Timmins Training Consulting Sdn. Bhd. Machine Learning through Python & R

Transcription

1 Timmins Training Consulting Sdn. Bhd. Machine Learning through Python & R

2 Timmins Training Consulting Our Training Profile About Us - was established in We are an HRDF registered training provider and our courses are HRDF claimable. We have done hundreds days of training sessions in many countries including Malaysia, Singapore, Indonesia, China, Taiwan, India Canada and USA. We have diverse clientele from telecommunications, information technology, finance, semi-conductor, manufacturing and oil & gas sectors. Why choose Timmins? We customize the content We provide experiential learning We work with subject matter experts Our training programmes include courses in: Artificial Intelligence & Data Semiconductor Software Development Telecommunications Web Development Professional Development Embedded Programming Sdn. Bhd.

3 Timmins Training Consulting Timmins clients include: Our clients come from Malaysia, Singapore, Indonesia, India, China, Canada and USA. Sdn. Bhd.

4 DESCRIPTION This course builds comprehensive foundation for Machine Learning using Python and R through its associated libraries. It is not only hands-on course but also helps in developing understanding of the underpinning statistical methods involved. It covers all aspects of building blocks of machine learning involving data extraction, choosing appropriate model, model fine tuning, and result validation using variety of cutting edge libraries available today. Specifically, the course will cover, Python/R basics, fundamentals of machine learning, data handling and preparation and ML technique application. PREREQUISITE There is no pre-requisite to participate in the course. Participants are expected to have the latest version of Anaconda, Python, GitHub and JuPyteR labs installed on their laptop. Prior knowledge of statistical concepts will be useful. A lot of enthusiasm and wonderment about what can be achieved through active participation will significantly enhance the learning from the program. LEARNING OUTCOME On completion of this program, participants will be able to: Navigate the interactive data science environment JuPyteR for Python. Import and Export data from variety of sources in Python and R ecosystem. Apply appropriate model using Python/R libraries. Architect data pipeline for machine learning products. Conceptualize and kick start machine learning projects. MODULES DAY 1 DAY 2 DAY 3 DAY 4 Introduction to Python Data Handling in Python Basic Descriptive Statistics Data Preparation for Analysis Regression Analysis in Python Logistic Regression Model Selection and Cross validation Distance Concepts Unsupervised Learning Introduction to R Data Handling in R Data Preparation for Analysis Regression Analysis in R Logistic Regression in R Showcase of Rattle Program in R Business Case Studies Live Case Application in Data Science

5 Machine Learning through Python & R - 4 days 01 Introduction to Python Anaconda and Jupiter notebook basics Basic commands in Python Data Types and Operations Python packages Introduction to libraries like NumPy, SciPy, Matplotlib, Pyspark and Pandas 04 Data Preparation for Analysis Exploratory Data Analysis Data Validation rules Data cleaning techniques Data Preparation for analysis 02 Data Handling in Python Data importing Working with datasets Manipulating the datasets Creating new variables Exporting the datasets into external files Data Merging 05 Regression Analysis in Python Correlation Simple Regression models R-Square Multiple regressions Multicollinearity Individual Variable Impact (VIF) 03 Basic Descriptive Statistics Measures of Central tendency Measures of dispersion Probability Binomial Distribution Normal Distribution Hypothesis Testing Correlation Data exploration / Cleaning / Preparation Variable Identification Missing value treatment Outlier treatment Feature Engineering 06 Logistic Regression Need of logistic Regression Logistic regression models Validation of logistic regression models Multicollinearity in logistic regression Individual Impact of variables Confusion Matrix

6 Machine Learning through Python & R - 4 days 07 Model Selection and Cross validation How to validate a model? What is a best model? Types of data Types of errors The problem of over fitting The problem of under fitting Bias Variance Trade-off Regularization Cross validation Boot strapping 10 Introduction to R Basic commands in R Data Types and Operations R packages Introduction to R libraries Chart Plottng in R 08 Distance Concepts Decision trees Classification KNN LDA/SVM 11 Data Handling in R Data importing Working with datasets Manipulating the datasets Creating new variables Exporting the datasets into external files Data Merging 09 Unsupervised Learning Clustering k means PCA Dimensionality Reduction 12 Data Preparation for Analysis Exploratory Data Analysis Data Validation rules Data cleaning techniques Data Preparation for analysis

7 Machine Learning through Python & R - 4 days 13 Regression Analysis in R Correlation Simple Regression models R-Square Multiple regressions Multicollinearity Individual Variable Impact (VIF) 16 Business Case Studies Discussion on the Scope and possibilities within R based Data Science A 30 Minute Case Study & Analysis - BFSI/ Telecom A 30 Minute Case Study & Analysis - CPG/ Retail 14 Logistic Regression in R Need of logistic Regression Logistic regression models Validation of logistic regression models Multicollinearity in logistic regression Individual Impact of variables Confusion Matrix 17 Live Case Application in Data Science Discussion on Solutioning possibilities for a Business Problem Evolving a simple Data Science solution through a Practical Exercise 15 Showcase of Rattle Program in R Solving a pre-existing problem like MTCars. csv or Weather.csv Executing Supervised and Unsupervised methodologies on the dataset.

8 FACILITATOR Dr. Kamaljit Anand, Education PhD in Management, Indian Institute of Management, Ahmedabad PGDM Coursework, Indian Institute of Management, Ahmedabad Masters in Science, University of Delhi, University 3rd Rank (M.Phil/PhD Admission to St. Edmunds College, Cambridge University, UK in 1998 with Cambridge Commonwealth Trust Award Initial Shortlist) Experience Bracket Over 18 years of Research, Teaching & Consulting Experience across Public and Private institutions driving large teams of professionals for Technical excellence in dynamic multi-cultural and multi ethnic environments Worked on over 25 long term and 70 short term projects across BFSI, Government and Retail verticals covering areas of Economics, Risk and Business Analytics Conducted over 50 trainings in Data Analytics for Mid-Level/ Senior Executives and Academicians encompassing Intermediate and Advanced Programs on Statistical Data Analysis, Business Analytics, Applied Data Science, Market Research, Marketing Management and Business Strategy Responsible for development of Consumer Behavior Frameworks and application of fundamentals as well as advanced concepts of Marketing Science & Economics areas in Business Decision Making Officiated as Academic Director for over 4 years at a large Educational group academically supported by IIM Ahmedabad. Also Chaired Centre for Data Science, IMS.

9 Our clients say Dr Kamal is very knowledgable and helpful. Knowledge The tutor is experienced and the lecturing is easy to understand Everything was very good. Materials on data analysis are useful. Good knowledge of the trainer The tutor is experienced and the lecturing is easy to understand

10 Timmins Training Consulting Contact Us Sdn. Bhd. shan.t@consult-timmins.com Telephone: Mobile : Suite A27-07, Mercu Summer Suites, 8 Jalan Cendana, Kuala Lumpur PT Timmins Konsultan Utama nadiah.s@consult-timmins.com Telephone: Mobile : IDX Building, Tower 2, 17th Floor, jalan Jend. Sudirman Kav Jakarta 12190, Indonesia. Website: consult-timmins.com