Pinnacle | AIML
The Pinnacle AIML course by CodeGrads is an 11-month, instructor-led online Certification PG program in collaboration with National American University (NAU), USA. It equips learners with industry-relevant skills in Python programming, data analytics, machine learning, deep learning, NLP, and Generative AI. Designed for working professionals and beginners .
FREE
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Course Overview
Schedule of Classes
Course Curriculum
3 Subjects
Python
12 Exercises • 19 Learning Materials
Module 1
Data Structure
Data Types
Language Syntax
Module 2
Lambda functions
Notable Built-In Functions in Python
User Defined Functions
Comments Python Keywords and Identifiers
Lambda functions
Assignment P1, P2, P3 and P4
P1
P2
P3
P4
Module 3
For and While Loops
If Else Block
List Comprehensions
Module 4
Error Handling
OOP Concepts
OS Library
Python Packages
Assignment P5, P6, P7 and P8
P5
P6
P7
P8
Module 5
Threading and Parallel Processing
Understanding Date and Time
Virtual Environment
Assignment P9 and P10
P9
P10
Module Assessment 1
Module Assessment 1
Module Assessment 1
Python Modules Examples
Python Examples
P1
P2
P3
P4
P5
P6
P7
P8
P9
Module Assessment 1
Data Analytics
9 Exercises • 32 Learning Materials
Module 1
Pandas DataFrame and Series
Data type and Conversions
Module 2
Indexing and Data Selection
Pandas DataFrame Query
Assignment D1 and D2
D1
D2
Module 3
Numpy
Data Exploration and Summary
Handling Missing Values
Module 4
Duplicates and Outliers
String Manipulation
Type Conversion and Dates
Assignment D3, D4 and D5
D3
D4
D5
Module 5
Grouping DataFrames
Merging and Concatenating Data
Pivot and Aggregation
Module 6
Random Sampling Datapoints
Seaborn Graphs
Matplotlib
D1
D2
D3
D4
D5
D6
D7
D8
Module Assignment 2
Assignment D6, D7 And D8
D6
D7
D8
Module Assessment 2
Module Assessment 2
Data Analytics - Examples
Pandas DataFrame and Series
Data type and Conversions
Indexing and Data Selection
Pandas DataFrame Query
Numpy
Data Exploration and Summary
Handling Missing Values
Duplicates and Outliers
String Manipulation
Type Conversion and Dates
Grouping DataFrames
Merging and Concatenating Data
Pivot and Aggregation
Random Sampling Datapoints
Seaborn Graphs
Matplotlib
Machine Learning
27 Exercises • 31 Learning Materials
Module 1
Machine Learning
Types of Machine Learning
app
Machine Learning Pipeline
ML vs Traditional Programming
Module 2
Feature Engineering
Feature Scaling - Normalization and Standardization
Encoding Categorical Variables - Label and One-Hot
Handling Missing Data
Outlier Detection and Treatment
Train-Test Split and Cross-Validation
Assignment M1, M2, M3, M4, M5 and M6
M1
M2
M3
M4
M5
M6
Module 3
Linear Regression
Polynomial Regression
Ridge - Lasso and ElasticNet
Evaluation Metrics - MAE - MSE - RMSE - R2
Logistic Regression
K-Nearest Neighbors (KNN)
Decision Trees
Random Forest
Support Vector Machines (SVM)
Naive Bayes
XGBoost and LightGBM
Assignments M7, M8, M9, M10, M11, M12, M13, M14, M15, M16, M17 and M18
M7
M8
M9
M10
M11
M12
M13
M14
M15
M16
M17
M18
Module 4
K-Means Clustering
Hierarchical Clustering
DBSCAN
PCA
t-SNE and UMAP (Visualization)
Assignments M19, M20, M21, M22, M23
M19
M20
M21
M22
M23
Module 5
Bias-Variance Trade-off
Hyperparameter Tuning - GridSearchCV and RandomizedSearchCV
Cross-Validation Techniques - K-Fold and Stratified K-Fold
Feature Selection Techniques
Assignments M24, M25, M26, M27
M24
M25
M26
M27
Course Instructor
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CodeGrads Tech LLP
5 Courses • 6 Students
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