Artificial Intelligence and Data Science Specialisation

Introduction to Python Programming

• Introduction to the Jupyter Notebook environment. Basics Jupyter notebook Commands.
• Syntax, variables, and Data types.
• Data structures in python – List, Tuple, Set, Dictionary
• Conditional statements – if, Else, Elif.
• Loops – While & For Loops.
• Functions – lambda, recursions, map, filter & reduce functions.
• User-Defined Functions
• Errors and Exceptions- Try and Except
• List and Dictionary Comprehension

SQL

• Introduction to DBMS & RDBMS
• OLAP vs OLTP
• Database Design
• Database creation in MYSQL Workbench
• Single Table Queries – SELECT, WHERE, ORDER BY, Distinct, And, OR.
• Aggregation functions- Group by, max, min, sum,avg, etc.
• Multiple Table Queries: INNER, SELF, CROSS, and OUTER, Join, Left Join, Right Join, Full Join, Union, Union All.
• Subquery
• Analytics Function- Partition by, rank, dense rank, row number, lag, lead, etc.

Data analysis using python

• Pandas
• Numpy
• Data Cleaning – null and infinite values, outliers, capping, sanity
  checks, data formatting, etc.
• Types of Variables – Categorical & Continuous
• Visualization Libraries: Seaborn and Matplotlib
• Exploratory Data Analysis: Univariate, Bivariate, and Multivariate analysis.

Statistics

• Introduction to Statistics
• Basics of Probability
• Discrete Probability Distribution
• Continuous Probability Distribution
• Normal Distribution
• Poisson’s Distribution
• Bayes’ Theorem
• Central Limit Theorem
• Pearson Co-Relation, Co-Variance
• Creating confidence interval for the population parameter
• Characteristics of Z-distribution and T Distribution.
• Concepts of Hypothesis Testing: Null and Alternate Hypothesis
• Making a Decision and Critical Value Method
• p-Value Method and Types of Errors
• Two-Sample T-Test
• Two sample Z-test
• ANOVA, Chi-Square, A/B Testing

Supervised Learning – Regression

• Simple Linear Regression
• Multiple Linear Regression
• Ridge & Lasso Regression (L1 & L2)

Supervised Learning – Classification

• Logistic Regression
• KNN Algorithm
• Naive Bayes
• Decision Trees
• Random Forest
• Support Vector Machines

Supervised Learning – Ensemble

• Bagging and Boosting
• Xgboost Algorithm
• Adaboost Algorithm
• VotingClassifier
• Gradient Boosting Algorithm

Unsupervised Learning

• Introduction to Clustering
• K-Means Clustering: Linkage, Use of Elbow Curve & Silhouette
  Score
• Hierarchical Clustering
• Principal Component Analysis (PCA

• Introduction to Deep Learning and Neural Networks
• Introduction to Linear Algebra
• TensorFlow & Keras for Neural Networks
• Artificial neural Networks (ANN)
• Image Classification using Convolutional
• Neural Networks (CNN)
• Transfer Learning (LeNet 5, Alex Net, VGG 19 & 16, ResNet, Inception V3)
• Recurrent neural networks – Natural Language processing
• LSTMs – Long short-term memory
• GRUs – Gated Recurrent Unit
• LSTMs Applications- Language Modeling

MLOPS/Model Deployment

Machine Learning model deployment with Industry level architecture

• Data Transformation
• Data Analysis Expressions (DAX)
• Data Visualisation
• Connectivity modes
• Power BI report server

Learn how to take data from different sources (Extract), change it to the format you need (Transform), and put it into a database (Load).

Extract: Learn how to gather data from different sources like databases, spreadsheets, or online services.

Transform: Discover how to clean, format, and modify the data to fit your needs.

Load: Learn how to store the transformed data into a database or another storage system, making it ready for analysis and use.

• Industry relevant projects
• Mentorship by industry experts

• Resume building
• Mock interviews