k2 analytics machine learning course
TRANSCRIPT
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MACHINE LEARNING COURSE
CURRICULUM(70 Hrs Online + 45 Hrs Videos)
K 2 A N A LY T I C S | B U I L D I N G S K I L L S , B U I L D I N G I N D I V I D U A L S
W e b s i t e : h t t p s : / / k 2 a n a l t i c s . c o . i n
M o b i l e : 8 9 3 9 6 9 4 8 7 4 | E m a i l : i n f o @ k 2 a n a l y t i c s . c o . i n
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We provide Training, Placement and
Consulting Services in the field of Data
Science / Machine Learning
Our Website
https://k2analytics.co.in
Our Mission
“To provide training and skill development courses to
individuals, make them skilled & industry ready, and
create a pool of skilled resources readily available for
the industry”
About K2 Analytics
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Our Offerings
Instructor Led Online Training
Access to Recorded Video Content
Consult with our faculty for your projects
Hands-on Project Experience
Hands-on Experiential Training
Capstone Projects
Placement Assistance
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Machine Learning Course Curriculum
Instructor-led Online Session
Sr. No Topic Hrs.
Tool Training
1 Python Programming 14
Numerical Skills
2 Statistics 24
Machine Learning Techniques
3 Clustering 6
4 Linear Regression 8
5 Classification Tree 8
6 Logistic Regression 10
Total Hours 70
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Complimentary Video Access
Complimentary Six Month Access to Video Recordings
Sr. No Topic Hrs.
1 R Programming 10
2 K Nearest Neighbour 1
3 Naïve Bayes 1
4 Linear Regression in R 3
5 Logistic Regression in R 12
2 Bagging (Random Forest) in R & Python 5
3 Classification Tree in R 5
4Artificial Neural Network in R & Python (Tensorflow & Keras)
8
Total Hours 45
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P Y T H O N P R O G R A M M I N G
COURSE DETAILS
• Introduction to Python and Anaconda
• Spyder and Jupyter Notebook
• Understanding Python Data Structures
• List, Tuple, Dictionary, Sets
• Mutable and immutable Objects
• Numpy and Pandas Packages in Python
• 1D, 2D, 3D Array
• Series and Dataframe
• Data Import – Export using PANDAS
• Data Manipulation
• Selecting Rows / Observations
• Selecting Columns / Fields
• Merging Data
• Relabeling the Column Names
• Converting Variable Types
• Data Sorting
• Data Aggregation
• Matplotlib and Seaborn packages
• Charts & Graphs
COURSE DURATION• 14 Hours
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S TAT I S T I C S
COURSE DETAILS
• Introduction to Statistics for Data Science
• Types of Variables
• Descriptive Statistics – Numerical Methods
• Measures of Central Tendency
• Mean, Median, Mode
• Measures of Dispersion
• Range, Interquartile Range, Standard Deviation, Variance
• Descriptive Statistics – Tabular & Graphical Methods
• Histogram, Line Plot, Bar Plot, Pie Chart
• Box Plot, Scatter Plot
• Frequency Table, Crosstab
• Probability Concepts
• Distributions
• Normal Distribution
• Binomial Distribution
• Central Limit Theorem
• Hypothesis Testing
COURSE DURATION• 24 Hours
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U N - S U P E R V I S E D M A C H I N E L E A R N I N G
CLUST ERING
COURSE DETAILS• Clustering
• Why Clustering?
• What is Clustering?
• Measure of Similarity
• Distance Measures
• Hierarchical Clustering
• K Means Clustering
• Finding Optimal No. of Clusters
PROJECTS• Clustering of Retail Customers
BLOG LINKS: Hierarchical ClusteringK Means Clustering
COURSE DURATION• 6 Hours
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S U P E R V I S E D M A C H I N E L E A R N I N G
L INEAR REGRESSION
COURSE DETAILS• Introduction to Linear Regression
• Assumptions of Linear Regression
• Simple Linear Regression
• Multiple Linear Regression
• Line of Best Fit
• Residual Error, SSE
• R-Squared & Adj, R-Squared
• Correlation & Multi-Collinearity
• Variance Inflation Factor
• Homoscedasticity & Heteroscedasticity
• Variable Transformation and its Importance
PROJECTS• Build Linear Regression Model to Estimate Monthly Household
Expense
BLOG LINK Linear Regression blog series
COURSE DURATION• 8 Hours
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S U P E R V I S E D M A C H I N E L E A R N I N G
CLASSIF ICAT ION T REE
COURSE DETAILS• Introduction to Classification Tree
• CHAID, CART, C4.5
• Greedy Algorithm
• Balanced & Unbalanced Data
• CART – Gini Gain Calculation
• Binary / Multi-way Split
• Pruning
• Cross-Validation
• Overfitting
• Model Development & Evaluation
• Pros & Cons of Classification Tree Technique
PROJECTS• Case-Study – Dormant Account Win-back Model
• Classification Tree Model Development on Balanced Dataset
BLOG LINKRetail Banking Case-Study
COURSE DURATION• 8 Hours
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S U P E R V I S E D M A C H I N E L E A R N I N G
LOGIST IC REGRESSION
COURSE DETAILS• Introduction to Logistic Regression
• Log Odds Concept and Logistic Function
• Development, Validation and Hold-out
• Hypothesis Testing
• Outlier Treatment & Missing Value Imputation
• Information Value
• Pattern Detection and Visualization
• Variable Transformation
• Weight of Evidence
• Multi-Collinearity & Variance Inflation Factor (VIF)
• Model Development & Validation
• Model Performance Measurement
• KS, Rank Order, Lift Chart, AUC-ROC, Gini,
Concordance, Hosmer-Lemeshow Goodness of Fit Test
PROJECTS• Personal Loans Cross-Sell Model using Logistic Regression
Technique
BLOG LINK Logistic Regression blog series
COURSE DURATION• 10 Hours
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K2 Analytics Finishing School Private LimitedB / 18, Mirani Nagar, Kopri Colony, Thane East - 400603
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