Machine learning Training with Data Science Expert
👉 35 Hours of Instructor-Led Google DFP Training
 👉 Real World use cases and ScenariosÂ
 👉 Hands on Practical ExperienceÂ
Machine learning is the branch of Artificial Intelligence where computer learns the rules of solving complex problems without explicitly programmed. It enables the development of computer programs that can access data and use it for learn themselves.
Machine learning let computers learns automatically without human intervention based on the observations or data, past experiences to look patterns in the data and make decision in the future.
Will CoverÂ
- Data Science (Pandas, NumPy, SciPy, Scikit-learn, matplotlib)
- Exploratory Data Analysis, Statistics
- Machine learning Algorithms (Supervised & Unsupervised), Model Tuning, Recommendation Systems
- Hands on Projects and Assignments
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Machine Learning Training Course Overview
What you’ll learn: By end of the training you will have the deep understanding/knowledge of how Machine learning algorithm works, how to optimize it and how you can apply these algorithms with real world data with practical applications. You will be solving multiple case studies as well as assignments to have more hands-on experience.
You will learn mathematical and heuristic aspects for Machine Learning algorithms.
Introduction to Machine Learning
How  What is Machine learning?
Machine learning applications
Kinds of Machine learning problems
Tools for Machine learning
When to apply Machine learning.
Data Handling using numpy and pandas
Numpy arrays
Pandas and Data Frame
Data import and export
Data transformations
Practice Assignment
Exploratory Data Analysis
Data visualization using matplotlib
Data insights
Probability and Central tendency
Summary Statistics
Data distribution
Handling missing values
Correlation analysis
Outlier detection
Practice Assignment
Generalized Liner Model
Linear Regression Algorithm
Feature engineering
Cost function
Gradient Descent
Model building process
Overfitting and Under fitting
Bias variance tradeoff
Model evaluation metrics(MSE,RMSE)
Logistic Regression Algorithm
Confusion matrix
ROC plot
Case study using Linear Regression & Logistic Regression
Unsupervised Learning
K-Means Clustering Algorithm
K-Mediods Clustering Algorithm
Determination of right K
Hierarchical clustering Technique
Case Study using Clustering Algorithm
Time Series Analysis
Understanding trend, seasonality and randomness in time series data
Stationary
ACF and PACF
Time Series forecasting using ARIMA
Case study using Time Series analysis
Decision Tree Algorithm
Entropy
Information gain, Gini index
Building Decision Tree
Case study using Decision Tree
Support Vector Machine Algorithm
Geometric intuition
Mathematical derivation
Kernel trick
Cost complexity
Case study using SVM
K-Nearest Neighbors Algorithm
Distance measures: Manhattan, Euclidean, Hamming
Cosine distance and cosine similarity
Decision surface
Overfitting and Underfitting
Classification and Regression
Limitation of KNN
Case study using KNN
Ensemble model
Weak learner and Strong learner
Bagging techniques
Boosting techniques
Case study using ensemble model
Random Forest Algorithm
Geometric intuition
Feature selection
Feature Importance
Case study using Random Forest Algorithm
Building Recommendation Engine
Collaborative filtering model
Matrix factorization
Popularity Model
Implement Recommendation Engine
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