualization: Create insightful visualizations using Matplotlib and Seaborn.
• Machine Learning Techniques: Implement algorithms like Linear Regression, Decision Trees, and Support Vector Machines.
• Deep Learning Basics: Build and train artificial neural networks (ANNs). Learn key concepts like activation functions, backpropagation, optimization techniques, and regularization to develop AI models for complex tasks.
• Introduction to Generative AI & Prompt Engineering: Understand how AI models like ChatGPT work, explore real-world applications of Generative AI, and learn the basics of crafting effective prompts to get accurate and useful AI-generated responses

Salient Features:

• 120 Hours of Live Training along with recorded videos
• Lifetime access to the recorded videos
• Course Completion Certificate

Who can enroll in this course?

• Beginners who want to start their journey in AI and ML.
• Students looking to enhance their programming and data science skills.
• Professionals aiming to transition into a career in AI and ML.
• Data Enthusiasts eager to learn about data manipulation and visualization.
• Anyone interested in understanding the basics and advanced concepts of AI and machine learning.

Course syllabus:

Module 1: Introduction to AI and ML

1. Definition and Scope of AI and ML
2. Introduction to programming
3. Starting with Google Collab

Module 2: Programming with Python – Basics

1. Why Python?
2. Data Types and Type Conversion
3. Variables
4. Operators
5. Conditional statements
6. While Loops
   • Infinite While Loops
   • Nested While Loops
   • Break keyword 
7. For Loops
8. Python Identifiers
9. Function 
   • Inbuilt vs User Defined
   • Types of Function Arguments
   • Global variable vs Local variable
   • Anonymous Function | LAMBDA
   • libraries

Module 3: Python-Data Structures and Functions

     3.1 List

1. List creation to store multiple data-type values
2. Retrieving an item from a list
3. Item Replacement
4. List comprehension
5. List mutable concept
6. Create nested list
7. Functions:
   • len(), append(), pop(), insert(), remove(), sort(), reverse()
8. Indexing
   • Forward indexing
   • Backward indexing
9. Slicing
   • Forward slicing
   • Backward slicing
   • Step slicing

    3.2 Dictionary 

1. Create a dictionary 
2. Creating a CSV file from python dictionary
3. Keys: Values concept
4. Important points about python dictionary
5. Retrieving Keys,Values
6. Retrieving Key-Value pair together
7. Update Dictionary
8. Checking whether an item exists in dictionary
9. Functions
   • len(), keys(), values(), items(), get(), pop(), update(),from_dict(),zip(),clear(),map()

     3.3 Tuple

1. Tuple creation
2. Finding length of Tuple
3. Slice a Tuple
4. Retrieve items using Tuple indexing
5. Tuple immutable concept
6. Concatenate Tuples
7. Unpacking
8. Functions
   • len(), count(), index(),sorted(),zip()
9. Indexing
   • Forward indexing
   • Backward indexing

     3.4 Set

1. Set creation
2. Functions
   • len(), add(), remove(), pop(), union(), intersection(), difference()

    3.5 String Operations 

1. Common data structure operations on a string
2. Create a multiline string
3. Creating Patterns in strings
4. Zero padding
5. F-strings
6. Replace a part of a string with another string item
7. Mathematical operations inside place holders
8. Performance of f-literal vs format()
9. Create a well-punctuated string using the escape character
10. Functions: strip(),split(),join(),capitalize(),lower(),title(),upper(),istitle(),replace()

Module 4: Numpy

1. Importance of Numpy in Data Science
2. Creating Numpy Arrays
   • From Lists and Tuples
   • Using arange(), linspace(), and logspace()
   • Using zeros(), ones(),full()
   • From Random Numbers: random(),rand(),randn(),randint() 
3. Array Attributes: shape, size, ndim, dtype, itemsize
4. Basics Array Manipulation, Mathematical Operations, Indexing & Slicing
5. Functions:
   • add(),subtract(),multiply(), divide(),type(),arange(),linspace(),log(),abs(),reshape(),ravel(), flatten()
6. Statistics using numpy array
   • numpy.mean(), numpy.median(), numpy.std(), numpy.sum(), numpy.min(),corrcoef(),cov()
7. Comparing performance of list and array
8. Diagonal of a Matrix
9. Trace of a Matrix
10. Identity Matrix
11. Multiplicative Inverse of a Matrix
12. Determinant of a Matrix
13. Adjoint Matrix
14. Parsing, Adding and Subtracting Matrices

Module 5: Pandas

1. Introduction
2. Series
   • Creating Panda Series, Empty Series Object, Create series from List/Array/Column from DataFrame, Index in Series, Accessing values in Series
   • Statistical Operations on Series
   • NaN Value
   • Keywords: Values, index, dtypes, size
   • Python List vs Numpy Array vs Pandas Series
   • Functions: head(), tail(), sum(), count(), nunique(),sort_values(),value_counts() 
3. DataFrame 
   • Introduction to DataFrames
   • Creating DataFrames from Lists ,Dictionaries, Arrays 
   • Creating DataFrames from CSV, Excel, and Other File Formats
   • Common Attributes: shape, size, dtypes, index, columns
   • Basic DataFrame Methods: head(), tail(), info(), describe()
   • Accessing and Modifying Data: loc, iloc
   • Adding and Dropping Columns and Rows
   • Renaming Columns and Index
   • Identifying Missing Data
   • Handling Missing Data: fillna(), dropna()
   • Replacing Values
   • Filtering Data
   • Sorting DataFrames
   • Applying Functions: apply(), map()
   • Grouping Data: groupby()
   • Merging and Joining DataFrames
   • Concatenating DataFrames
   • Setting and Resetting Index
   • MultiIndex (Hierarchical Indexing)
   • Mathematical and Statistical Operations
   • Aggregation Methods: sum(), mean(), count(), nunique(), etc.
   • Pivot Tables and Cross-tabulations
   • Transpose of a Dataframe
   • Inplace Parameter
   • The Ampersand (&) Logical Operator

Module 6: Matplotlib

1. Introduction to “pyplot”
2. Basic Plot Functions: plot(), show(), title(), xlabel(), ylabel()
3. Understanding Figures and Axes
4. Setting Limits and Tick Labels
5. Creating Multiple Plots in a Single Figure using subplots
6. Adding Legends to Plots
7. Different Types of Plots
   • Line Graphs
   • Bar Charts
   • Histograms
   • Scatter Plot
   • Pie Chart

Module 7: Seaborn

1. Creating Basic Plots with Seaborn
2. Setting Styles and Color Palettes
3. Plotting with Seaborn
   • Categorical Plots: barplot(),countplot(),boxplot() etc
   • Distribution Plots: histplot(),pairplot() etc
   • Relational Plots: scatterplot(),line plot()
   • Regression Plots: regplot()
   • Matrix Plots: heatmap()
4. Annotation
5. Using hue, size, and style for Multi-variable Plots
6. Comparison with Matplotlib

Module 8: Scipy

1. Linear Algebra (scipy.linalg)
2. Integration (scipy.integrate)
3. Statistics (scipy.stats)

Module 9: Exploratory Data Analysis(EDA) 

      9.1 Introduction to EDA

1. Definition and Importance
2. Steps in EDA
3. Tools (Pandas, Matplotlib, Seaborn, Plotly)

     9.2  Data Collection and Preparation

1. Loading Data (CSV, Excel, Databases)
2. Data Structure Understanding
3. Handling Missing Values
4. Data Cleaning (Duplicates, Outliers, Standardization)

     9.3  Data Profiling

1. Summary Statistics (Mean, Median, Mode, Std Dev, Variance)
2. Data Distribution 
3. Data Types and Conversion

      9.4  Univariate Analysis

1. Frequency Distribution
2. Visualizations (Histograms, Bar Plots, Box Plots, Violin Plots, KDE Plots)
3. Central Tendency and Dispersion (Range, IQR, Std Dev, Variance)

      9.5 Bivariate Analysis

1. Relationship Types (Numerical vs Numerical, Numerical vs Categorical, Categorical vs Categorical)
2. Visualizations (Scatter Plots, Pair Plots, Joint Plots, Heatmaps, Box Plots)
3. Correlation (Pearson, Spearman, Kendall Tau)
4. Covariance

      9.6 Multivariate Analysis

1. Techniques (PCA)
2. Visualizations (Pair Plot Matrix, Heatmaps, 3D Scatter Plots)

     9.7 Feature Engineering

1. Creating New Features (Polynomial, Interaction, Aggregation)
2. Encoding Categorical Variables (One-Hot, Label, Target)
3. Scaling and Normalization (Standardization, Min-Max Scaling, Robust Scaling)

Module 10: Mathematics

     10.1 Linear Algebra

1. Linear Equations
2. Matrices
3. Determinant
4. Eigen Value and Eigenvector
5. Euclidean Distance & Manhattan Distance

     10.2 Calculus

1. Derivatives (or Differentiation)
2. Partial Differentiation
3. Max & Min
4. Indices & Logarithm

Module 11: Statistics

     11.1 Descriptive Statistics

1. Measures of Central Tendency (Mean, Median, Mode)
2. Measures of Variability (Range, Variance, Standard Deviation)
3. Quartiles, Percentiles, Interquartile Range (IQR)
4. Frequency Distribution Tables
5. Histograms and Box Plots

     11.2  Probability Basics

1. Basic Probability Concepts
2. Sample Spaces, Events, and Probability Axioms
3. Conditional Probability
4. Bayes’ Theorem
5. Random Variables and Probability Distributions

     11.3 Statistical Distributions

1. Discrete Distributions (Binomial, Poisson)
2. Continuous Distributions (Normal, Exponential)
3. Central Limit Theorem and Sampling Distributions

     11.4  Hypothesis Testing

1. Introduction to Hypothesis Testing
2. Null and Alternative Hypotheses
3. Z-Tests and T-Tests

 Module 12: Machine Learning

12.1 Introduction to Machine Learning

1. Types of Machine learning: Supervised, Unsupervised, and Reinforcement Learning
2. Discussion on different packages used for ML
3. Working on Linear Regression: Understanding the regression technique
4. Related concepts: Splitting the dataset into training and validation
5. Case study based practical application of the technique on Python

12.2 Simple Linear Regression

1. Understanding about linear regression
2. Simple linear regression
3. Linear Regression Assumptions. 
4. Best Fit line
5. Cost Function
6. Loss Optimization, Least squares.
7. Gradient Descent Algorithm 
8. Evaluation Metrics for Regression 
   1. Mean Absolute Error (MAE)
   2. Mean Squared Error (MSE)
   3. Root Mean Squared Error (RMSE)
   4. Mean-Squared Log Error (MSLE)
   5. R²
   6. Adjusted R²
   7. Residual (Error) Analysis
   8. Homoscedasticity & Heteroscedasticity
   9. Multicollinearity
   10. Variance Inflation Factor Math
   11. Recursive Feature Elimination (RFE)
9. Multiple Linear Regression

12.3  Logistic Regression 

1. What is Logistic Regression?
2. Types of Logistic Regression.
3. Assumptions of Logistic Regression.
4. Why not Linear Regression for Classification?
5. The Logistic Model. (Sigmoid curve and activation function)
6. Interpretation of the co-efficients.
7. Decision Boundary.
8. Cost Function of Logistic Regression.
9. Gradient Descent in Logistic Regression.
10. Evaluating the Logistic Regression Model.

12.4 Support Vector Machine

1. Support Vector Machine
2. Introduction to Support Vector Machine
3. Mathematical Approach
4. Theory on hyperplane and kernels
5. Kernel function
6. Different kinds of kernels
7. Practical application on Python

12.5 Decision Tree

1. Decision Tree
2. Introduction to Decision tree
3. Significance of using Decision Tree
4. Different kinds of Decision Tree
5. Procedure and technique of Decision Tree
6. Practical application of Decision Tree on Python

12.6 Random Forest Classifier

1. Target and Feature Variable Separation
2. Fitting the Model
3. Evaluation Methods: Confusion Matrix,Precision and Recall, f1-score
4. Classification using random forest on Python 

12.7 Naïve Bayes

1. Naïve Bayes
2. Theory of classification
3. Concept of probability: prior and posterior
4. Bayes Theorem
5. Mathematical concepts
6. Limitation of Naïve Bayes
7. Practical application on Python 

12.8 K-Nearest Neighbours (Clustering)

1. K-Nearest Neighbours
2. Concept and theory
3. Distance functions: Euclidean, Hamming, Minkowski
4. Why should we use KNN?
5. Mathematical approach
6. Practical application on Python 

 12.9 Important Elements of Machine Learning

1. Multiclass Classification
2. One-vs-All
3. Overfitting and Underfitting
4. Error Measures
5. Principal Component Analysis (PCA)
6. Statistical Learning Approaches
7. Introduction to SKLEARN Framework

Module 13: Deep Learning & Introduction to Advanced Neural Network Architectures

13.1 Introduction to Neural Networks

1. Introduction to ANN
   1. Biological Neuron vs Artificial Neuron
   2. Structural ML vs Deep Learning 
2. Perceptron 
   1. Perceptron Model
   2. Activation function
3. Neural Network Structure 
   1. Multi Layer Perceptron and Deep Neural Network
4. NN Training process/ working details
   1. Data Preparation
   2. Weights Initialisation
   3. Feed Forward Architecture
   4. Forward Propagation
      1. Activation Function
      2. Loss Calculation
   5. Back Propagation Algorithm
      1. Chain Rule
   6. Cost Function
   7. Gradient Descent Algorithm
   8. Learning Rate
5. Different types of Activation functions
   1. Sigmoid
   2. Tanh
   3. ReLU and its variants
   4. Softmax Activation
6. Other NN Training Concepts
   1. Over fitting 
   2. Under fitting
   3. Regularization Techniques
   4. Optimization algorithms
      1. Stochastic Gradient Descent
      2. Adam Optimizer

13.2 Deep Learning Architectures

1. Convolutional Neural Networks (CNN)
   • Concept, striding, pooling, architecture, and implementation
2. Recurrent Neural Networks (RNNs)
   • Concept, architecture, and implementation
3. Long Short-Term Memory (LSTM)
   • Concept
   • Applications of RNN and LSTM

Module 14: Gen ai concepts overview and prompt engineering

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