Data Science & Machine Learning using Python & R
Your path to a career in data science and machine learning.
Course Overview
This programme in data science will help you in learning and analyzing the structure of data with the help of Python and R programming. This certification will cover the Data Investigation, Machine Learning, Information Visualization, Web Scraping, NLP prescient analytics, analytics procedures cluster investigation, and estimation with the help of Python and R language. By the time of course completion you will ace the basic Information Science devices utilizing Python & R languages and their application in various real world situation.
Netkom Learning Effect: We are industry practitioners, technologists and academics who advocate the efficacy of outcome-based and evidence-informed learning through experiential –Project based social experiences. Become a practitioner from day one as you embark on your learning journey through a live project which you will mature with the help of a team member (learning collaboration skills and teamwork) during the later stages. Upon deployment, you will have not just a certificate of completion from us but a live project on your CV and on LinkedIn to show your prospective employers.
Learning Outcomes
- You will be able to develop relevant programming abilities.
- You will be able to demonstrate proficiency with statistical analysis of data.
- You will be able to develop the ability to build and assess data-based models.
- You will be able to execute statistical analyses with professional statistical software.
- You will be able to demonstrate skill in data management.
- You will be able to apply data science concepts and methods to solve problems in real-world contexts and will communicate these solutions effectively
Course Outline
- Basics of Data Science Flow
- Anaconda Installation
- Intro to Jupyter Notebook
- Intro to Python
- Python Objects & Data Structure
- Subsetting (Strings, Lists, Dictionaries)
- Python Comparison Operators
- Python Statements
- Methods & Functions
- Importing Data in Python
- NumPy & Pandas Basics in Python
- Subsetting Data frames in Pandas
- Data Aggregation using Group By, Pivot and Melt
- What is Probability?
- Conditional Probability (Disjoint Events + General Addition Rule).
- Dependence vs Independence
- Probability Trees & Bayesian Inference with their examples.
- Machine Learning Basics
- Machine Learning Playground
- Supervised Learning.
- Train Test Splitting
- Overfitting vs Underfitting
- Cross Validation using K-folds
- Linear Regression
- Gradient Descent, Ordinary Least Squares
- Decision Trees.
- Information Gain, Gini Index, Chi Square
- Random Forest.
- Grid Search CV of Random Forest Hyper-parameters
- What is Boosting
- What is Bagging
- AdaBoost on Python
- Multi-Classification and Analyzing its Confusion Matrix
- Unsupervised Learning
- Clustering
- K-Means algorithm
- Elbow Analysis, Internal Indexes, Silhouette Score
- Visualizing Association Rules
- Network Graph Theory
- Social Network Analysis by Network Graph
- Dimensionality Reduction Concept
- Principal Component Analysis (PCA)
- Principal Vectors/Components
- Composite Features
- Types of Variables
- Data Visualizations (Scatter plot, Histogram, Bar plots, Line plots, Heat maps)
- Data Centricity (Mean, Modes, Median, STD, Variance, Interquantile Range).
- Box plot
- Data Transformation (Log, Natural Log, Min Max )
- Data Cleaning in Python
- Visualization on Matplotlib
- Visualization on Seaborn
- Exploratory Data Analysis of Titanic dataset
- Feature Engineering
- Techniques of Filling Missing values in EDA
- Correlation Matrix
- Hands-on Assignment of EDA
- Interactive Discussions on Project
- Multivariate Regression
- Residual Plots, R square, Adjusted R Square
- Polynomial Regression
- Model Complexity, Model Selection
- Lasso Regularization
- Ridge Regularization
- Classifications
- Logistic Regression.
- Confusion Matrix.
- True Positive, True Negative, False Positive , False Negative.
- Precision, Accuracy, Recall, F Measure.
- ROC Curve, AUC, TPR, FPR
- Project-2 Assigned to Students
- External Indexes, Adjusted Rand Index
- Cluster Profiling using Radar Chart
- Feature Scaling
- DBSCAN Algorithm
- Cluster Validation using DBCV
- Hierarchical clustering
- Average vs Complete vs Ward linkage
- Dendrogram Creation and Reading clusters
- External Indexes, Adjusted Rand Index
- Hierarchical clustering Use Cases
- Association Rules
- Apriori Algorithm
- Support, Confidence, Lift, Leverage, Conviction
- Maximal Variance
- Info Loss and Principal Component Analysis
- Image Classification using PCA
- Model Deployment Basics
- Flask App Introduction
- Model Deployment on Flask App
- Data Science Test
- Project & Presentation
- Self-learning Path Guidance
Our Approach for ZOOM Interactive Classes
Schedule
Starting
To be Announced
Duration & Time
To be Announced
Language
Urdu/English
Remaining Seats
Limited Seats Available
Dr Kamran Shafi
Training: Data Science, Machine Learning and Artificial Intelligence
Dr Kamran is a Co-Founder of Netkom Learning has almost 15 years of experience working in the field of data and decision sciences in different roles and industries, a PhD in machine learning and a strategic thinking mind and training, He can take the lead to building an organization-wide and strategic-level differentiating advanced analytics capability, or contribute to evolving an existing analytics capability to next level of maturity.
Training Options
Live Online Training
- Hands-on, live instructor-led training in a virtual class environment by top instructors
- Real-time remote access to class software & virtual lab environment
- Seamless instructor-peer interaction in a virtual classroom setting
Physical Training
- Strategically designed sessions following a practical training approach in a collaborative learning & critical thinking atmosphere.
- Offering an intensive and interactive learning experience followed by Q&A sessions, real-time examples.
Facebook Private Group Training
- Most specifically designed for students and unemployed persons
- Full support by mentor with live comments
- Flexible timings
- Live and short sessions. Fully interactive learning process.
- 5 sessions a week. Each session is 30-45 minutes.
- Cancel subscription anytime.
Online Training
- Customized training modules
- Direct mentor support
- Schedule a training with instructor on your own choice
- Intensive and interactive learning experience followed real-time examples, Q/A sessions and doubt clearance opportunities.
- Personalized direct support for preparation in exams.
- High success ratio
FAQ
Anyone can join this course. There is no pre requisite and eligibility.
Duration: 4 weeks (Saturdays & Sundays)
Timings: 11AM – 4PM
Since course aredriven by best industry specialists so it is made beyond any doubt that content secured in course is outlined with hand on viable of more than 90% together with supporting theory.
Yes, you will be granted with a course completion certificate by Netkom Learning. We moreover definitely conduct an yearly conference for the appreciation and acknowledgment of our students.
Since our instructors are industry specialists so they do prepare the students approximately viable world additionally suggest shinning understudies in industry for relevant positions.
Certification Training course incorporates different real-time, industry-based ventures, which is ablesharpen your aptitudes as per current industry guidelines and get ready you for the long run career needs.