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Machine Learning & Data Science

Machine Learning & Data Science

Curriculum

20 Sections
295 Lessons
1 Week

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Introduction
2
- 1.0
  1.1 Course Outline
- 1.1
  1.2 Your First Day
Machine Learning 101
8
- 2.0
  2.1 What Is Machine Learning
- 2.1
  2.2 AI Machine Learning Data Science
- 2.2
  2.3 Exercise Machine Learning Playground
- 2.3
  2.4 How Did We Get Here
- 2.4
  2.5 Exercise YouTube Recommendation Engine
- 2.5
  2.6 Types of Machine Learning
- 2.6
  2.7 What Is Machine Learning Round 2
- 2.7
  2.8 Section Review
3. Machine Learning and Data Science Framework
13
- 3.0
  3.1 Section Overview
- 3.1
  3.2 Introducing Our Framework
- 3.2
  3.3 6 Step Machine Learning Framework
- 3.3
  3.4 Types of Machine Learning Problems_1
- 3.4
  3.5 Types of Data
- 3.5
  3.6 Types of Evaluation
- 3.6
  3.7 Features In Data
- 3.7
  3.8 Modelling – Splitting Data
- 3.8
  3.9 Modelling – Picking the Model
- 3.9
  3.10 Modelling – Tuning
- 3.10
  3.11 Modelling – Comparison
- 3.11
  3.13 Experimentation
- 3.12
  3.14 Tools We Will Use
4. The 2 Paths
1
- 4.0
  4.1 The 2 Paths
5. Data Science Environment Setup
11
- 5.0
  5.1 Section Overview
- 5.1
  5.2 Introducing Our Tools
- 5.2
  5.3 What is Conda
- 5.3
  5.4 Conda Environments
- 5.4
  5.5 Mac Environment Setup
- 5.5
  5.6 Mac Environment Setup 2
- 5.6
  5.7 Windows Environment Setup
- 5.7
  5.8 Windows Environment Setup 2
- 5.8
  5.9 Jupyter Notebook Walkthrough
- 5.9
  5.10 Jupyter Notebook Walkthrough 2
- 5.10
  5.11 Jupyter Notebook Walkthrough 3
6. Pandas Data Analysis
10
- 6.0
  6.1 Section Overview
- 6.1
  6.2 Pandas Introduction
- 6.2
  6.3 Series, Data Frames and CSVs
- 6.3
  6.4 Describing Data with Pandas
- 6.4
  6.5 Selecting and Viewing Data with Pandas
- 6.5
  6.6 Selecting and Viewing Data with Pandas Part 2
- 6.6
  6.7 Manipulating Data
- 6.7
  6.8 Manipulating Data 2
- 6.8
  6.9 Manipulating Data 3
- 6.9
  6.10 How To Download The Course Assignments
7. NumPy
15
- 7.0
  7.1 Section Overview
- 7.1
  7.2 NumPy Introduction
- 7.2
  7.3 NumPy DataTypes and Attributes
- 7.3
  7.4 Creating NumPy Arrays
- 7.4
  7.5 NumPy Random Seed
- 7.5
  7.6 Viewing Arrays and Matrices
- 7.6
  7.7 Manipulating Arrays
- 7.7
  7.8 Manipulating Arrays 2
- 7.8
  7.9 Standard Deviation and Variance
- 7.9
  7.10 Reshape and Transpose
- 7.10
  7.11 Dot Product vs Element Wise
- 7.11
  7.12 Exercise Nut Butter Store Sales
- 7.12
  7.13 Comparison Operators
- 7.13
  7.14 Sorting Arrays
- 7.14
  7.15 Turn Images Into NumPy Arrays
8. Matplotlib Plotting and Data Visualization
18
- 8.0
  8.1 Section Overview
- 8.1
  8.2 Matplotlib Introduction
- 8.2
  8.3 Importing And Using Matplotlib
- 8.3
  8.4 Anatomy Of A Matplotlib Figure
- 8.4
  8.5 Scatter Plot And Bar Plot
- 8.5
  8.6 Histograms And Subplots
- 8.6
  8.7 Subplots Option 2
- 8.7
  8.8 Quick Tip Data Visualizations
- 8.8
  8.9 Plotting From Pandas DataFrames
- 8.9
  8.10 Plotting From Pandas DataFrames 2
- 8.10
  8.11 Plotting from Pandas DataFrames 3
- 8.11
  8.12 Plotting from Pandas DataFrames 4
- 8.12
  8.13 Plotting from Pandas DataFrames 5
- 8.13
  8.14 Plotting from Pandas DataFrames 6
- 8.14
  8.15 Plotting from Pandas DataFrames 7
- 8.15
  8.16 Customizing Your Plots
- 8.16
  8.17 Customizing Your Plots 2
- 8.17
  8.18 Saving And Sharing Your Plots
9. Scikit-learn Creating Machine Learning Models
34
- 9.0
  9.1 Section Overview
- 9.1
  9.2 Scikit-learn Introduction
- 9.2
  9.3 Refresher What Is Machine Learning
- 9.3
  9.4 Scikit-learn Cheatsheet
- 9.4
  9.10. Getting Your Data Ready Handling Missing Values With Pandas
- 9.5
  9.10.2 Getting Your Data Ready Handling Missing Values With Sci-Kit Learn
- 9.6
  9.12 Choosing The Right Model For Your Data
- 9.7
  9.13 Choosing The Right Model For Your Data 2 (Regression)
- 9.8
  9.14 Quick Tip How ML Algorithms Work
- 9.9
  9.15 Choosing The Right Model For Your Data 3 (Classification)
- 9.10
  9.16 Fitting A Model To The Data
- 9.11
  9.17 Making Predictions With Our Model
- 9.12
  9.18 predict() vs predict_proba()
- 9.13
  9.19 Making Predictions With Our Model (Regression)
- 9.14
  9.20 Evaluating A Machine Learning Model (Score)
- 9.15
  9.21 Evaluating A Machine Learning Model 2 (Cross Validation)
- 9.16
  9.22 Evaluating A Classification Model 1 (Accuracy)
- 9.17
  9.23 Evaluating A Classification Model 2 (ROC Curve)
- 9.18
  9.24 Evaluating A Classification Model 3 (ROC Curve)
- 9.19
  9.25 Evaluating A Classification Model 4 (Confusion Matrix)
- 9.20
  9.26 Evaluating A Classification Model 5 (Confusion Matrix)
- 9.21
  9.27 Evaluating A Classification Model 6 (Classification Report)
- 9.22
  9.28 Evaluating A Regression Model 1 (R2 Score)
- 9.23
  9.29 Evaluating A Regression Model 2 (MAE)
- 9.24
  9.30 Evaluating A Regression Model 3 (MSE)
- 9.25
  9.31 Evaluating A Model With Cross Validation and Scoring Parameter
- 9.26
  9.32 Evaluating A Model With Scikit-learn Functions
- 9.27
  9.33 Improving A Machine Learning Model
- 9.28
  9.34 Tuning Hyperparameters
- 9.29
  9.35 Tuning Hyperparameters 2
- 9.30
  9.36 Tuning Hyperparameters 3
- 9.31
  9.37 Quick Tip Correlation Analysis
- 9.32
  9.38 Saving And Loading A Model
- 9.33
  9.39 Saving And Loading A Model 2
11. Milestone Project 1 Supervise
19
- 10.1
  11.1 Section Overview
- 10.2
  11.2 Project Overview
- 10.3
  11.3 Project Environment Setup
- 10.4
  11.4 Step 1~4 Framework Setup
- 10.5
  11.5 Getting Our Tools Ready
- 10.6
  11.6 Exploring Our Data
- 10.7
  11.7 Finding Patterns
- 10.8
  11.8 Finding Patterns 2
- 10.9
  11.9 Finding Patterns 3
- 10.10
  11.10 Preparing Our Data For Machine Learning
- 10.11
  11.11 Choosing The Right Models
- 10.12
  11.12 Experimenting With Machine Learning Models
- 10.13
  11.13 Tuning Improving Our Model
- 10.14
  11.16 Tuning Hyperparameters 3
- 10.15
  11.17 Evaluating Our Model
- 10.16
  11.18 Evaluating Our Model 2
- 10.17
  11.19 Evaluating Our Model 3
- 10.18
  11.20 Finding The Most Important Features
- 10.19
  11.21 Reviewing The Project
12. Milestone Project 2 Supervised Learning (Time Series Data)
19
- 11.0
  12.1 Section Overview
- 11.1
  12.2 Project Overview
- 11.2
  12.3 Project Environment Setup
- 11.3
  12.4 Step 1~4 Framework Setup
- 11.4
  12.5 Exploring Our Data
- 11.5
  12.6 Exploring Our Data 2
- 11.6
  12.10 Filling Missing Categorical Values
- 11.7
  12.11 Fitting A Machine Learning Model
- 11.8
  12.12 Splitting Data
- 11.9
  12.15 RandomizedSearchCV
- 11.10
  12.16 Improving Hyperparameters
- 11.11
  12.18 Making Predictions
- 11.12
  12.8 Turning Data Into Numbers
- 11.13
  12.9 Filling Missing Numerical Values
- 11.14
  12.13 Custom Evaluation Function
- 11.15
  12.14 Reducing Data
- 11.16
  12.17 Preproccessing Our Data
- 11.17
  12.19 Feature Importance
- 11.18
  12.7 Feature Engineering
13. Data Engineering
11
- 12.0
  13.1 Data Engineering Introduction
- 12.1
  13.2. What Is Data
- 12.2
  13.3 What Is A Data Engineer
- 12.3
  13.4 What Is A Data Engineer 2
- 12.4
  13.5 What Is A Data Engineer 3
- 12.5
  13.6 What Is A Data Engineer 4
- 12.6
  13.7 Types Of Databases
- 12.7
  13.9 Optional OLTP Databases
- 12.8
  13.10 Hadoop, HDFS and MapReduce
- 12.9
  13.11 Apache Spark and Apache Flink
- 12.10
  13.12 Kafka and Stream Processing
14. Neural Networks Deep Learning, Transfer Learning and Tensor Flow 2
41
- 13.0
  14.1 Section Overview
- 13.1
  14.2 Deep Learning and Unstructured Data
- 13.2
  14.3 Setting Up Google Colab
- 13.3
  14.4 Google Colab Workspace
- 13.4
  14.5 Uploading Project Data
- 13.5
  14.6 Setting Up Our Data
- 13.6
  14.7 Setting Up Our Data 2
- 13.7
  14.8 Importing TensorFlow 2
- 13.8
  14.9 Optional TensorFlow 2.0 Default Issue
- 13.9
  14.10 Optional GPU and Google Colab
- 13.10
  14.11 Optional Reloading Colab Notebook
- 13.11
  14.12 Loading Our Data Labels
- 13.12
  14.13 Preparing The Images
- 13.13
  14.14 Turning Data Labels Into Numbers
- 13.14
  14.15 Creating Our Own Validation Set
- 13.15
  14.16 Preprocess Images
- 13.16
  14.17 Preprocess Images 2
- 13.17
  14.18 Turning Data Into Batches
- 13.18
  14.19 Turning Data Into Batches 2
- 13.19
  14.20 Using A GPU
- 13.20
  14.21 Preparing Our Inputs and Outputs
- 13.21
  14.22 Visualizing Our Data
- 13.22
  14.23 Building A Deep Learning Model
- 13.23
  14.24 Building A Deep Learning Model 2
- 13.24
  14.25 Building A Deep Learning Model 3
- 13.25
  14.26 Building A Deep Learning Model 4
- 13.26
  14.27 Summarizing Our Model
- 13.27
  14.28 Evaluating Our Model
- 13.28
  14.29 Preventing Overfitting
- 13.29
  14.30 Training Your Deep Neural Network
- 13.30
  14.31 Evaluating Performance With TensorBoard
- 13.31
  14.32 Make And Transform Predictions
- 13.32
  14.33 Transform Predictions To Text
- 13.33
  14.34 Visualizing Model Predictions
- 13.34
  14.35 Visualizing And Evaluate Model Predictions 2
- 13.35
  14.36 Visualizing And Evaluate Model Predictions 3
- 13.36
  14.37 Saving And Loading A Trained Model
- 13.37
  14.38 Training Model On Full Dataset
- 13.38
  14.39 Making Predictions On Test Images
- 13.39
  14.40 Submitting Model to Kaggle
- 13.40
  14.41 Making Predictions On Our Images
15. Storytelling + Communication How To Present Your Work
7
- 14.0
  15.1 Section Overview
- 14.1
  15.2 Communicating Your Work
- 14.2
  15.3 Communicating With Managers
- 14.3
  15.4 Communicating With Co-Workers
- 14.4
  15.5 Weekend Project Principle
- 14.5
  15.6 Communicating With Outside World
- 14.6
  15.7 Storytelling
16. Career Advice + Extra Bits
7
- 15.0
  16.1 What If I Don’t Have Enough Experience
- 15.1
  16.2 JTS Learn to Learn
- 15.2
  16.3 JTS Start With Why
- 15.3
  16.4 CWD Git + Github
- 15.4
  16.5 CWD Git + Github 2
- 15.5
  16.6 Contributing To Open Source
- 15.6
  16.7 Contributing To Open Source 2
17. Learn Python
37
- 16.0
  17.4 Our First Python Program
- 16.1
  17.6 Exercise How Does Python Work
- 16.2
  17.7 Learning Python
- 16.3
  17.10 Math Functions
- 16.4
  17.12 Operator Precedence
- 16.5
  17.13 Optional bin() and complex
- 16.6
  17.15 Expressions vs Statements
- 16.7
  17.16 Augmented Assignment Operator
- 16.8
  17.17 Strings
- 16.9
  17.19 Type Conversion
- 16.10
  17.20 Escape Sequences
- 16.11
  17.20 String Concatenation
- 16.12
  17.21 Formatted Strings
- 16.13
  17.22 String Indexes
- 16.14
  17.23 Immutability
- 16.15
  17.25 Booleans
- 16.16
  17.26 Exercise Type Conversion
- 16.17
  17.27 DEVELOPER FUNDAMENTALS II
- 16.18
  17.28 Exercise Password Checker
- 16.19
  17.29 Lists
- 16.20
  17.30 List Slicing
- 16.21
  17.31 Matrix
- 16.22
  17.32 List Methods
- 16.23
  17.33 List Methods 2
- 16.24
  17.34 List Methods 3
- 16.25
  17.35 Common List Patterns
- 16.26
  17.36 List Unpacking
- 16.27
  17.37 None
- 16.28
  17.38 Dictionaries
- 16.29
  17.39 DEVELOPER FUNDAMENTALS III
- 16.30
  17.40. Dictionary Keys
- 16.31
  17.41 Dictionary Methods
- 16.32
  17.42 Dictionary Methods 2
- 16.33
  17.43 Tuples
- 16.34
  17.44 Tuples 2
- 16.35
  17.45 Sets
- 16.36
  17.46 Sets 2
18. Learn Python Part 2
42
- 17.0
  18.1 Breaking The Flow
- 17.1
  18.2 Conditional Logic
- 17.2
  18.3 Indentation In Python
- 17.3
  18.4 Truthy vs Falsey
- 17.4
  18.5 Ternary Operator
- 17.5
  18.10 For Loops
- 17.6
  18.11 Iterables
- 17.7
  18.12 Exercise Tricky Counter
- 17.8
  18.13 range()
- 17.9
  18.14 enumerate()
- 17.10
  18.15 While Loops
- 17.11
  18.16 While Loops 2
- 17.12
  18.17 break, continue, pass
- 17.13
  18.18 Our First GUI
- 17.14
  18.19 DEVELOPER FUNDAMENTALS IV
- 17.15
  18.20 Exercise Find Duplicates
- 17.16
  18.21 Functions
- 17.17
  18.22 Parameters and Arguments
- 17.18
  18.23 Default Parameters and Keyword Arguments
- 17.19
  18.24 return
- 17.20
  18.25 Methods vs Functions
- 17.21
  18.26 Docstrings
- 17.22
  18.27 Clean Code
- 17.23
  18.28 args and kwargs
- 17.24
  18.29 Exercise Functions
- 17.25
  18.30 Scope
- 17.26
  18.31 Scope Rules
- 17.27
  18.32 global Keywordmp4
- 17.28
  18.33 nonlocal Keyword
- 17.29
  18.34 Why Do We Need Scope
- 17.30
  18.35 Pure Functions
- 17.31
  18.36 map()
- 17.32
  18.37 filter()
- 17.33
  18.38 zip()
- 17.34
  18.39 reduce()
- 17.35
  18.40 List Comprehensions
- 17.36
  18.41 Set Comprehensions
- 17.37
  18.42 Exercise Comprehensions
- 17.38
  18.43 Modules in Python
- 17.39
  18.44 Optional PyCharm
- 17.40
  18.45 Packages in Python
- 17.41
  18.46 Different Ways To Import
19. Bonus Learn Advanced Statistics and Mathematics for FREE!
0
20. Where To Go From Here
0
21. Extras
0

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1.2 Your First Day

About

Leadzin Delivers Best-In-Class Specially Customised Business Packages And Educational Programs To Help You In Every Stage of Your Journey to Financial Freedom.

GSTIN: 27AAFCL1245C1ZX

Contact

Salunkhe Vihar, Pune,
Maharashtra, India – 411048

contact@leadzin.in
@hussainkhalil_
@leadzin_official

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About

Leadzin Delivers Best-In-Class Specially Customised Business Packages And Educational Programs To Help You In Every Stage of Your Journey to Financial Freedom.

GSTIN: 27AAFCL1245C1ZX

Contact

Mannat Heights, Lane no. 7,
Salunkhe Vihar, Pune,
Maharashtra, India – 411048

Contact@leadzin.in
@HussainKhalil_
@leadzin_official

Home
About
Service
Contact

Terms & Conditions
Privacy Policy
Refund Policy
Partner Policy
Legal Documents

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