Python is the foundation upon which modern data science is built. This course provides a solid foundation in Python programming, covering:

1. Introduction
   1. Programming language introduction
   2. Translators (Compiler, Interpreter)
   3. Uses of computer programs
   4. Algorithm
   5. Flow chart
2. Python Introduction
   1. History
   2. Why Python was created
   3. Fields of use
   4. Use of Python in Cybersecurity
   5. Reasons for using Python
   6. Syntax
   7. Installation of IDE
3. Variables
   1. History
   2. What is variable
   3. Declaration rules
   4. Multiple variable declarations
   5. Valid and invalid variables
   6. Type casting
4. Data Type
   1. Introduction
   2. Discuss all data types
   3. Use type() to show dynamically typed language
   4. String
   5. List
   6. List: List Comprehension
   7. Tuple
   8. Dictionary
   9. Set
5. Operators
   1. Introduction
   2. Arithmetic operators
   3. Assignment operators
   4. Comparison operators
   5. Logical operators
   6. Identity operator
   7. Bitwise operator
   8. Membership operator
6. Control Flow
   1. Introduction to Conditional Statement
   2. Conditional Statement: if
   3. Conditional Statement: elif
   4. Conditional Statement: else
   5. Conditional Statement: Nested if
   6. Introduction to Looping
   7. Looping: for loop
   8. Looping: While loop
   9. Looping: Nested loop
7. Function
   1. Introduction function
   2. Declaration, calling of function
   3. Lambda function
   4. Filter
   5. Reduce function
   6. Map function
8. File Handling
   1. Introduction
   2. Text file handling
   3. Binary file handling
9. Object Oriented Programming
   1. Introduction
   2. Difference b/w procedural programming and OOPS
   3. Class
   4. Object
   5. Encapsulation
   6. Inheritance
   7. Abstraction
   8. Polymorphism
10. Web Scrapping
    1. Introduction
    2. Introduce basic HTML tags
    3. Introduction to Requests Library
    4. Introduction to bs4
    5. Scrapping through Beautiful Soup
11. Numpy
    1. Creating NumPy arrays
    2. Properties of Array
    3. Indexing and Slicing
    4. Aggregate Functions
    5. Numpy Functions
    6. Vectorization
    7. Broadcasting
    8. Boolean indexing
12. Pandas
    1. Series
    2. Data Frame
    3. Data Frame Properties
    4. Data Frame indexing and slicing
    5. Reading data from various sources
    6. Dataframe Functions
    7. Pandas Functions
    8. Filter Data
13. Visualization
    1. Introduction to Matplolib and Seaborn
    2. Properties of plots
    3. Line plot
    4. Histogram / Distplot
    5. Bar plot/ Count Plot
    6. Pie Chart
    7. Heat Map
    8. Scatter Plot
    9. Box Plot

Artificial intelligence (AI) is causing a revolution in a variety of sectors all over the world, including the healthcare and financial sectors. This course will provide you with an introduction to the field of artificial intelligence and the various applications of this field. The following subjects are discussed:

1. Artificial Neural Network and Regularization
   1. Single layered ANN
   2. Multiple Layered ANN
   3. Vanishing Gradient problem
   4. Dropout
2. Introduction to Deep Learning
   1. Difference between ML, DL, and AI
   2. Activation functions
   3. Gradient Descent
3. Computer Vision & OpenCV
   1. What is Computer Vision
   2. History of Computer Vision
   3. Tools & Technology used in Computer Vision
   4. Application of Computer Vision
   5. What is OpenCV
   6. Installation of OpenCV
   7. The first program with OpenCV
   8. Reading & Writing Images
   9. Capture Videos from Camera
   10. Reading & Saving Videos
4. Image Classification
   1. Haar Cascade Classifier
   2. Image Classification with CNN
5. Object Detection
   1. What is Object Detection
   2. Object Detection using Haar Cascade
6. Introduction to NLP
   1. What is Natural Language Processing
   2. Uses of NLP
   3. Application of NLP
   4. Components of NLP
   5. Stages of NLP
   6. Chatbot
7. Text Preprocessing
   1. Tokenization
   2. Non-Alphabets Removal
   3. Bag of Words
   4. Stemming & Lemmatization
8. Sentiment Analysis
   1. What is Sentiment Analysis
   2. Challenges in Sentiment Analysis
   3. Handling Emotions
   4. Sentiment Analysis with ANN
9. Sequence Model
   1. Sequential Data
   2. Recurrent Neural Network
   3. Architecture of RNN
   4. Vanishing Gradient Problem in RNN
   5. Long Short-Term Memory

Machine learning is the driving force behind artificial intelligence, and it is causing a shift in the way that businesses analyze and respond to data. This lesson will walk you through the fundamentals of machine learning as well as the algorithms that underpin it, including the following:

1. Welcome to the ML experience
   1. Importance of ML in your career
   2. AI FAMILY TREE
   3. System requirements
   4. Prerequisites
2. Machine learning basics
   1. What is machine learning
   2. Classification and regression
   3. Supervised and Unsupervised
   4. Preparing for your ML journey
3. EDA and Preprocessing
   1. Reading/Writing Excel, CSV, and Other File Formats
   2. Basic EDA (Info, Shape, Describe)
   3. Handling Missing Values
   4. Handling Outliers
   5. Handling Skewness
   6. Encoding Categorical Data (One-Hot, Label Encoding)
   7. Data Normalization and Scaling (MinMax, Standard Scaler)
   8. Feature Engineering
   9. Correlation Analysis and Heatmaps
   10. Train-Test Split & Cross-validation Strategy
4. Introduction to Regression
   1. Simple Linear Regression
   2. Multiple Linear Regression
   3. Lost and Cost Function (Mean Squared Error)
   4. Regression Evaluation Metrics
   5. Assumptions of Linear Regression
   6. Polynomial Regression
5. Regularization
   1. Overfitting vs Underfitting
   2. Bias Variance trade-off
   3. Ridge and Lasso Regularization
   4. Cross Validation
6. Introduction to Classification
   1. Introduction to Logistic Regression
   2. Model Evaluation: Accuracy, Precision & Recall
   3. Model Evaluation: F1 Score, Confusion Matrix
   4. SVM
   5. Decision Tree
7. Ensemble Learning
   1. What is Ensemble Learning
   2. Bagging
   3. Random Forest
   4. Introduction to Boosting
   5. Boosting: Adaboost
   6. Boosting: Gradient Boost
   7. Boosting: XG Boost
8. Introduction to Hyperparameter Tuning
   1. Hyperparameter Tuning: GridsearchCV
   2. Hyperparameter Tuning: RandomizedSearchCV
   3. Model Selection Guide
   4. Selecting the Right Evaluation
9. Unsupervised ML
   1. Introduction to Clustering
   2. K-Means Clustering
   3. Principal Component Analysis