4.99 GB | 00:14:43 | mp4 | 1920X1080  | 16:9
Genre:eLearning |Language:English

Files Included :
1 - Installing Anaconda Distribution for Windows  (158.54 MB)
10 - Creating NumPy Array with Ones Function  (13.54 MB)
11 - Creating NumPy Array with Full Function  (8 MB)
12 - Creating NumPy Array with Arange Function  (8.73 MB)
13 - Creating NumPy Array with Eye Function  (9.3 MB)
14 - Creating NumPy Array with Linspace Function  (4.76 MB)
15 - Creating NumPy Array with Random Function  (39.9 MB)
16 - Properties of NumPy Array  (19.13 MB)
17 - Identifying the Largest Element of a Numpy Array  (13.21 MB)
18 - Detecting Least Element of Numpy Array Min Ar  (7.5 MB)
19 - Reshaping a NumPy Array Reshape Function  (20.31 MB)
20 - Concatenating Numpy Arrays Concatenate Functio  (28.79 MB)
21 - Splitting OneDimensional Numpy Arrays The Split  (15.79 MB)
22 - Splitting TwoDimensional Numpy Arrays Split  (22.56 MB)
23 - Sorting Numpy Arrays Sort Function  (12.54 MB)
24 - Indexing Numpy Arrays  (19.54 MB)
25 - Slicing OneDimensional Numpy Arrays  (16.29 MB)
26 - Slicing TwoDimensional Numpy Arrays  (25.57 MB)
27 - Assigning Value to OneDimensional Arrays  (13.57 MB)
28 - Assigning Value to TwoDimensional Array  (26.4 MB)
29 - Fancy Indexing of OneDimensional Arrrays  (13.19 MB)
3 - Installing Anaconda Distribution for MacOs  (71.5 MB)
30 - Fancy Indexing of TwoDimensional Arrrays  (29.6 MB)
31 - Combining Fancy Index with Normal Indexing  (9.43 MB)
32 - Combining Fancy Index with Normal Slicing  (12.05 MB)
33 - Operations with Comparison Operators  (16.24 MB)
34 - Arithmetic Operations in Numpy  (83.18 MB)
35 - Statistical Operations in Numpy  (36.61 MB)
36 - Solving SecondDegree Equations with NumPy  (18.32 MB)
5 - Installing Anaconda Distribution for Linux  (178.11 MB)
6 - Introduction to NumPy Library  (54.55 MB)
7 - The Power of NumPy  (48.2 MB)
8 - Creating NumPy Array with The Array Function  (23.56 MB)
9 - Creating NumPy Array with Zeros Function  (21.28 MB)
109 - K Nearest Neighbors Algorithm Theory  (17.44 MB)
110 - K Nearest Neighbors Algorithm with Python Part 1  (19.8 MB)
111 - K Nearest Neighbors Algorithm with Python Part 2  (41.55 MB)
112 - K Nearest Neighbors Algorithm with Python Part 3  (19.67 MB)
113 - Hyperparameter Optimization Theory  (34.74 MB)
114 - Hyperparameter Optimization with Python  (34.49 MB)
115 - Decision Tree Algorithm Theory  (24.77 MB)
116 - Decision Tree Algorithm with Python Part 1  (22.6 MB)
117 - Decision Tree Algorithm with Python Part 2  (32.26 MB)
118 - Decision Tree Algorithm with Python Part 3  (8.98 MB)
119 - Decision Tree Algorithm with Python Part 4  (33.63 MB)
120 - Decision Tree Algorithm with Python Part 5  (25.41 MB)
121 - Random Forest Algorithm Theory  (18.01 MB)
122 - Random Forest Algorithm with Pyhon Part 1  (28.54 MB)
123 - Random Forest Algorithm with Pyhon Part 2  (27.32 MB)
124 - Support Vector Machine Algorithm Theory  (14.96 MB)
125 - Support Vector Machine Algorithm with Python Part 1  (48.01 MB)
126 - Support Vector Machine Algorithm with Python Part 2  (33.12 MB)
127 - Support Vector Machine Algorithm with Python Part 3  (28.56 MB)
128 - Support Vector Machine Algorithm with Python Part 4  (23.14 MB)
129 - Unsupervised Learning Overview  (12.05 MB)
130 - K Means Clustering Algorithm Theory  (11.34 MB)
131 - K Means Clustering Algorithm with Python Part 1  (18.81 MB)
132 - K Means Clustering Algorithm with Python Part 2  (21.58 MB)
133 - K Means Clustering Algorithm with Python Part 3  (22.89 MB)
134 - K Means Clustering Algorithm with Python Part 4  (20.64 MB)
135 - Hierarchical Clustering Algorithm Theory  (24.06 MB)
136 - Hierarchical Clustering Algorithm with Python Part 1  (20.98 MB)
137 - Hierarchical Clustering Algorithm with Python Part 2  (20.97 MB)
138 - Principal Component Analysis PCA Theory  (29.43 MB)
139 - Principal Component Analysis PCA with Python Part 1  (15.06 MB)
140 - Principal Component Analysis PCA with Python Part 2  (5.09 MB)
141 - Principal Component Analysis PCA with Python Part 3  (22.08 MB)
142 - What is the Recommender System Part 1  (14.66 MB)
143 - What is the Recommender System Part 2  (12.35 MB)
38 - Introduction to Pandas Library  (23.37 MB)
39 - Creating a Pandas Series with a List  (44.13 MB)
40 - Creating a Pandas Series with a Dictionary  (14.42 MB)
41 - Creating Pandas Series with NumPy Array  (9.06 MB)
42 - Object Types in Series  (15.43 MB)
43 - Examining the Primary Features of the Pandas Seri  (12.31 MB)
44 - Most Applied Methods on Pandas Series  (39.27 MB)
45 - Indexing and Slicing Pandas Series  (23.33 MB)
46 - Creating Pandas DataFrame with List  (16.95 MB)
47 - Creating Pandas DataFrame with NumPy Array  (8.93 MB)
48 - Creating Pandas DataFrame with Dictionary  (11.6 MB)
49 - Examining the Properties of Pandas DataFrames  (19.02 MB)
50 - Element Selection Operations in Pandas DataFrames Lesson 1  (22.11 MB)
51 - Element Selection Operations in Pandas DataFrames Lesson 2  (22.34 MB)
52 - Top Level Element Selection in Pandas DataFramesLesson 1  (27.7 MB)
53 - Top Level Element Selection in Pandas DataFramesLesson 2  (22.39 MB)
54 - Top Level Element Selection in Pandas DataFramesLesson 3  (16.37 MB)
55 - Element Selection with Conditional Operations in Pandas Data Frames  (34.22 MB)
56 - Adding Columns to Pandas Data Frames  (24.68 MB)
57 - Removing Rows and Columns from Pandas Data frames  (9.52 MB)
58 - Null Values in Pandas Dataframes  (78.39 MB)
59 - Dropping Null Values Dropna Function  (24.63 MB)
60 - Filling Null Values Fillna Function  (38.38 MB)
61 - Setting Index in Pandas DataFrames  (34.46 MB)
62 - MultiIndex and Index Hierarchy in Pandas DataFrames  (27.18 MB)
63 - Element Selection in MultiIndexed DataFrames  (21.2 MB)
64 - Selecting Elements Using the xs Function in MultiIndexed DataFrames  (26.92 MB)
65 - Concatenating Pandas Dataframes Concat Function  (54.12 MB)
66 - Merge Pandas Dataframes Merge Function Lesson 1  (43.15 MB)
67 - Merge Pandas Dataframes Merge Function Lesson 2  (26.03 MB)
68 - Merge Pandas Dataframes Merge Function Lesson 3  (71.27 MB)
69 - Merge Pandas Dataframes Merge Function Lesson 4  (36.04 MB)
70 - Joining Pandas Dataframes Join Function  (48.19 MB)
71 - Loading a Dataset from the Seaborn Library  (31.36 MB)
72 - Examining the Data Set 1  (31.51 MB)
73 - Aggregation Functions in Pandas DataFrames  (110.64 MB)
74 - Examining the Data Set 2  (37.24 MB)
75 - Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes  (77.05 MB)
76 - Advanced Aggregation Functions Aggregate Function  (23.4 MB)
77 - Advanced Aggregation Functions Filter Function  (19.47 MB)
78 - Advanced Aggregation Functions Transform Function  (36.81 MB)
79 - Advanced Aggregation Functions Apply Function  (49.23 MB)
80 - Examining the Data Set 3  (29.71 MB)
81 - Pivot Tables in Pandas Library  (65.95 MB)
82 - Accessing and Making Files Available  (41.53 MB)
83 - Data Entry with Csv and Txt Files  (38.85 MB)
84 - Data Entry with Excel Files  (17.79 MB)
85 - Outputting as an CSV Extension  (22.36 MB)
86 - Outputting as an Excel File  (17.35 MB)
144 - AI Machine Learning and Deep Learning  (9.87 MB)
145 - History of Machine Learning  (13.63 MB)
146 - Turing Machine and Turing Test  (23.61 MB)
147 - What is Deep Learning  (12.67 MB)
148 - Learning Representations From Data  (23.14 MB)
149 - Workflow of Machine Learning  (18.86 MB)
150 - Machine Learning Methods  (30.94 MB)
151 - Supervised Machine Learning Methods 1  (20.06 MB)
152 - Supervised Machine Learning Methods 2  (37.03 MB)
153 - Supervised Machine Learning Methods 3  (35.24 MB)
154 - Supervised Machine Learning Methods 4  (79.33 MB)
155 - Gathering data  (10.61 MB)
156 - Data preprocessing  (15.38 MB)
157 - Choosing the right algorithm and model  (43.51 MB)
158 - Training and testing the model  (25.77 MB)
159 - Evaluation  (14.42 MB)
160 - What Is ANN  (14.83 MB)
161 - Anatomy of Neural Network  (26.73 MB)
162 - Optimizers in Ai  (26.13 MB)
163 - What is TensorFlow  (38.14 MB)
164 - What is CNN  (56.48 MB)
165 - Understanding RNN and LSTM Networks  (31.05 MB)
166 - What is Transfer Learning  (46.14 MB)
167 - What Is Data Science  (12.97 MB)
168 - Data literacy in Data Science  (6.61 MB)
169 - What is Numpy  (15.87 MB)
170 - Why Numpy  (8.15 MB)
87 - What is Machine Learning  (20.3 MB)
88 - Machine Learning Terminology  (8.92 MB)
90 - Classification vs Regression in Machine Learning  (12.5 MB)
91 - Machine Learning Model Performance Evaluation Classification Error Metrics  (106.19 MB)
92 - Evaluating Performance Regression Error Metrics in Python  (29.38 MB)
93 - Machine Learning With Python  (93.47 MB)
94 - What is Supervised Learning in Machine Learning  (26.47 MB)
95 - Linear Regression Algorithm Theory in Machine Learning AZ  (22.23 MB)
96 - Linear Regression Algorithm With Python Part 1  (54.91 MB)
97 - Linear Regression Algorithm With Python Part 2  (78.48 MB)
98 - Linear Regression Algorithm With Python Part 3  (51.76 MB)
99 - Linear Regression Algorithm With Python Part 4  (67.59 MB)
100 - What is Bias Variance TradeOff  (36.27 MB)
101 - What is Logistic Regression Algorithm in Machine Learning  (17.61 MB)
102 - Logistic Regression Algorithm with Python Part 1  (85.28 MB)
103 - Logistic Regression Algorithm with Python Part 2  (60.29 MB)
104 - Logistic Regression Algorithm with Python Part 3  (25.21 MB)
105 - Logistic Regression Algorithm with Python Part 4  (34.62 MB)
106 - Logistic Regression Algorithm with Python Part 5  (23.85 MB)
107 - KFold CrossValidation Theory  (11.55 MB)
108 - KFold CrossValidation with Python  (37.74 MB)
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