Applied Data Science & AI Program

Last Update May 21, 2025

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37 Lessons300h

439 Students

Applied Data Science & AI Program

Curriculum

Python

Introduction to Python, Why Python, Variables, Data Types, Type castings, Strings, Indexing00:00

Operators and Conditional Statements, Looping Statements and it’s Control Statement00:00

Lambda Functions, *args, **kwargs, Functions00:00

Data Structures – List, Tuple and List Comprehensions00:00

Data Structures – Set and Dictionaries00:00

Classes, Objects and Constructors, Inheritance00:00

Polymorphism, Abstraction and Encapsulation00:00

Connecting & Establishing connections to databases, Executing SQL Queries, ORM, Working with NoSQL Databases00:00

Introduction of Numpy, and Pandas00:00

Introduction of Seaborn and Matplotlib00:00

Statistics

Introduction to Statistics, Descriptive Statistics, Sample, Population, Major of Central Tendency, Standard Deviation00:00

Variance, Range, IQR, Outliers, Correlation, Covariance, Skewness, Kurtosis, Probability00:00

Probability, Probability distributions, Central Limit Theorem, Binomial and Poisson Distribution00:00

Normal Distribution, Type I & Type II Error00:00

T-test, Z-test, Hypothesis, Testing Interview Questions00:00

Machine Learning

Introduction to ML, Types of variables, Encoding, Normalization, Standardization, Types of ML, Linear Regression00:00

Linear Regression, Logistic Regression, SVM, KNN, Naïve Bayes, Decision Tree, Random Forest00:00

Mean Absolute Error, Mean and Root Mean Square Error, Confusion Matrix, R2 Score, Adjusted R2 Score,F1 Score00:00

Classification Report, AUC ROC, Accuracy, Ensemble Techniques, Random Forest, Xgboost00:00

Unsupervised Machine Learning, PCA, Clustering, k-Means Clustering and Hierarchical clustering00:00

Project Session 01

Project Session: Data Analysis & Model Building with Machine Learning00:00

Deep Learning

Introduction to Neural Network, Foreward Propagation, Activation Function00:00

Activation Function(Linear, Sigmoid, Relu, Leaky Relu), Optimizers, Gradient Descent, Stochastics Gradient Descent00:00

Mini batch Gradient Descent, Adagrad, Padding, Pooling, Convolution, Checkpoints and Neural Networks Implementation00:00

Introduction to Time Series Analysis, Various components of the TSA, Decomposition Method (Additive Method and Multiplicative)00:00

ARMA and ARIMA00:00

Computer Vision

Introduction to Image Processing, Feature Detection, OpenCV00:00

Convolution, Padding, Pooling & its Mechanisms00:00

Forward Propagation & Backward Propagation for CNN00:00

CNN Architectures like AlexNet, VGGNet, InseptionNet, ResNet, Transfer Learning00:00

Natural Language Processing

Introduction to Text Mining, Text Processing using Python and Introduction to NLTK00:00

Sentiment Analysis, Topic Modeling (LDA) and Name- Entity Recognition00:00

BERT, Text Segmentation, Text Mining, Text Classification00:00

Automatic Speech Recognition, Introduction to Web Scraping00:00

Reinforcement Learning

RL Framework, Component of RL Framework, Exampes of Systems00:00

Types of RL Systems, Q-Learning00:00

Project Session 02

Image & Text Analysis, with Intelligent Decision-Making capabilities using Advanced Deep Learning & NLP Techniques00:00

Your Instructors

skillzrevo

Data Science & AI Batch Jan SkillzRevo

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