Course Content
Introduction to AI and Machine Learning
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Overview of AI and ML – History and evolution of AI
00:00
Differences between AI, ML, and Data Science
00:00
Types of Machine Learning : Supervised Learning
00:00
Unsupervised Learning
00:00
Reinforcement Learning
00:00
Semi-supervised Learning
00:00
Applications of AI/ML
00:00
Mathematics for Machine Learning
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Linear Algebra : Vectors, Matrices, and Tensors
00:00
Eigenvalues and Eigenvectors
00:00
Singular Value Decomposition
00:00
Calculus : Differentiation and Integration
00:00
Partial Derivatives and Gradient Descent
00:00
Probability and Statistics : Probability Theory
00:00
Distributions (Normal, Binomial, etc.)
00:00
Bayes’ Theorem
00:00
Optimization Techniques : Cost Function
00:00
Optimization Algorithms
00:00
Programming for AI/ML
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Introduction to Python : Python basics and libraries
00:00
Data Handling : Numpy, Pandas, Matplotlib
00:00
Data Preprocessing and Cleaning
00:00
Introduction to Jupyter Notebooks
00:00
Supervised Learning
0/9
Linear Regression : Simple and Multiple Linear Regression
00:00
Evaluation Metrics
00:00
Classification Algorithms : Logistic Regression
00:00
Decision Trees
00:00
Support Vector Machines (SVM)
00:00
k-Nearest Neighbors (k-NN)
00:00
Model Evaluation : Confusion Matrix
00:00
Precision, Recall, F1-Score
00:00
ROC-AUC Curve
00:00
Unsupervised Learning
0/6
Clustering : k-Means Clustering
00:00
Hierarchical Clustering
00:00
DBSCAN
00:00
Dimensionality Reduction : Principal Component Analysis (PCA)
00:00
t-SNE
00:00
LDA
00:00
Neural Networks and Deep Learning
0/7
Introduction to Neural Networks : Perceptron Model
00:00
Activation Functions
00:00
Deep Learning : Multi-Layer Perceptrons (MLP)
00:00
Backpropagation
00:00
Optimization Techniques (Adam, RMSProp)
00:00
Convolutional Neural : Networks (CNNs) CNN Architecture
00:00
Applications in Image Processing
00:00
Reinforcement Learning
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Introduction to Reinforcement Learning : Markov Decision Processes (MDPs)
00:00
Q-Learning and Deep Q-Networks (DQN)
00:00
Applications in Gaming and Robotics
00:00
AI/ML Model Deployment
0/3
Model Deployment Techniques : Flask, Django, FastAPI
00:00
Cloud Services : AWS, Google Cloud, Microsoft Azure
00:00
Monitoring and Scaling ML Models
00:00
AI/ML
About Lesson

Data preprocessing and cleaning are crucial steps in data handling that ensure the quality and usability of data for analysis. This process involves several key tasks. First, data preprocessing aims to convert raw data into a format that is suitable for analysis, which often involves handling missing values, correcting errors, and transforming data into a standardized format. Cleaning, on the other hand, focuses on identifying and addressing inconsistencies and inaccuracies within the dataset, such as duplicate entries, incorrect values, or irrelevant information.

During preprocessing, data may be normalized or scaled to bring it into a consistent range, which can improve the performance of machine learning algorithms. Outliers, which are values that significantly differ from other observations, may also be handled to prevent them from skewing results. Additionally, data may be encoded or categorized to make it more suitable for analysis, especially when dealing with categorical variables.

Overall, effective data preprocessing and cleaning help to enhance the accuracy and reliability of analytical models and ensure that the data used in decision-making processes is both meaningful and actionable.

Data Preprocessing and Cleaning
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